AI Agents and Autonomous Business Discovery

3. The Autonomous Business Discovery Process

The development of AI agents introduces the possibility that business discovery may be conducted through a sequence of software-mediated research activities rather than exclusively through human-directed search.

Research into reasoning-and-acting language models demonstrates how systems can combine reasoning processes with actions involving external environments and information sources (Yao et al., 2023).

A user seeking a product, service or commercial provider may delegate part of the research process to an AI system capable of retrieving information, evaluating alternatives and preparing recommendations.

However, discovering an organisation is not equivalent to verifying its suitability, and identifying a potential provider is not equivalent to recommending or selecting that provider.

These distinctions are central to understanding how autonomous business discovery may operate.

CGO Media proposes a structured analytical model that separates the discovery process into nine distinct stages.

The model is intended to support research into agent-mediated commercial discovery, provider selection and organisational visibility.

It is a conceptual framework rather than an empirically validated description of the internal architecture of every AI agent.

3.1 The CGO Media Autonomous Business Discovery Model

The CGO Media Autonomous Business Discovery Model organises agent-mediated provider evaluation into nine analytical stages.

The proposed model draws on established research concerning language-model agents, external information retrieval, tool use and multi-step task evaluation. These areas are examined in research by Yao et al. (2023), Lewis et al. (2020) and Zhou et al. (2024).

However, the particular nine-stage classification presented below is an original CGO Media conceptual contribution. The cited literature provides relevant foundations but does not independently validate this model.

The nine stages are analytically distinct, but actual agent implementations may combine, repeat or reorder them.

An agent may return to earlier stages when information is incomplete or new requirements emerge.

The model should therefore be understood as a research structure rather than a mandatory execution sequence.

Research evaluating autonomous agents in interactive environments demonstrates why multi-step behaviour and task outcomes warrant systematic examination (Zhou et al., 2024); (Liu et al., 2024).

3.2 Stage One — Requirement Interpretation

The discovery process begins with an objective.

A user may request assistance finding a suitable organisation, product, supplier or professional adviser.

However, commercial requirements are frequently incomplete.

A request to identify the best software provider may not specify budget, technical requirements, deployment constraints or integration needs.

Similarly, a request to find a manufacturing supplier may omit required certifications, production capacity or delivery location.

The agent must therefore determine which requirements are explicit, which are ambiguous and which information is missing.

Research on human–AI interaction identifies the importance of communicating system capabilities, managing uncertainty and supporting appropriate human control (Amershi et al., 2019).

3.2.1 Mandatory Requirements

Mandatory requirements are conditions that a candidate must satisfy to remain eligible.

Examples may include:

• Required professional authorisation.
• Specified geographic coverage.
• Necessary technical compatibility.
• Required product functionality.
• Minimum production capacity.
• Relevant regulatory compliance.
• Defined accessibility requirements.

A candidate that does not satisfy a genuinely mandatory requirement may be unsuitable regardless of its other characteristics.

3.2.2 Preferences

Preferences are desirable characteristics that may influence comparative evaluation without necessarily determining eligibility.

These might include preferred pricing, additional functionality, sector experience or particular service arrangements.

The distinction between mandatory requirements and preferences should remain explicit.

Multi-criteria decision analysis provides a relevant methodological foundation for distinguishing requirements, preferences and trade-offs between competing objectives (Keeney and Raiffa, 1976).

3.2.3 Missing Information

An agent may encounter requirements that cannot be interpreted reliably without further clarification.

In such circumstances, requesting additional information may be more appropriate than silently introducing unsupported assumptions.

3.3 Stage Two — Discovery Strategy

Once the objective has been interpreted, the agent may determine how to investigate the available alternatives.

This stage concerns the selection of information sources and research methods.

Potential sources may include public websites, search engines, official registers, technical documentation and structured databases.

An agent with appropriate access may also use authorised APIs or specialist software tools.

Research into tool-using language models and reasoning-and-acting architectures provides relevant technical foundations for understanding how agents may select and interact with external information resources (Schick et al., 2023); (Yao et al., 2023).

The discovery strategy may depend on the type of organisation being investigated.

A legal services provider may require professional register verification.

A manufacturing supplier may require technical documentation and certification evidence.

A SaaS provider may require product documentation, integration information and security disclosures.

Consequently, an identical research strategy may not be appropriate across every industry.

3.3.1 Source Selection

The agent may need to distinguish between different types of sources.

• Official organisational information.
• Independent third-party publications.
• Regulatory and professional registers.
• Product and technical documentation.
• Structured business directories.
• Commercial comparison platforms.
• Customer-generated information.

Each source category has different strengths and limitations.

An official website may provide current product information but may also contain promotional claims.

An independent publication may provide external assessment but may be outdated.

A regulatory register may provide authoritative information about a defined registration status without evaluating broader commercial suitability.

Source selection should therefore reflect the particular claim being investigated.

Research on retrieval-augmented generation establishes a relevant technical foundation for combining external information retrieval with language generation, but it does not establish that all retrieved sources are reliable or independently verified (Lewis et al., 2020).

3.4 Stage Three — Candidate Discovery

Candidate discovery is the process through which potentially relevant organisations enter the agent's research set.

This stage is particularly important for understanding commercial visibility.

An organisation cannot be evaluated within a particular workflow unless it is first identified or introduced through an available information source.

However, candidate discovery does not establish eligibility, quality or commercial preference.

3.4.1 Candidate Sources

Potential candidates may be identified through search results, retrieved documents, structured records, directories or previously available context.

The relative contribution of these sources may vary between agent implementations.

Researchers should avoid assuming that all agents use the same discovery mechanisms.

3.4.2 Candidate Set Completeness

A candidate set may be incomplete.

Relevant organisations may be absent because the agent did not retrieve an appropriate source, the available information was insufficient or the discovery strategy was too narrow.

The absence of an organisation from a candidate set does not necessarily establish that the organisation was considered and rejected.

It may never have been discovered.

3.4.3 Discovery Bias

Candidate discovery may be influenced by source availability, search queries, geographic assumptions, language and other research conditions.

An agent may encounter some organisations more readily than others.

This creates a research question concerning the relationship between information availability and observed candidate inclusion.

Research on AI system evaluation and dataset documentation highlights the importance of examining the conditions under which information is collected and outcomes are measured (Gebru et al., 2021).

3.5 Stage Four — Evidence Retrieval

After identifying potential candidates, the agent may retrieve information relevant to their suitability.

This information may concern organisational identity, products, services, expertise, location, availability, pricing or technical capabilities.

The evidence required depends on the user's original objective.

For example, a provider's geographic coverage may be essential for one task and irrelevant to another.

3.5.1 Evidence Coverage

A credible evaluation should consider whether sufficient information has been retrieved to assess the relevant requirements.

The agent may have extensive information about one candidate and limited information about another.

This difference may affect the comparison.

However, limited retrieved evidence does not automatically establish that a candidate lacks the required capability.

It may indicate an information gap.

3.5.2 Evidence Currency

Commercial information can change.

Prices, product features, operating locations, professional registrations and service availability may be updated.

An agent should not assume that previously retrieved information remains current.

Where information currency is consequential, the evaluation should document the source date or verification status.

3.5.3 Evidence Provenance

Evidence provenance concerns the origin of information.

A claim published by a provider should be distinguishable from a claim verified through an independent source.

Similarly, information generated by another AI system should not automatically be treated as independent corroboration.

The reliability of evidence depends on the nature of the claim and the supporting source.

The World Wide Web Consortium's PROV-O specification provides a relevant technical framework for representing provenance information, including relationships between entities, activities and agents (W3C, 2013).

The application of provenance principles to autonomous business discovery is a proposed research direction; it should not be interpreted as evidence that current commercial AI agents consistently perform source-provenance verification.

3.6 Stage Five — Entity and Evidence Verification

Verification is a separate activity from information retrieval.

An agent may retrieve a statement without establishing its accuracy.

The verification stage attempts to determine whether relevant claims can be supported by appropriate evidence.

3.6.1 Organisational Identity

A provider may operate under a trading name that differs from its registered legal name.

An organisation may also maintain several offices, subsidiaries or historical corporate entities.

A reliable evaluation should avoid combining information from unrelated organisations with similar names.

Entity resolution is therefore an important research consideration.

Knowledge graph research provides a relevant foundation for examining entity identification, relationships and structured representations of organisations (Hogan et al., 2021).

3.6.2 Professional and Regulatory Claims

Some commercial sectors require specific professional qualifications, registrations or authorisations.

Where these requirements are material to provider eligibility, verification may require consultation of appropriate official records.

An organisation's own statement should not necessarily be treated as sufficient evidence of regulatory status.

3.6.3 Technical Claims

Technology and manufacturing providers may publish detailed technical capability statements.

Verification may require documentation, certification records or independent testing.

The appropriate method depends on the nature and significance of the claim.

3.6.4 Conflicting Evidence

Different sources may contain contradictory information.

For example, a directory may list an outdated location while an official website identifies a newer address.

The agent should distinguish conflicting evidence from confirmed factual error.

Where the conflict cannot be resolved, the uncertainty should remain visible.

These classifications are proposed for research purposes.

They should be applied using documented criteria rather than subjective impressions.

The distinction between evidence provenance and factual validity is important: a traceable source may still contain an inaccurate claim. Provenance documentation should therefore complement, rather than replace, substantive verification (W3C, 2013).

3.7 Stage Six — Eligibility Assessment

Eligibility assessment determines whether discovered candidates satisfy mandatory requirements.

This stage differs from comparative preference.

A candidate may offer attractive commercial terms but fail a required technical or regulatory condition.

Such a candidate may be ineligible regardless of its performance on other criteria.

3.7.1 Hard Constraints

Hard constraints are requirements that must be satisfied.

Examples may include:

• Required professional authorisation.
• Mandatory geographic availability.
• Specified technical compatibility.
• Required certification.
• Essential service capability.
• Non-negotiable operational requirements.

3.7.2 Conditional Eligibility

Some requirements may depend on further information.

For example, a supplier may appear capable of meeting a production requirement, but available documentation may not establish its current capacity.

The appropriate classification may be conditionally eligible or insufficiently verified rather than automatically excluded.

3.7.3 False Exclusion

An agent may incorrectly exclude a suitable organisation.

This could occur because of outdated information, misinterpreted requirements or incomplete evidence.

False exclusion is particularly important for commercial visibility.

An organisation may be discovered but never reach the final shortlist.

Researchers should therefore distinguish genuine ineligibility from errors in the evaluation process.

Established research on classification evaluation provides a methodological foundation for distinguishing correct classifications from false exclusions, although the commercial application proposed here requires a task-specific reference standard (Sokolova and Lapalme, 2009).

3.8 Stage Seven — Comparative Evaluation

Comparative evaluation considers the relative suitability of candidates that remain eligible.

The process may involve several criteria.

These can include technical capability, price, experience, delivery arrangements, support and operational risk.

The importance of each criterion depends on the user's objective.

3.8.1 Multi-Criteria Evaluation

A provider may perform strongly on one criterion and less favourably on another.

For example, a software provider may offer extensive functionality but require a more complex implementation.

A manufacturing supplier may provide competitive pricing but have longer delivery times.

The evaluation should preserve these trade-offs rather than presenting a single unexplained overall score.

Multi-criteria decision analysis provides a relevant theoretical foundation for evaluating alternatives against competing objectives and explicitly examining trade-offs (Keeney and Raiffa, 1976).

3.8.2 Weighting

Some evaluation methods assign weights to different criteria.

However, weighting introduces assumptions about the relative importance of requirements.

Where weights are used, they should be documented and aligned with the user's preferences.

Arbitrary weighting may produce misleading provider rankings.

3.8.3 Missing Data

A candidate may lack information required for a complete comparison.

Missing information should not automatically be treated as poor performance.

Researchers should distinguish between a negative finding and an absence of evidence.

3.8.4 Comparison Consistency

Repeated evaluations using equivalent requirements may produce different candidate rankings.

This creates a research opportunity to investigate recommendation stability.

Such testing should document the prompts, candidate information, platform configuration and observation conditions.

Research benchmarks for language-model agents demonstrate the importance of specifying evaluation environments and tasks when comparing agent performance (Liu et al., 2024); (Zhou et al., 2024).

3.9 Stage Eight — Shortlist Construction

Shortlist construction is the stage at which an agent presents a selected group of candidates for further consideration.

This is a commercially important distinction.

An organisation may have been discovered and evaluated without appearing in the final shortlist.

Consequently, candidate discovery visibility and shortlist visibility should be measured separately.

3.9.1 Shortlist Inclusion

A shortlist may contain several potentially suitable alternatives.

Inclusion does not necessarily establish that the agent prefers one candidate over another.

3.9.2 Recommendation Position

Some systems may present candidates in a particular order.

However, the meaning of that order may not be explicit.

Researchers should avoid assuming that the first-listed candidate is always the preferred provider.

Where recommendation priority is explicitly stated, it can be classified separately.

3.9.3 Explanation Quality

A credible shortlist should explain why candidates were included.

It should identify relevant supporting evidence and important limitations.

A recommendation without an understandable rationale may be difficult for a user to evaluate.

Research into explainable machine learning and human–AI interaction provides relevant foundations for examining how explanations can support human understanding and assessment of AI-generated outputs (Ribeiro et al., 2016); (Amershi et al., 2019).

3.10 Stage Nine — Human Review and Authorised Action

The final stage concerns the relationship between agent-generated research and commercial action.

A user may review the shortlist, request additional information or contact a provider.

Where the agent has appropriate permissions and tool access, it may also prepare or execute specific authorised workflow steps.

However, the authority to retrieve information does not automatically include authority to submit enquiries, disclose personal information or make financial commitments.

Research into levels of human interaction with automation provides a relevant theoretical foundation for distinguishing different degrees of human control and automated action (Parasuraman, Sheridan and Wickens, 2000).

3.10.1 Human Review

A user may examine the supporting evidence before accepting a recommendation.

The agent's output should make material uncertainties visible.

3.10.2 Provider Contact

A user or authorised agent may contact a provider to request additional information.

This represents a different outcome from shortlist inclusion.

3.10.3 Transaction Execution

A transaction may involve financial commitments, contractual terms or personal data.

Appropriate permissions and safeguards are therefore necessary.

The paper does not assume that autonomous transaction execution is appropriate for every commercial context.

The NIST Artificial Intelligence Risk Management Framework provides relevant guidance for examining AI-related risks, accountability and governance throughout system operation (NIST, 2023).

3.11 The Autonomous Discovery Funnel

The nine-stage model can also be examined as a sequence of observable commercial discovery outcomes.

This funnel is a proposed analytical representation.

Actual agent workflows may not expose every stage.

Researchers should not infer unobserved stages solely from the final response.

The distinction between observed behaviour and inferred internal processing is especially important when evaluating proprietary systems whose intermediate reasoning or tool interactions are not available to external researchers.

Agent evaluation research provides relevant methodological foundations for examining observable task performance, but it does not validate the commercial discovery funnel proposed here (Zhou et al., 2024).

3.12 Discovery Failure and Exclusion Points

An organisation may fail to progress through the discovery process for different reasons.

These reasons should be distinguished when investigating agent-mediated visibility.

These failure points have different commercial implications.

They should not be combined into a single unexplained visibility metric.

The proposed classification also distinguishes a genuine provider deficiency from an agent error or an information-access limitation. This distinction is important when interpreting commercial discovery outcomes.

3.13 Measuring Autonomous Business Discovery

The proposed model creates opportunities for stage-specific measurement.

However, measurement depends on access to reliable observation records.

Where agent intermediate actions are unavailable, researchers may only be able to evaluate the final output.

This limitation must be documented.

Research on agent benchmarking and classification evaluation provides relevant methodological foundations for defining observable outcomes, reference classifications and performance measures (Liu et al., 2024); (Sokolova and Lapalme, 2009).

3.13.1 Candidate Discovery Rate

The proportion of eligible tasks in which a specified organisation is identified.

3.13.2 Evidence Retrieval Rate

The proportion of relevant candidate evaluations in which specified evidence categories are retrieved.

3.13.3 Verification Completion Rate

The proportion of required verification checks completed according to a defined research protocol.

3.13.4 Eligibility Assessment Accuracy

The proportion of observable eligibility decisions that agree with an independently established reference classification.

3.13.5 Shortlist Inclusion Rate

The proportion of eligible tasks in which a specified provider appears in the final shortlist.

3.13.6 Recommendation Consistency

The stability of recommendations across repeated evaluations under documented conditions.

3.13.7 Authorised Action Rate

The proportion of eligible workflows in which a specified permitted action is completed.

This metric should be distinguished from commercial conversion and financial value.

These measures are proposed research variables. Their names and definitions should not be interpreted as universally accepted industry standards or as evidence that the corresponding measurements have already been collected.

3.14 Measurement Denominators and Interpretation

Measurement denominators are critical to interpreting agent discovery statistics.

A provider may be eligible for only a subset of the tasks included in a research sample.

Using all tasks as the denominator may therefore understate its visibility within relevant commercial requirements.

Conversely, excluding difficult or unfavourable tasks may artificially inflate reported performance.

Researchers should define eligibility criteria before collecting observations.

The report should disclose the total sample, the eligible sample and the number of observations supporting each metric.

Research on classification performance demonstrates the importance of carefully selecting and interpreting evaluation measures, particularly where different error types have different consequences (Sokolova and Lapalme, 2009).

For agent-mediated discovery, denominator definitions should also distinguish tasks that were genuinely relevant to a provider from tasks in which that provider could not reasonably meet the user's requirements.

3.15 Research Design for Empirical Validation

Future studies could evaluate the proposed model through controlled agent discovery tasks.

A research protocol might define commercial requirements, candidate populations, available information sources and reference eligibility classifications.

Agents could then be evaluated against those defined tasks.

Researchers could record observable candidate discovery, evidence retrieval, eligibility assessment and final recommendations.

Where appropriate, independent reviewers could assess whether the agent's classifications were supported by the available evidence.

Such research would require careful control of task design, platform configuration and observation conditions.

It would also require explicit recognition of the limitations associated with proprietary systems.

Existing research benchmarks, including AgentBench and WebArena, provide relevant examples of structured agent evaluation across defined tasks and interactive environments (Liu et al., 2024); (Zhou et al., 2024).

However, those benchmarks should not be interpreted as direct empirical validation of the CGO Media Autonomous Business Discovery Model.

A dedicated commercial discovery study would need to establish its own task population, provider eligibility criteria, evidence standards and observable outcomes.

It would also need to document model versions, tool availability, sampling procedures and the dates on which observations were collected.

The methodological importance of documenting datasets and their intended uses is supported by research on datasheets for datasets (Gebru et al., 2021).

3.16 Commercial Implications for Organisations

The model suggests that organisational visibility may need to be examined across several distinct discovery stages.

An organisation may be discoverable but poorly documented.

It may have extensive documentation but lack independent verification for important claims.

It may satisfy mandatory requirements but be excluded during comparative evaluation.

Alternatively, it may reach the final shortlist without generating a commercial enquiry or transaction.

These outcomes should not be treated as equivalent.

For organisations investigating agent-mediated discovery, the practical research priority is to understand which stages are observable and what evidence supports conclusions about performance.

The commercial implications presented here are reasoned hypotheses derived from the proposed discovery model. They do not establish that a particular optimisation intervention will increase agent recommendations, enquiries or sales.

3.17 Relationship to Entity Authority and AI Recommendation Research

The Autonomous Business Discovery Model connects with several existing CGO Media research themes.

Entity authority concerns how organisations are identified and represented across information environments.

Citation and source selection concern the retrieval and use of supporting information.

Recommendation authority concerns the circumstances under which organisations may be presented as suitable alternatives.

Agent-mediated discovery potentially connects these activities within a longer research workflow.

Knowledge graph research provides a relevant foundation for the entity representation component (Hogan et al., 2021).

Retrieval-augmented generation research provides a foundation for examining the relationship between retrieved sources and generated outputs (Lewis et al., 2020).

Research into autonomous language-model agents provides relevant foundations for examining the execution of multi-step tasks (Yao et al., 2023); (Wang et al., 2024).

However, the relationships between these factors and commercial selection outcomes require empirical investigation.

Section 3 — Research Interpretation

Autonomous business discovery should not be understood as a single event in which an AI agent selects a provider.

It is more appropriately examined as a sequence of research, verification, evaluation and decision-support activities.

Each stage introduces different opportunities for organisational visibility, information errors and candidate exclusion.

The distinction between candidate discovery and final recommendation is particularly important.

An organisation may be retrieved and evaluated without appearing in the final response presented to the user.

Consequently, researchers should avoid inferring complete internal agent behaviour from final recommendations alone.

This interpretation is consistent with the broader methodological emphasis on evaluating observable agent behaviour against defined tasks, although the nine-stage commercial discovery model remains a CGO Media conceptual contribution (Zhou et al., 2024).

Supporting CGO Media Research

• Entity Authority in AI Search
• AI Source Selection in Generative Search
• AI Citation Selection in Generative Search
• AI Recommendation Authority in Generative Search
• Knowledge Graph Optimisation and AI Search
• CGO Media Framework Library
• CGO Media Research Methodology

Section 3 — Key Finding

4. Entity Authority, Trust and Machine-Readable Evidence

The development of agent-mediated business discovery introduces important questions concerning how organisations are identified, represented and evaluated by artificial intelligence systems.

An AI agent investigating potential suppliers or service providers may encounter information distributed across organisational websites, structured databases, professional registers, technical documentation and independent publications.

Research into knowledge graphs provides a foundation for understanding how entities and their relationships can be represented across structured information environments (Hogan et al., 2021).

These sources may contain different descriptions of the same organisation, varying levels of detail and information published at different times.

The agent may therefore need to establish which organisation is being examined, what that organisation offers, whether its capabilities match the user's requirements and which claims can be supported by reliable evidence.

This process involves several distinct activities: entity identification, information retrieval, claim interpretation, evidence assessment and commercial suitability evaluation.

Although these activities may interact, they should not be treated as equivalent.

An organisation can be correctly identified without being commercially suitable. A provider can publish extensive technical documentation without independently verifying every claim.

Similarly, an organisation can possess credible professional qualifications without necessarily meeting the requirements of a particular customer.

This section develops an analytical framework for examining these distinctions.

4.1 What Entity Authority Means in Agent-Mediated Discovery

An entity is a distinguishable person, organisation, product, place or other identifiable subject represented within an information environment.

In commercial discovery, organisational entities may include registered companies, trading businesses, professional practices, manufacturers, software providers and other service organisations.

Entity authority is used in this research to describe the degree to which relevant organisational information can be identified, substantiated and evaluated through appropriate supporting evidence.

This is an analytical definition informed by research into entity representation, knowledge graphs and information provenance (Hogan et al., 2021); (W3C, 2013).

It does not imply that AI platforms use a universal entity authority score or that such a score determines their recommendations.

Different systems may retrieve, interpret and evaluate organisational information through different mechanisms.

The research objective is to examine which characteristics of entity information may be relevant to agent-mediated evaluation, not to claim knowledge of proprietary selection algorithms.

4.2 The Organisational Identity Problem

Before evaluating an organisation's capabilities, an agent may need to establish its identity.

This can be more complicated than identifying a company name within a search result.

Businesses may operate under trading names that differ from their registered legal names. A corporate group may contain several subsidiaries operating in different countries.

A brand may be owned by one organisation while products or services are delivered through another legal entity.

Historical company records may refer to organisations that have since changed ownership, location or structure.

Similarly named businesses may operate in unrelated industries.

These situations create opportunities for entity confusion.

Research into entity resolution and record linkage provides an established foundation for examining whether different records refer to the same real-world subject (Christen, 2012).

An agent may incorrectly associate a professional qualification, office location or commercial capability with the wrong organisation.

The consequences can be significant where provider selection depends on legal identity, regulatory status or contractual responsibility.

4.3 Entity Resolution and Identity Verification

Entity resolution is the process of determining whether different records refer to the same real-world entity.

Entity resolution research examines methods for comparing records, identifying matches and managing uncertainty when information is incomplete or inconsistent (Christen, 2012).

For commercial organisations, relevant identifying information may include:

• Registered legal name.
• Trading or brand names.
• Company registration identifiers.
• Official website domain.
• Registered business address.
• Current operating locations.
• Professional registration details.
• Corporate ownership relationships.
• Historical names and organisational changes.

The relevance of each identifier depends on the organisation and the jurisdiction.

No single identifier should be assumed to resolve every entity identification problem.

For example, a website domain may be associated with a brand rather than the legal entity responsible for delivering services.

Similarly, a registered address may differ from the location where a business operates.

An agent evaluating commercial suitability may need to distinguish these relationships.

4.4 The CGO Media Agent-Ready Entity Evidence Model

CGO Media proposes the Agent-Ready Entity Evidence Model as a structured method for evaluating the information available to AI agents during organisational discovery.

The model contains eight analytical dimensions.

These dimensions describe different aspects of organisational evidence rather than a single numerical authority score.

The model draws conceptually on established research into knowledge graphs, entity resolution, information provenance, data quality and information retrieval (Hogan et al., 2021); (Christen, 2012); (Wang and Strong, 1996).

However, the particular combination of eight dimensions is an original CGO Media analytical contribution and has not been independently validated as a predictor of agent recommendations.

These eight dimensions provide a proposed research structure for examining agent-ready organisational information.

They are not presented as a validated representation of how any particular AI platform ranks or selects providers.

4.5 Machine-Readable Information and Human-Readable Evidence

Machine-readable information is information presented in a form that software systems can process according to a defined structure or representation.

Examples include structured data, document metadata, API responses and machine-readable records.

The W3C Resource Description Framework provides an established technical foundation for representing information about resources and their relationships in a machine-processable form (W3C, 2014).

However, machine-readable information is not necessarily accurate.

A structured record can contain outdated or incorrect information. An API may return a technically valid response containing incomplete commercial details.

Similarly, a website may contain accurate human-readable information that an agent fails to retrieve or interpret correctly.

Research into information quality identifies accuracy, completeness, timeliness and relevance as distinct considerations when evaluating data for use (Wang and Strong, 1996).

Machine readability and evidential reliability should therefore be evaluated separately.

4.6 Structured Data and Organisational Representation

Structured data provides a way to describe information using defined properties and relationships.

For organisational websites, structured information may describe an entity's name, website, location, contact information, products or services.

Schema.org provides a widely used vocabulary for representing structured information on the web (Schema.org, n.d.).

The Resource Description Framework provides a broader technical foundation for representing structured relationships between resources (W3C, 2014).

However, structured data should not be interpreted as a direct instruction to AI agents to recommend an organisation.

Its potential relevance lies in making certain relationships more explicit and machine-interpretable.

Whether a particular agent retrieves, uses or relies upon such information depends on its architecture, tools and available sources.

Research is therefore required to establish the practical effects of structured data within specific agent workflows.

Potential Organisational Information Categories

• Organisation and legal entity information.
• Brand and trading-name relationships.
• Office and operating locations.
• Contact channels.
• Products and service descriptions.
• Professional affiliations where applicable.
• Relevant publications.
• Documented organisational relationships.

These categories should reflect verifiable organisational facts.

Structured data should not be used to manufacture unsupported credentials or relationships.

4.7 Knowledge Graphs and Entity Relationships

Knowledge graphs represent entities and relationships within a structured information environment.

Research by Hogan et al. (2021) provides a comprehensive foundation for understanding knowledge graph representation, construction and applications (Hogan et al., 2021).

They can provide a way to connect organisations with products, locations, people and other entities.

For commercial discovery, entity relationships may help distinguish a parent company from its subsidiaries or a product from its manufacturer.

They may also support the representation of professional affiliations, research publications and organisational history.

However, not every AI agent uses the same knowledge graph or entity resolution process.

The presence of information within a knowledge graph does not guarantee that an agent will retrieve or rely upon it.

The relevant research question is whether access to accurate entity relationships improves observable identification and evaluation outcomes.

Knowledge Graph Optimisation and AI Search

4.8 Official Registers and Regulatory Evidence

Some commercial decisions depend on legal or regulatory status.

A financial services provider may require specific authorisation.

A legal professional may need to appear on an appropriate professional register.

A healthcare provider may be subject to relevant licensing or professional requirements.

These requirements vary between jurisdictions and professional activities.

An agent conducting provider research may need to consult appropriate official sources.

However, the existence of a registration record does not necessarily establish overall service quality.

Regulatory eligibility and commercial suitability are different questions.

The distinction is consistent with multi-criteria decision analysis, in which satisfying necessary conditions does not automatically establish superiority across other objectives (Keeney and Raiffa, 1976).

4.9 Professional Credentials and Qualifications

Professional qualifications can be important evidence when selecting specialist service providers.

However, their significance depends on the service and the jurisdiction.

A professional qualification may establish completion of a defined programme without demonstrating current commercial performance.

Membership of a professional association may indicate an affiliation but may not constitute regulatory authorisation.

Similarly, a certification may apply to a particular individual, product or business process rather than the entire organisation.

Agent-mediated evaluation should distinguish these relationships.

Claims concerning credentials should be interpreted according to their actual scope.

These distinctions form part of the proposed CGO Media evidence-assessment approach. They should be evaluated against the applicable professional and regulatory requirements rather than inferred from a generic AI authority score.

4.10 Independent Publications and Third-Party Evidence

Organisations may be described within independent research, industry publications, professional directories and specialist assessments.

These sources can provide information beyond the organisation's own website.

However, third-party publication does not automatically establish reliability.

An independent article may contain errors. A commercial directory may contain outdated information.

A review platform may include subjective or unverified statements.

An agent should therefore evaluate the relevance, currency and reliability of each source.

Research into information quality supports examining multiple dimensions of evidence rather than treating source availability as proof of accuracy (Wang and Strong, 1996).

Source independence is one consideration among several.

4.11 Research Publications as Evidence

Research publications may contribute evidence of an organisation's subject-matter activity.

They can document analytical methods, conceptual frameworks, technical investigations and published findings.

However, publication volume does not independently establish research quality or commercial competence.

A publication should be evaluated according to its methodology, evidence, limitations and relevance.

Research on dataset documentation and model reporting illustrates the importance of transparent methods, intended uses and limitations when evaluating technical research outputs (Gebru et al., 2021); (Mitchell et al., 2019).

Similarly, publication within a repository does not necessarily mean that the research has undergone independent peer review.

Agent-mediated evaluation should distinguish research availability from research validation.

This distinction is relevant to organisations that publish substantial research libraries or technical documentation.

4.12 Source Provenance and Claim Traceability

Source provenance concerns the origin and history of information used during an evaluation.

Claim traceability concerns whether a particular statement can be connected to supporting evidence.

The W3C PROV-O ontology provides a technical framework for describing provenance relationships between entities, activities and agents (W3C, 2013).

These concepts are related but distinct.

An agent may identify where a statement appeared without establishing that the statement is correct.

Similarly, several websites may repeat the same unsupported claim.

Repetition does not necessarily constitute independent corroboration.

A credible evaluation should consider whether supporting sources are genuinely independent and appropriate to the claim.

This evidence record is a proposed CGO Media research instrument. Its purpose is to make claim-level evaluation more transparent and reproducible.

4.13 Information Currency and Historical Records

Organisational information can change over time.

A business may relocate, introduce new services, change its ownership or discontinue products.

A professional registration may expire or be renewed.

A technical certification may apply only during a defined period.

Historical information may remain accurate as a record of past activity while being unsuitable for describing current operations.

An agent should therefore distinguish historical facts from current claims.

The relevant date should be considered when evaluating commercial suitability.

Research into information quality identifies timeliness as an important consideration when determining whether information is fit for its intended use (Wang and Strong, 1996).

4.14 Conflicting Organisational Information

Conflicting information can arise for legitimate reasons.

A registered office may differ from an operating location.

A corporate name may differ from a trading brand.

A company may have changed ownership or reorganised its operations.

Other conflicts may indicate outdated or incorrect information.

An agent should not automatically treat every difference as a factual contradiction.

Instead, the evaluation should consider the meaning and context of each record.

Entity resolution research provides relevant foundations for interpreting inconsistent identifying information across records (Christen, 2012).

Where material conflicts remain unresolved, the uncertainty should be documented.

4.15 Agent Accessibility and Technical Retrieval

An organisation may publish accurate information that is difficult for an agent to retrieve.

Possible reasons include authentication requirements, technical restrictions, inaccessible document formats or limitations of the agent's available tools.

Research into retrieval-augmented generation demonstrates how external information retrieval can support language-model outputs, while agent research examines the use of external tools during task execution (Lewis et al., 2020); (Schick et al., 2023).

However, technical accessibility should not be confused with an obligation to provide unrestricted automated access.

Organisations may have legitimate security, privacy, licensing or commercial reasons for restricting certain information.

The relevant research question is whether the information necessary for a particular authorised evaluation can be accessed and interpreted reliably.

4.16 APIs and Structured Commercial Information

Application programming interfaces may provide structured access to specific information or services.

In agent-mediated commercial discovery, an authorised API may allow a system to retrieve product details, availability or other relevant business information.

Research into tool-using language models provides a relevant foundation for examining how AI systems may interact with external software capabilities (Schick et al., 2023).

However, API access depends on permissions, technical compatibility and the capabilities of the connected systems.

An API response may also be incomplete or outdated.

Consequently, structured API access should be evaluated for accuracy, relevance and authorisation.

The existence of an API does not establish that an organisation will be selected or recommended by an AI agent.

4.17 Agent Instructions and Machine-Readable Files

Some organisations publish machine-readable files intended to describe website content or provide information for automated systems.

Such files may help communicate available resources or organisational information.

However, the effectiveness of any particular file format depends on whether an agent or platform supports and uses it.

A published file should not be assumed to influence all AI systems.

Similarly, instructions contained in retrieved website content should not automatically override the agent's governing instructions or the user's authorised objective.

Research into indirect prompt injection demonstrates why untrusted retrieved content can create security risks when language-model systems interact with external information sources (Greshake et al., 2023).

The distinction between task instructions and retrieved source content is important for agent security and reliability.

4.18 Prompt Injection and Untrusted Content

An AI agent may encounter instructions embedded within retrieved documents or web pages.

Some instructions may be unrelated to the user's legitimate task.

Others may attempt to redirect the agent's behaviour or induce unauthorised actions.

This creates a security challenge commonly associated with prompt injection.

Greshake et al. (2023) examine indirect prompt injection attacks against language-model-integrated applications, demonstrating how external content may be used to influence system behaviour (Greshake et al., 2023).

Retrieved information should be treated as task data unless explicitly authorised otherwise through the system's governing controls.

An organisation's ability to publish text that an agent retrieves should not confer authority over the agent's actions.

4.19 Trust and Commercial Suitability Are Different

An organisation may have accurate identity records, verifiable credentials and reliable documentation.

However, it may still be unsuitable for a particular commercial requirement.

For example, a technically credible provider may not operate in the required geographic market.

A professionally qualified adviser may not offer the specific service requested.

A reliable manufacturer may lack the required production capacity.

Trust-related evidence should therefore be distinguished from task-specific suitability.

The agent must evaluate both questions.

Multi-criteria decision analysis provides a relevant foundation for understanding why strength on one evaluation dimension does not necessarily establish overall suitability (Keeney and Raiffa, 1976).

4.20 Measuring Entity Evidence Quality

The proposed Agent-Ready Entity Evidence Model creates opportunities for structured research measurement.

However, the eight dimensions should not automatically be combined into a single authority score.

Different commercial tasks require different evidence.

A regulatory record may be essential for one provider category and irrelevant to another.

Researchers should therefore define the relevant evidence requirements before evaluating candidate organisations.

Established research on information quality demonstrates that data quality involves multiple dimensions and must be evaluated in relation to intended use (Wang and Strong, 1996).

Any future composite score would require a separately justified weighting methodology and empirical validation.

4.21 Proposed Entity Evidence Metrics

CGO Media proposes five measures for investigating entity identification and evidence quality.

These metrics are research proposals rather than established measures of AI platform authority.

These are proposed research metrics.

They require clear definitions, documented denominators and independent reference classifications where accuracy is being measured.

Research on classification performance highlights the importance of distinguishing different types of error and interpreting evaluation metrics within their intended context (Sokolova and Lapalme, 2009).

4.22 An Agent-Ready Entity Evidence Audit

Organisations may use the proposed model to structure an internal review of their published information.

Such a review should not be presented as a guarantee of improved AI recommendations.

Its immediate purpose is to identify information gaps, inconsistencies and verification requirements.

The audit structure draws conceptually on entity resolution, information quality and provenance principles (Christen, 2012); (Wang and Strong, 1996); (W3C, 2013).

However, the checklist itself is a proposed CGO Media instrument and has not been validated as a predictor of commercial recommendation outcomes.

4.23 Research Design for Empirical Validation

Future research could investigate the relationship between organisational information quality and agent-mediated provider evaluation.

A controlled study might compare agent performance across different information conditions.

For example, researchers could examine whether clearer entity identifiers reduce identity confusion.

They could investigate whether access to authoritative records improves verification accuracy.

They could also examine whether structured product information affects the completeness of retrieved evidence.

Such studies would require careful experimental design.

Researchers should control for differences in underlying factual content when testing presentation formats.

They should also distinguish information retrieval success from final recommendation outcomes.

A change in one stage does not necessarily produce a change in another.

Research benchmarks such as WebArena and AgentBench provide relevant examples of structured evaluation of AI agents against defined tasks and environments (Zhou et al., 2024); (Liu et al., 2024).

However, these benchmarks do not independently validate the Agent-Ready Entity Evidence Model.

A dedicated study would need to establish reference entity identities, predefined evidence requirements, independent verification criteria and repeatable observation procedures.

Where possible, researchers should document the agent platform, model version, retrieval tools, test dates, task instructions and information sources.

Dataset documentation research provides relevant methodological support for recording data provenance, intended use and limitations (Gebru et al., 2021).

4.24 Limitations of Entity Evidence Research

Several limitations must be considered.

First, external researchers may not observe every source consulted by a proprietary AI agent.

Second, different systems may apply different retrieval and evaluation methods.

Third, source availability can change over time.

Fourth, the reliability of evidence depends on the specific claim being evaluated.

Fifth, organisational information quality does not independently determine commercial suitability.

Finally, observed correlations between information characteristics and agent recommendations do not establish causal relationships without appropriate research designs.

These limitations should be documented when interpreting results from future empirical studies.

The distinction between observable task performance and inferred internal behaviour is particularly important in research involving proprietary agent systems (Zhou et al., 2024).

4.25 Implications for SEO and GEO

Entity-focused SEO and GEO research may become relevant to the study of agent-mediated business discovery.

However, the research emphasis should extend beyond visibility alone.

Organisations may need to consider whether their identity, capabilities and supporting evidence can be accurately interpreted.

This includes distinguishing official claims from independent verification.

It also includes maintaining accurate information across relevant digital sources.

Knowledge graph and information quality research provides a relevant foundation for examining these characteristics (Hogan et al., 2021); (Wang and Strong, 1996).

Nevertheless, these activities should not be represented as guaranteed methods for securing agent recommendations.

Their effects require empirical evaluation.

4.26 Implications for Commercial Provider Selection

The proposed model suggests that agent-mediated provider selection may involve several distinct evidence-related failure points.

An organisation may be correctly discovered but incorrectly identified.

It may be identified but lack sufficient evidence for eligibility assessment.

It may have reliable documentation but fail a mandatory commercial requirement.

Alternatively, it may satisfy the requirements but not appear within the final shortlist.

These outcomes should be measured separately.

The commercial significance of entity evidence depends on how it affects the specific evaluation process.

The distinction between evidence quality and comparative suitability is consistent with multi-criteria decision principles, although the particular agent-mediated failure-point analysis is proposed by CGO Media (Keeney and Raiffa, 1976).

Section 4 — Research Interpretation

Agent-mediated business discovery introduces additional importance to the interpretation of organisational information.

An agent may need to identify an organisation, retrieve relevant documentation, evaluate claims and determine whether available evidence supports commercial suitability.

These activities are distinct.

Machine-readable information may support retrieval and interpretation, but it does not independently establish accuracy.

Independent evidence may support verification, but it does not automatically establish commercial preference.

Similarly, accurate organisational information does not guarantee shortlist inclusion or recommendation.

The proposed Agent-Ready Entity Evidence Model provides a structured basis for investigating these relationships.

The underlying research foundations include knowledge graphs, entity resolution, information provenance, data quality and multi-criteria evaluation (Hogan et al., 2021); (Christen, 2012); (W3C, 2013); (Wang and Strong, 1996).

The eight-dimensional model itself remains an original conceptual contribution requiring future empirical validation.

Supporting CGO Media Research

• Entity Authority in AI Search
• Knowledge Graph Optimisation and AI Search
• Brand Authority Signals in AI Search
• AI Citation Authority in Generative Search
• AI Source Selection in Generative Search
• AI Recommendation Authority in Generative Search
• CGO Media Framework Library
• CGO Media Research Methodology

Section 4 — Key Finding

5. AI Agent Recommendation and Provider Selection

The ability of artificial intelligence agents to investigate commercial requirements and compare potential providers introduces important questions concerning recommendation formation, provider suitability and decision authority.

Traditional search engines generally present resources that users can investigate. Conversational AI systems may generate comparisons or recommend organisations in response to questions.

Agent-mediated discovery may extend these activities by allowing software systems to undertake multiple research and evaluation steps before presenting a recommendation (Yao et al., 2023); (Schick et al., 2023).

However, provider discovery, eligibility assessment, comparative evaluation, recommendation and commercial selection represent different outcomes.

These distinctions are essential when investigating the potential influence of AI agents on commercial discovery and customer decision-making.

This section develops a structured model for examining how AI agents may construct recommendations, how selection criteria can influence outcomes and where uncertainty or evaluation errors may arise.

The proposed model is conceptual and methodological. It does not claim to describe the proprietary recommendation algorithms of individual AI platforms.

5.1 The Difference Between Discovery and Recommendation

An organisation may appear within an AI agent's research process without being recommended.

For example, an agent investigating software providers may initially identify numerous potential candidates.

Some candidates may be excluded because they do not satisfy mandatory requirements.

Others may remain eligible but perform less favourably against the user's preferences.

A smaller group may then be presented within a final shortlist.

The agent may recommend one or more of those candidates for further consideration.

These stages should not be treated as equivalent measures of visibility.

5.2 The CGO Media Agent Recommendation and Provider Selection Model

CGO Media proposes the Agent Recommendation and Provider Selection Model as a framework for examining how agents may evaluate commercial alternatives.

The model contains eight analytical dimensions.

The proposed dimensions draw conceptually on research into multi-criteria decision analysis, information quality and human–AI interaction (Keeney and Raiffa, 1976); (Wang and Strong, 1996); (Amershi et al., 2019).

These dimensions describe the information and decisions that may contribute to provider recommendations.

They should not be interpreted as a universal ranking formula or an established description of how commercial AI agents operate.

5.3 Requirement Alignment

Requirement alignment is a central consideration in provider selection. Multi-criteria decision analysis provides a relevant foundation for evaluating alternatives against defined objectives (Keeney and Raiffa, 1976).

A recommendation should address the actual problem the user is attempting to solve.

An organisation may possess substantial experience or a strong reputation without meeting the requirements of a particular task.

For example, a software provider may offer extensive functionality but lack a required integration.

A professional services organisation may have relevant expertise but operate outside the required jurisdiction.

A manufacturer may have suitable technical capabilities but lack the necessary production capacity.

These examples illustrate why general organisational credibility should not be confused with task-specific suitability.

5.3.1 Mandatory Requirements

Mandatory requirements establish conditions that must be satisfied before a candidate can be considered fully eligible.

These may include professional authorisation, technical compatibility, geographic coverage or defined operational capabilities.

A candidate that fails a mandatory requirement should not be recommended as fully suitable merely because it performs strongly on other criteria.

5.3.2 Preferred Characteristics

Preferences influence the relative attractiveness of eligible candidates.

Examples include preferred pricing, additional functionality, service arrangements or specialist experience.

Preferences may require trade-offs, an established consideration in multi-objective decision analysis (Keeney and Raiffa, 1976).

An agent should avoid treating every preference as a mandatory condition unless the user has specified that requirement.

5.3.3 Ambiguous Requirements

Some requirements may be insufficiently defined to support reliable evaluation.

A request for the best provider may not specify what best means in the relevant context.

An agent may need to request clarification or present alternative evaluation scenarios. Human–AI interaction research provides relevant guidance concerning clarification and communicating uncertainty (Amershi et al., 2019).

Unsupported assumptions can materially change which providers are recommended.

5.4 Eligibility Before Preference

Eligibility assessment should be distinguished from comparative ranking.

An organisation may be commercially attractive but unsuitable because it does not satisfy an essential requirement.

Conversely, an organisation may satisfy every mandatory condition without being the preferred option.

The proposed model therefore separates eligibility determination from comparative preference.

5.5 Multi-Criteria Provider Evaluation

Commercial provider selection frequently involves multiple evaluation criteria. Multi-criteria decision analysis provides a methodological foundation for examining competing objectives and trade-offs (Keeney and Raiffa, 1976).

These criteria may include:

• Technical or professional capability.
• Geographic suitability.
• Regulatory eligibility.
• Commercial pricing.
• Implementation requirements.
• Service availability.
• Relevant experience.
• Operational risk.
• Support and maintenance arrangements.
• Evidence quality and verification status.

The relevance of each criterion depends on the user's requirements.

An agent may attempt to organise these considerations into a comparative evaluation.

However, different evaluation methods can produce different recommendations.

Researchers should therefore examine how criteria are selected, interpreted and applied.

5.6 Weighted Scoring and Its Limitations

One possible evaluation method assigns weights to different selection criteria.

For example, technical capability may be assigned greater importance than price in a specialist technology procurement task.

However, weighted scoring introduces methodological assumptions. The importance of defining and justifying preferences is addressed in multi-objective decision theory (Keeney and Raiffa, 1976).

The selected weights may substantially affect the final ranking.

A small change in weighting may alter which candidate appears most suitable.

Consequently, a weighted score should not be presented as an objective measure of universal provider quality.

5.7 Mandatory Constraints Should Not Be Averaged Away

Weighted scoring can create misleading results when mandatory requirements are treated as ordinary preferences.

A provider that lacks a required professional authorisation should not become eligible merely because it scores highly on price or customer service.

Similarly, a software product that cannot support an essential integration should not necessarily be recommended because it offers other attractive features.

The proposed model therefore recommends evaluating mandatory constraints before applying comparative preference scoring.

5.8 Evidence Quality and Recommendation Confidence

An agent may identify a candidate that appears suitable based on available descriptions.

However, the evidence supporting that conclusion may be incomplete.

For example, a provider may claim a particular technical capability without supplying sufficient documentation for verification.

Another provider may have independently documented evidence supporting the same capability.

These situations should not automatically receive equivalent evidence classifications.

However, stronger documentation does not independently establish that a provider is commercially superior in every respect.

Evidence confidence and comparative suitability should remain distinct. Research into information quality and provenance provides relevant foundations for evaluating supporting material (Wang and Strong, 1996); (W3C, 2013).

5.9 Recommendation Explainability

A recommendation is more useful for human review when the supporting rationale can be examined.

Human–AI interaction research and AI risk-management guidance provide relevant foundations for examining transparency, oversight and the communication of system limitations (Amershi et al., 2019); (NIST, 2023).

An agent may recommend a provider because it appears to satisfy defined technical, commercial or operational requirements.

The explanation should identify which evidence supports that conclusion.

It should also distinguish verified information from assumptions or unresolved uncertainty.

An unexplained recommendation may be difficult to evaluate, particularly in consequential commercial contexts.

5.10 Recommendation Confidence Is Not a Universal Probability

An AI system may describe a recommendation as high confidence.

However, the meaning of such a statement depends on how confidence is defined.

A model-generated confidence description should not automatically be interpreted as a calibrated probability of commercial suitability. Research on neural network calibration illustrates why predicted confidence and observed correctness must be distinguished (Guo et al., 2017).

Similarly, a numerical recommendation score does not necessarily represent the probability that a customer will select a provider.

Research into recommendation confidence should therefore distinguish:

• Confidence in organisational identity.
• Confidence in factual evidence.
• Confidence in eligibility assessment.
• Confidence in comparative suitability.
• Confidence in predicted commercial outcomes.

These are different research questions.

5.11 Managing Missing Information

Commercial provider comparisons often contain incomplete information.

One provider may publish detailed technical documentation while another provides only a general service description.

A candidate may not publish current pricing.

Another may require a consultation before confirming availability.

Missing information should not automatically be treated as evidence of poor performance.

However, missing information may limit the reliability of a recommendation.

An agent may need to identify the gap, request clarification or recommend additional verification.

5.12 Conflicting Evidence and Recommendation Stability

An agent may encounter contradictory information about a candidate.

For example, an official website may describe one operating location while an external directory contains an older address.

A technical document may describe functionality that differs from a marketing summary.

The agent should evaluate the context and currency of the conflicting sources. Information quality and provenance research provides relevant foundations for this assessment (W3C, 2013); (Wang and Strong, 1996).

Where the conflict cannot be resolved, the uncertainty should remain visible.

Researchers should investigate whether such conflicts affect shortlist inclusion or recommendation outcomes.

5.13 Provider Comparisons and Relative Suitability

A provider's apparent suitability depends partly on the alternatives included in the comparison.

A candidate may perform favourably within one shortlist but less favourably when additional providers are considered.

This creates an important methodological issue for agent recommendation research.

Researchers should document the candidate population where it is observable.

They should also distinguish recommendations made within a restricted candidate set from claims about the wider market.

A recommendation generated from a limited research sample should not automatically be presented as evidence that the provider is the best available organisation overall.

5.14 Shortlist Construction

A shortlist is a selected set of candidates presented for further consideration.

Shortlist inclusion may be commercially important because it determines which organisations are presented to the user.

However, shortlist inclusion is not necessarily equivalent to an explicit recommendation.

An agent may present several eligible alternatives without identifying a preferred option.

Alternatively, it may recommend one provider while identifying others as viable alternatives.

These presentation differences should be classified separately.

5.15 Recommendation Types

CGO Media proposes distinguishing several types of provider recommendation.

These classifications are proposed for research and reporting.

They should be applied according to documented coding criteria.

5.16 The Difference Between Recommendation and Persuasion

A provider recommendation may influence a user's commercial evaluation.

However, the presence of a recommendation does not establish that the user accepted it.

The user may reject the recommendation, request alternatives or undertake additional research.

The recommendation may also contain limitations that reduce its commercial relevance.

Consequently, recommendation visibility should not be reported as confirmed customer acquisition.

Commercial influence requires additional supporting evidence.

5.17 Human Review and Decision Authority

Agent-mediated provider evaluation can support human decision-making.

Research into human interaction with automation and human–AI interface design provides relevant foundations for examining decision authority and oversight (Parasuraman et al., 2000); (Amershi et al., 2019).

However, commercial authority should remain consistent with the permissions granted and the nature of the decision.

A user may authorise an agent to research providers without authorising it to contact them.

The user may permit quotation requests while retaining approval over contractual commitments.

In regulated or consequential sectors, additional professional or organisational oversight may be required.

The distinction between recommendation and execution is therefore fundamental.

5.18 Recommendation Errors

Agent recommendations may contain different types of error.

These errors should be classified separately when conducting research.

5.19 Measuring Recommendation Visibility

The proposed model creates several opportunities for observational measurement.

However, measurement should reflect the information actually available to researchers.

Where intermediate agent actions cannot be observed, researchers should avoid inferring internal evaluation stages from the final recommendation alone.

Agent evaluation research provides relevant foundations for defining observable tasks and performance outcomes (Zhou et al., 2024); (Liu et al., 2024).

5.20 Measuring Recommendation Quality

Recommendation visibility and recommendation quality are different concepts.

A provider may be recommended frequently even when some recommendations are poorly supported.

Conversely, an organisation may receive fewer recommendations that are closely aligned with relevant customer requirements.

Researchers should therefore avoid using recommendation frequency as a universal quality measure.

Recommendation quality may require independent assessment of eligibility, requirement alignment, evidence accuracy and explanation adequacy.

5.21 Repeated Testing and Recommendation Stability

AI-generated recommendations may vary between repeated evaluations.

This variation may arise from differences in retrieved information, model behaviour, task interpretation or platform configuration.

A single recommendation observation may therefore provide limited evidence about the consistency of an organisation's visibility.

Repeated testing can help describe observed variation.

However, repeated observations are not necessarily statistically independent.

Research design should account for the relationship between repeated tasks and observations.

5.22 Prompt and Task Design

Recommendation research depends heavily on the commercial requirements used during testing.

A task asking for the cheapest provider may produce different results from a task prioritising technical capability.

A geographically restricted task may exclude organisations that would otherwise be suitable.

Similarly, a task specifying a particular integration may change the eligible candidate population.

Researchers should therefore define task categories before collecting observations.

Relevant categories may include:

• General provider discovery.
• Specialist capability requirements.
• Geographic provider selection.
• Commercial comparison.
• Technical compatibility.
• Regulatory eligibility.
• Budget-constrained selection.
• Multi-criteria procurement.

5.23 Branded and Unbranded Tasks

Research tasks that explicitly name an organisation differ from tasks that ask an agent to discover suitable providers without naming a preferred candidate.

A branded task may measure how an agent evaluates a specified organisation.

An unbranded task may provide evidence about candidate discovery and recommendation visibility.

These task types should not be combined without clear methodological justification.

The distinction is important because including an organisation's name within the prompt can directly influence its appearance in the response.

5.24 Commercial Relevance and Recommendation Value

Not every recommendation has the same commercial relevance.

An organisation may be recommended for a general informational task that does not involve an immediate purchasing requirement.

Another recommendation may occur during a specific provider-selection task with defined commercial requirements.

These situations should be classified separately.

However, a commercially relevant recommendation does not independently establish a completed transaction.

Recommendation value should therefore be distinguished from attributable revenue.

5.25 The Commercial Recommendation Funnel

The proposed model can be represented as a sequence of commercial discovery outcomes.

These stages are analytically distinct.

Researchers should not assume that every stage is observable or that all workflows follow the same sequence.

5.26 Implications for Organisations

The proposed model suggests that organisational visibility may depend on several different evaluation outcomes.

An organisation may be discovered but lack evidence supporting a mandatory requirement.

It may be eligible but perform less favourably against the user's preferences.

It may reach a shortlist but not receive an explicit recommendation.

Alternatively, it may be recommended without generating an identifiable commercial enquiry.

These outcomes should be investigated separately.

Organisations should avoid interpreting recommendation visibility as guaranteed commercial performance.

5.27 Implications for SEO and GEO

SEO and GEO research may need to examine not only whether an organisation is mentioned but also how it is evaluated within agent-mediated commercial tasks.

Relevant research questions include whether the organisation is correctly identified, whether its capabilities are accurately represented and whether supporting evidence is retrieved.

Further questions concern eligibility classification, comparative evaluation and recommendation consistency.

However, no single optimisation technique should be assumed to guarantee provider recommendation.

The relationship between organisational information and agent selection requires controlled empirical investigation.

5.28 Research Design for Empirical Validation

Future research could investigate agent recommendations through structured commercial evaluation tasks.

A study might define a set of hypothetical or real-world provider requirements with independently established eligibility criteria.

Researchers could then evaluate whether agents correctly identify eligible candidates and construct recommendations supported by the available evidence.

The research could compare results across platforms, task types and observation periods.

However, such studies would require careful control of information sources, candidate populations and evaluation conditions.

They would also require explicit recognition of the limitations associated with proprietary agent systems.

5.29 Limitations of Recommendation Research

Several limitations must be considered when investigating agent recommendation behaviour.

First, researchers may not observe every intermediate evaluation step.

Second, recommendation criteria may not be fully disclosed by the system.

Third, candidate populations may be incomplete.

Fourth, recommendations may vary between repeated observations.

Fifth, commercial requirements may be ambiguous or inconsistently interpreted.

Sixth, evidence availability may differ between candidate organisations.

Finally, recommendation visibility does not establish customer acceptance or commercial causation.

Section 5 — Research Interpretation

Agent-mediated provider selection should be examined as a multi-stage evaluation process.

Candidate discovery, eligibility assessment, comparative evaluation, shortlist inclusion and recommendation represent different outcomes.

A credible recommendation should reflect the user's requirements, appropriate supporting evidence and the limitations of the available information.

However, the presence of a recommendation does not establish that the user accepted it or that a commercial transaction occurred.

The proposed CGO Media Agent Recommendation and Provider Selection Model provides a structured basis for investigating these distinctions.

Supporting CGO Media Research

• AI Recommendation Authority in Generative Search
• Entity Authority in AI Search
• AI Source Selection in Generative Search
• AI Citation Selection in Generative Search
• Brand Authority Signals in AI Search
• CGO Media Framework Library
• CGO Media Research Methodology

Section 5 — Key Finding

6. Commercial Transactions and Agent-Mediated Purchasing

The development of AI agents introduces the possibility that software systems may undertake commercial activities extending beyond information retrieval and provider evaluation.

An agent may be capable of identifying suitable products, comparing suppliers, requesting information, preparing purchase instructions or interacting with external transaction systems.

Research into tool-using language models and autonomous agents provides a foundation for examining how AI systems may interact with external applications and undertake multi-stage tasks (Schick et al., 2023); (Yao et al., 2023); (Wang et al., 2024).

In some environments, agents may also be able to initiate authorised commercial actions through connected applications, web interfaces or application programming interfaces.

However, the technical ability to perform an action does not establish that the action is authorised, legally effective or commercially appropriate.

The transition from recommendation to transaction introduces additional requirements concerning permissions, security, contractual responsibility, financial controls and transaction verification.

These requirements are particularly important where an action may create financial obligations, disclose personal information or affect an organisation's legal position.

Research on human interaction with automation provides a relevant foundation for examining the allocation of decision authority between automated systems and human operators (Parasuraman et al., 2000).

This section develops a structured analytical model for examining the relationship between AI agents, commercial transactions and human decision authority.

The proposed framework is conceptual and methodological. It does not claim that fully autonomous purchasing is universally available, reliable or widely adopted.

6.1 From Commercial Discovery to Commercial Action

Commercial discovery and commercial execution are different activities.

An agent may investigate available products without purchasing anything.

It may compare service providers without contacting them.

It may prepare a quotation request without being authorised to submit it.

Similarly, it may identify a suitable purchase option without possessing permission to commit funds.

These distinctions are important because commercial actions can create consequences beyond the research environment.

A provider recommendation may influence a user's decision.

A submitted enquiry may disclose commercial or personal information.

A confirmed booking may create cancellation obligations.

An accepted quotation may, depending on its terms and the applicable law, contribute to a binding agreement.

A completed payment may transfer financial value.

Each activity therefore requires a different level of evaluation, authorisation and oversight.

The distinction between levels of automation and human control is consistent with established research into human interaction with automated systems (Parasuraman et al., 2000).

6.2 The CGO Media Agent-Mediated Commercial Transaction Model

CGO Media proposes the Agent-Mediated Commercial Transaction Model as a framework for examining how agent-supported commercial workflows may progress from research to execution.

The model contains nine analytical stages.

These stages describe different responsibilities and control requirements.

They are not intended to represent a mandatory sequence followed by every agent or commercial platform.

The model draws conceptually on research concerning autonomous agents, tool use, human oversight and AI risk management (Wang et al., 2024); (Parasuraman et al., 2000); (NIST, 2023).

However, the nine-stage structure is an original CGO Media analytical contribution and has not been independently validated as a universal commercial transaction model.

6.3 Commercial Actions Require Different Permission Levels

Not all commercial actions carry the same level of risk.

Reading a product description is different from submitting a purchase order.

Preparing an enquiry is different from sending that enquiry.

Comparing quotations is different from accepting contractual terms.

Research into levels of automation supports distinguishing between information acquisition, analysis, decision selection and action implementation (Parasuraman et al., 2000).

The proposed framework therefore distinguishes five categories of agent action.

6.4 Defining the Scope of Agent Authority

An AI agent should not be assumed to possess unrestricted authority simply because it has access to a commercial application.

Authority may be limited by the user's instructions, application permissions, organisational policies and applicable law.

For example, an organisation may permit an agent to obtain supplier quotations but prohibit it from accepting those quotations.

A user may authorise a booking search without authorising a confirmed reservation.

A procurement system may allow preparation of purchase orders while requiring separate financial approval.

The boundaries of authority should therefore be defined explicitly.

Human–AI interaction research supports the importance of communicating system capabilities, limitations and opportunities for human control (Amershi et al., 2019).

6.4.1 Financial Limits

A delegated commercial task may include a maximum permitted expenditure.

However, a spending limit does not independently authorise every purchase below that amount.

Other conditions may include approved suppliers, permitted product categories and specific transaction purposes.

6.4.2 Supplier Restrictions

An organisation may maintain approved supplier lists or procurement requirements.

An agent should not assume that an otherwise suitable provider is authorised for commercial engagement.

6.4.3 Geographic and Regulatory Restrictions

Commercial transactions may be subject to geographic, regulatory or contractual limitations.

These conditions may affect whether a particular transaction can lawfully or practically be completed.

6.4.4 Approval Requirements

Some actions may require explicit approval from a user, manager or other authorised person.

The approval should relate to the actual proposed action, including material commercial terms.

A general instruction to research providers should not be interpreted as unlimited permission to enter into agreements.

6.5 The Difference Between User Intent and Transaction Consent

A user's commercial objective does not necessarily establish consent to every action that might advance it.

For example, asking an agent to find a suitable hotel does not automatically authorise the agent to make a non-refundable booking.

Similarly, asking for the best payment provider does not necessarily authorise submission of a merchant application containing personal or financial information.

The agent should distinguish the intended outcome from the actions the user has permitted.

Research into human–AI interaction provides a foundation for designing systems that make consequential actions understandable and controllable (Amershi et al., 2019).

6.6 Commercial Offer Verification

Before a transaction is initiated, the relevant commercial terms may need to be verified.

An AI agent may have previously retrieved a product price or service description.

However, commercial conditions can change.

A product may become unavailable.

A quotation may expire.

Delivery costs may depend on location.

Taxes and fees may alter the final payable amount.

A booking may involve cancellation restrictions.

Consequently, information used during initial research should not automatically be treated as a confirmed transaction offer.

Research into information quality identifies timeliness, accuracy and completeness as relevant considerations when evaluating information for a particular purpose (Wang and Strong, 1996).

6.6.1 Price Verification

A published price may be indicative rather than final.

The agent may need to establish whether additional charges, taxes or conditions apply.

6.6.2 Availability Verification

Availability can change between initial discovery and transaction execution.

An agent should not assume that an earlier availability observation remains valid.

6.6.3 Seller Identity

The organisation presenting a product may differ from the legal entity responsible for the sale.

This distinction may matter for contractual responsibility, consumer protection and transaction verification.

6.6.4 Terms and Conditions

Material contractual terms may require review before a commercial commitment.

An agent should not assume that a summary captures every relevant condition.

6.7 APIs and Agent-Mediated Commerce

Application programming interfaces can allow software systems to interact with commercial services through structured requests.

An authorised API may support activities such as retrieving product information, checking availability, creating draft orders or submitting transactions.

Research into tool-using language models provides a foundation for examining how language models may interact with external software capabilities (Schick et al., 2023).

However, API capabilities vary between providers.

Some interfaces support read-only information access.

Others permit actions that change records or initiate payments.

The availability of a technical endpoint does not independently establish that an agent is authorised to use it.

Appropriate authentication, access controls and transaction safeguards remain necessary.

6.8 Authentication and Identity

Commercial applications may need to establish which user or organisation is requesting an action.

Authentication provides a mechanism for verifying the identity associated with an interaction.

However, authentication and authorisation are different concepts.

A system may authenticate a user successfully while still restricting which actions that user can perform.

Similarly, an agent acting through a connected application may possess access to some functions but not others.

The relevant permission should be checked before performing an externally consequential action.

The distinction between identity verification, authentication and access control is consistent with established digital identity guidance (NIST, 2017).

6.9 Payment Authorisation and Transaction Execution

Payments introduce additional financial and security considerations.

An agent may assist with preparing payment information or navigating a purchasing workflow.

However, payment initiation and payment completion are distinct events.

A submitted payment request may be declined, require additional authentication or remain pending.

Similarly, a payment authorisation may not represent final settlement.

The relevant transaction status depends on the payment system and the workflow involved.

Research into agent-mediated payments should therefore distinguish attempted, authorised, completed and failed transactions.

The Payment Card Industry Data Security Standard provides a relevant security framework for environments handling payment account data, although it does not independently define the proposed CGO Media transaction classifications (PCI SSC, 2024).

6.10 Agent-Mediated Procurement

Business-to-business procurement can involve complex commercial workflows.

A purchasing organisation may require supplier approval, budget allocation, quotation comparison and internal authorisation.

An AI agent may be capable of supporting some of these activities.

For example, it may retrieve supplier documentation, compare technical specifications or prepare a procurement summary.

However, procurement decisions may require professional judgement, organisational approval and contractual review.

The presence of an agent-generated recommendation does not remove these requirements.

Research into human interaction with automation provides a foundation for distinguishing automated decision support from delegated decision authority (Parasuraman et al., 2000).

6.10.1 Supplier Discovery

Potential suppliers are identified according to relevant requirements.

6.10.2 Supplier Qualification

The organisation evaluates whether suppliers meet mandatory conditions.

6.10.3 Quotation Comparison

Commercial offers are compared using defined criteria.

6.10.4 Approval

The proposed procurement action is reviewed by an authorised decision-maker.

6.10.5 Order Execution

An approved order may be submitted through the relevant procurement system.

6.10.6 Fulfilment Verification

Delivery, performance or service completion is checked according to the transaction terms.

6.11 Agent-Mediated Bookings and Reservations

Bookings provide another potential application of agent-mediated commercial workflows.

An agent may search for available services, compare options and prepare a booking request.

However, availability may change before the reservation is confirmed.

A booking may also involve cancellation conditions, deposits or other obligations.

The agent should therefore distinguish availability discovery from confirmed reservation.

A booking request should not be reported as successful unless the relevant provider has confirmed the reservation.

This distinction reflects the wider requirement for AI systems to communicate action outcomes accurately rather than treating an attempted action as a completed result (Amershi et al., 2019).

6.12 Agent-Mediated Enquiries and Lead Generation

Commercial enquiries may represent an intermediate outcome between provider recommendation and completed transaction.

An agent may prepare a request for information or quotation.

Where authorised, it may submit that request to a provider.

However, enquiry submission does not establish that the provider received, accepted or responded to the request.

Similarly, an enquiry does not necessarily represent a qualified commercial opportunity.

Research should distinguish enquiry generation from sales qualification and completed commercial engagement.

6.13 Transaction Security

Agent-mediated transactions introduce security requirements associated with external tool use, authentication, financial information and commercial systems.

A system may encounter malicious or misleading information during the discovery process.

Retrieved content may attempt to influence the agent's actions.

External applications may also contain security vulnerabilities or configuration errors.

Research into indirect prompt injection demonstrates how externally retrieved content may be used to influence language-model-integrated applications (Greshake et al., 2023).

Consequently, transaction workflows should maintain appropriate separation between untrusted information and authorised instructions.

Security controls should reflect the consequences of the permitted actions.

The NIST AI Risk Management Framework provides a broader foundation for examining the governance and management of AI-related risks (NIST, 2023).

6.14 Prompt Injection and Commercial Manipulation

An AI agent may retrieve content from websites, documents or other external sources.

Some content may contain instructions attempting to redirect the agent's behaviour.

For example, retrieved information could attempt to induce an unauthorised purchase or redirect a transaction.

Such instructions should not be treated as legitimate authorisation merely because they appear within a retrieved source.

Commercial agents should maintain clear boundaries between source content, user instructions and system permissions.

These concerns are supported by research examining indirect prompt injection against language-model-integrated applications (Greshake et al., 2023).

6.15 Transaction Confirmation and Error Handling

Commercial systems may return different responses during transaction processing.

An action may complete successfully.

It may fail before submission.

It may be submitted but remain unconfirmed.

A communication failure may also prevent the agent from knowing whether the action completed.

This creates a risk of duplicate submissions.

Where supported, transaction systems may use unique identifiers and idempotency controls to reduce duplicate execution.

However, implementation depends on the capabilities of the relevant platform.

An agent should not assume that retrying a failed-looking request is always safe.

Research into reliable distributed systems provides a foundation for understanding transaction processing, system failures and the challenges associated with repeated operations (Kleppmann, 2017).

6.16 Contractual Responsibility

Commercial transactions may create contractual obligations.

The legal consequences depend on the parties involved, the transaction terms and the applicable legal framework.

The use of an AI agent does not automatically resolve questions of contractual authority or responsibility.

An organisation may need to determine who authorised the transaction, which legal entity is the contracting party and whether the action complied with internal approval requirements.

These questions require appropriate legal and organisational analysis.

This research does not provide a universal legal classification of agent-executed transactions.

AI governance frameworks provide useful foundations for evaluating organisational accountability, but they should not be interpreted as replacing applicable contract law (NIST, 2023); (ISO/IEC, 2023).

6.17 Consumer Protection and Commercial Transparency

Agent-mediated commercial activity may involve consumer protection requirements.

These requirements vary by jurisdiction and transaction type.

Relevant considerations may include accurate pricing, material terms, cancellation rights, seller identification and appropriate transaction disclosures.

An agent should not assume that a simplified purchase summary captures every material legal condition.

Commercial systems may need to present relevant information and obtain appropriate consent before execution.

Within the European Union, Directive 2011/83/EU provides an important legal reference concerning consumer information and certain consumer rights in distance contracts (European Union, 2011).

Its application to any particular agent-mediated transaction depends on the relevant facts and legal requirements.

6.18 Privacy and Personal Information

Commercial transactions may require personal or organisational information.

Examples include contact details, delivery addresses, billing information and transaction records.

An AI agent may be able to access such information through connected applications.

However, access does not establish permission to disclose the information to external parties.

Data processing should comply with applicable privacy requirements and the permissions granted.

The principle of data minimisation may be relevant when determining which information is necessary for a particular transaction.

For personal data within the scope of the General Data Protection Regulation, Article 5 establishes data protection principles including data minimisation, accuracy and integrity and confidentiality (European Union, 2016).

6.19 Financial Fraud and Transaction Risk

Agent-mediated commercial systems may encounter fraudulent providers, misleading offers or unauthorised transaction requests.

A convincing website or structured API response does not independently establish that a seller is legitimate.

Similarly, a provider recommendation does not guarantee the security of a proposed transaction.

Commercial workflows may require appropriate seller verification, payment security controls and transaction monitoring.

The safeguards should reflect the relevant risk and transaction context.

AI risk-management guidance and established payment security standards provide relevant foundations for evaluating these risks (NIST, 2023); (PCI SSC, 2024).

6.20 Human Oversight and Approval Design

Human oversight can be introduced at different stages of a commercial workflow.

An agent may request approval before contacting an external provider.

It may require confirmation before submitting a purchase.

Additional approval may be necessary when the transaction exceeds a financial threshold or involves material contractual terms.

However, approval mechanisms should present sufficient information for the user to understand the proposed action.

A confirmation request that omits important commercial conditions may not provide an adequate basis for informed decision-making.

Research into human–AI interaction and levels of automation provides a foundation for examining how approval mechanisms should support effective human control (Amershi et al., 2019); (Parasuraman et al., 2000).

6.21 Commercial Transaction Evidence

Research into agent-mediated commerce requires evidence of what actually occurred.

An agent may report that it completed a purchase without the researcher having independent confirmation.

Similarly, a submitted API request may not establish that the relevant commercial system accepted the transaction.

Researchers should therefore distinguish agent-generated statements from external transaction records.

Appropriate evidence may include system confirmations, transaction identifiers, provider acknowledgements or other authoritative records.

Information provenance research provides a relevant foundation for documenting the relationship between claims, activities and supporting evidence (W3C, 2013).

6.22 Proposed Commercial Transaction Metrics

The Agent-Mediated Commercial Transaction Model creates opportunities for stage-specific measurement.

These metrics are proposed research constructs.

They require documented eligibility criteria and reliable observation records.

Agent evaluation research provides relevant methodological foundations for measuring task performance against predefined conditions (Zhou et al., 2024); (Liu et al., 2024).

However, these studies do not independently validate the following CGO Media commercial transaction metrics.

6.23 Commercial Attribution and Agent-Mediated Transactions

An agent-mediated transaction may provide identifiable evidence of commercial activity.

However, the existence of a transaction does not necessarily establish which earlier discovery interactions caused it.

A customer may have encountered the provider through several channels.

An AI agent may have used multiple information sources during evaluation.

The final transaction record may identify the seller without identifying every contributing discovery source.

Commercial attribution should therefore be distinguished from transaction execution.

Research into causal inference provides a methodological foundation for distinguishing observed outcomes from causal effects (Pearl, 2009).

The research principles developed in the CGO Media AI Search Measurement and Attribution framework remain relevant.

6.24 Measuring Commercial Incrementality

A transaction completed through an AI agent does not independently establish that the agent generated additional commercial demand.

The customer may have completed the same transaction through another channel.

The agent may have changed the process without changing the final purchasing decision.

Consequently, incremental commercial value requires a suitable counterfactual evaluation.

Counterfactual reasoning and causal inference provide established methodological foundations for examining whether an intervention changes an observed outcome (Pearl, 2009).

Simple counts of agent-mediated transactions should not be presented as proof of incremental revenue.

6.25 Sector Differences in Agent-Mediated Commerce

The commercial significance of agent actions varies between industries.

An agent purchasing a standardised product may face different requirements from an agent assisting with professional service procurement.

Healthcare, financial services and legal activities may involve additional regulatory or professional requirements.

Manufacturing procurement may involve technical specifications, certification and contractual negotiation.

Travel bookings may involve availability, cancellation terms and dynamic pricing.

SaaS procurement may require security assessment, integration review and contractual approval.

Consequently, a universal commercial transaction workflow should not be assumed to apply across every sector.

Risk-management approaches should reflect the context, consequences and intended use of the relevant AI system (NIST, 2023).

6.26 Implications for Businesses

Agent-mediated commerce may introduce additional ways for organisations to receive enquiries, quotation requests and transaction instructions.

However, businesses should distinguish technical integration from commercial readiness.

A transaction interface may require accurate product information, current pricing, appropriate authentication and reliable confirmation mechanisms.

It may also require fraud controls, privacy safeguards and clear contractual terms.

The commercial benefits of agent integration should be evaluated through observable outcomes rather than assumed from technical capability.

Information quality and AI risk-management research provide relevant foundations for examining these operational requirements (Wang and Strong, 1996); (NIST, 2023).

6.27 Implications for SEO and GEO

Agent-mediated commercial activity may extend the research agenda of SEO and GEO beyond visibility and recommendation.

Relevant questions include whether an organisation can be correctly identified, whether its commercial information can be retrieved and whether authorised systems can interact reliably with its services.

However, these considerations should not be confused with traditional search ranking factors.

Nor should the existence of an agent-accessible commercial interface be presented as a guarantee of recommendation or sales.

The relationship between technical accessibility, agent behaviour and commercial outcomes requires empirical investigation.

Research into tool use and autonomous agents provides a technical foundation for investigating these interactions, but does not establish universal commercial optimisation effects (Schick et al., 2023); (Wang et al., 2024).

6.28 Research Design for Empirical Validation

Future research could investigate agent-mediated commercial workflows through controlled test environments.

Researchers might define tasks involving quotation requests, draft orders or simulated bookings.

The research could specify permission boundaries, transaction conditions and expected outcomes.

Agents could then be evaluated against these predefined requirements.

Relevant observations might include whether the agent respected financial limits, requested approval when required and accurately reported transaction status.

Research benchmarks such as WebArena and AgentBench provide relevant examples of evaluating agent behaviour through defined tasks and observable outcomes (Zhou et al., 2024); (Liu et al., 2024).

However, these benchmarks do not independently validate the CGO Media Agent-Mediated Commercial Transaction Model.

Such studies should avoid unnecessary real-world financial commitments or disclosure of sensitive information.

Where possible, sandbox environments and controlled test accounts should be used.

Researchers should document the agent platform, model version, available tools, task instructions, permission boundaries, test dates and expected transaction outcomes.

Independent confirmation records should be retained where appropriate.

6.29 Limitations of Transaction Research

Several limitations must be considered.

First, agent capabilities vary between systems.

Second, external researchers may not have access to proprietary transaction workflows.

Third, commercial platforms use different authorisation and confirmation mechanisms.

Fourth, legal requirements vary between jurisdictions.

Fifth, test environments may not fully represent real-world commercial conditions.

Sixth, successful transaction execution does not establish commercial incrementality.

Finally, the reliability of agent-mediated transactions may change as underlying technologies and controls develop.

These limitations should be documented when interpreting empirical findings.

The distinction between observed performance and inferred internal system behaviour is particularly important when evaluating proprietary AI agents (Zhou et al., 2024).

Section 6 — Research Interpretation

Agent-mediated commercial activity extends beyond provider discovery and recommendation.

It introduces additional stages involving transaction preparation, authorisation, execution and confirmation.

These stages have different technical, commercial and legal consequences.

An agent may be capable of performing an action without possessing authority to perform it.

Similarly, a submitted action does not necessarily represent a completed transaction.

Commercial measurement should therefore distinguish attempted actions, authorised actions, confirmed transactions and incremental financial outcomes.

The proposed nine-stage CGO Media model provides a conceptual framework for examining these distinctions.

Its underlying principles are informed by research into autonomous agents, human oversight, system reliability, information provenance and causal inference (Wang et al., 2024); (Parasuraman et al., 2000); (Kleppmann, 2017); (Pearl, 2009).

The model itself remains an original CGO Media conceptual contribution requiring empirical validation.

Supporting CGO Media Research

• AI Recommendation Authority in Generative Search
• AI Search ROI Research UK 2026
• AI Search Conversion Research UK 2026
• AI Source Selection in Generative Search
• Entity Authority in AI Search
• CGO Media Framework Library
• CGO Media Research Methodology

Section 6 — Key Finding

7. Sector-Specific Implications of AI Agents and Autonomous Business Discovery

The development of AI agents may influence business discovery, provider evaluation and commercial transactions across a wide range of industries.

Research into autonomous agents and tool-using language models provides a technical foundation for investigating these possibilities (Wang et al., 2024); (Yao et al., 2023).

However, the significance of agent-mediated discovery depends on the characteristics of the sector, the complexity of the commercial requirement and the consequences of an incorrect decision.

A consumer purchasing a standardised retail product may require a different evaluation process from a business selecting a specialist manufacturing supplier.

Similarly, identifying a healthcare provider may involve professional credentials, clinical suitability and regulatory considerations that are not relevant to many routine purchases.

Financial services, legal services and other regulated activities may introduce additional requirements concerning authorisation, professional responsibility and consumer protection.

These differences are consistent with established approaches to risk management, which emphasise the importance of evaluating systems in relation to their intended use, operating context and potential consequences (NIST, 2023).

Consequently, a universal model of autonomous commercial decision-making would risk overlooking important sector-specific differences.

This section applies the analytical principles developed in Sections 1–6 to the twelve CGO Media research sectors.

The analysis is conceptual. It identifies plausible applications, research questions and limitations rather than claiming that autonomous agent adoption has been empirically established across these industries.

7.1 The CGO Media Sector-Specific Agent Discovery Assessment Model

CGO Media proposes a sector-specific assessment model containing eight analytical dimensions.

The model is designed to help researchers identify which characteristics of an industry may influence agent-mediated discovery, evaluation and transaction execution.

Its conceptual foundations include entity identification, information quality, multi-criteria decision analysis, human oversight and AI risk management (Hogan et al., 2021); (Wang and Strong, 1996); (Keeney and Raiffa, 1976); (NIST, 2023).

These publications support relevant analytical concepts but do not independently validate the eight-dimensional CGO Media model.

The eight dimensions are proposed for comparative research.

They do not constitute a validated industry scoring system.

The model should be applied using sector-specific evidence requirements and documented evaluation criteria.

7.2 Financial Services

7.2.1 Potential Applications

AI agents may assist users and organisations with researching financial products, identifying service providers and comparing published information.

Potential applications include investigating business banking services, payment infrastructure, insurance products and specialist financial providers.

An agent may retrieve information about eligibility requirements, product characteristics, published charges and available service channels.

However, financial products can involve complex contractual conditions, individual eligibility assessments and significant financial consequences.

An agent-generated comparison should therefore not be assumed to establish personal suitability or regulatory compliance.

AI risk-management principles support evaluating these applications according to their context, limitations and potential consequences (NIST, 2023).

7.2.2 Entity and Regulatory Verification

The evaluation may require verification of the provider's legal identity and applicable regulatory authorisation.

Requirements differ between financial activities and jurisdictions.

A provider's marketing description should not automatically be treated as evidence that it is authorised to perform every advertised activity.

Knowledge graph and provenance research provides relevant foundations for distinguishing organisational identity claims from independently traceable evidence (Hogan et al., 2021); (W3C, 2013).

7.2.3 Commercial Decision Boundaries

Researching financial products is different from opening accounts, accepting financial agreements or initiating regulated transactions.

Additional identity checks, customer disclosures, suitability assessments or authorisations may apply.

The distinction between decision support and delegated action is consistent with research into human interaction with automation (Parasuraman et al., 2000).

7.2.4 Research Priorities

• Accuracy of regulatory status identification.
• Completeness of product comparison evidence.
• Recognition of eligibility limitations.
• Handling of material financial risk disclosures.
• Compliance with transaction permission boundaries.
• Accuracy of commercial terms and charges.

Explore CGO Media Financial Services AI & GEO Research

7.3 Ecommerce and Retail

7.3.1 Potential Applications

Ecommerce and retail provide potential applications for agent-mediated product discovery, comparison and purchasing.

An agent may investigate product specifications, compare prices, check availability and identify retailers.

Where appropriate tools and permissions are available, it may also assist with preparing or executing purchases.

Research into tool-using language models provides a technical foundation for investigating interactions with external commercial systems (Schick et al., 2023).

However, product information can change rapidly.

Prices, stock availability, delivery charges and promotional conditions may differ between observation and checkout.

7.3.2 Product Identity

The agent may need to distinguish between product models, configurations, sizes, versions and variants.

An incorrect product match can make a price comparison misleading.

Product identity should therefore be verified before evaluating commercial alternatives.

Entity representation and information quality research provide relevant foundations for examining identity consistency and the accuracy of product attributes (Hogan et al., 2021); (Wang and Strong, 1996).

7.3.3 Transaction Considerations

The purchasing workflow may involve seller identification, shipping arrangements, payment authorisation and return conditions.

A product recommendation does not independently establish that the item remains available or that the transaction can be completed.

Agent evaluation should distinguish information retrieval, decision support and successful action execution (Parasuraman et al., 2000); (Zhou et al., 2024).

7.3.4 Research Priorities

• Product matching accuracy.
• Price and availability currency.
• Seller identity verification.
• Accuracy of total purchase cost.
• Compliance with purchasing permissions.
• Transaction confirmation reliability.

Explore CGO Media Ecommerce & Retail AI & GEO Research

7.4 Education and EdTech

7.4.1 Potential Applications

AI agents may assist students, families and organisations with researching educational institutions, courses, training providers and education technology products.

Potential tasks include comparing programme content, entry requirements, delivery methods, fees and recognised qualifications.

However, educational suitability depends on individual objectives, prior qualifications, learning requirements and institutional conditions.

These comparisons may involve multiple criteria and trade-offs, making multi-criteria decision analysis a relevant conceptual foundation (Keeney and Raiffa, 1976).

7.4.2 Accreditation and Qualification Evidence

Agents may need to distinguish between recognised qualifications, professional training, institutional accreditation and commercial certificates.

The meaning of accreditation varies between countries and educational systems.

A course provider's claim should be evaluated against appropriate evidence.

Information provenance provides a useful framework for recording the source and traceability of qualification and accreditation claims (W3C, 2013).

7.4.3 Educational Suitability

A programme may be legitimate without being suitable for a particular learner.

The evaluation may need to consider academic prerequisites, delivery format, accessibility requirements and intended outcomes.

Research into human–AI interaction supports the importance of communicating system limitations and supporting informed human decisions (Amershi et al., 2019).

7.4.4 Research Priorities

• Institution and course identity accuracy.
• Verification of accreditation claims.
• Interpretation of entry requirements.
• Recognition of programme limitations.
• Accuracy of fees and course availability.
• Transparency of recommendation criteria.

Explore CGO Media Education & EdTech AI & GEO Research

7.5 Healthcare

7.5.1 Potential Applications

AI agents may assist with administrative healthcare discovery, such as locating providers, checking published service information and identifying appointment channels.

However, healthcare decisions can involve significant risks.

Finding a healthcare provider is not equivalent to determining the appropriate diagnosis, treatment or clinical pathway.

Clinical decisions may require qualified professional judgement and access to relevant patient information.

Research into human oversight and AI risk management provides a foundation for distinguishing administrative assistance from consequential professional decision-making (Parasuraman et al., 2000); (NIST, 2023).

7.5.2 Professional and Institutional Verification

Provider evaluation may require verification of professional registration, clinical qualifications, institutional authorisation or specialist credentials.

The relevant requirements depend on the jurisdiction and the healthcare activity.

Information provenance principles are relevant when determining whether credential claims can be traced to appropriate supporting sources (W3C, 2013).

7.5.3 Patient Safety and Privacy

Healthcare workflows may involve sensitive personal information.

Agent access to such information requires appropriate authorisation, security and privacy safeguards.

An administrative booking capability should not be confused with clinical decision authority.

Where the General Data Protection Regulation applies, health information may constitute special-category personal data subject to additional requirements (European Union, 2016).

7.5.4 Research Priorities

• Accuracy of provider identity and credentials.
• Recognition of clinical decision boundaries.
• Accuracy of service availability information.
• Handling of sensitive personal information.
• Appropriate escalation to qualified professionals.
• Compliance with appointment and communication permissions.

Explore CGO Media Healthcare AI & GEO Research

7.6 International Organisations

7.6.1 Potential Applications

International organisations may use AI agents to investigate suppliers, research partners, technical service providers and operational resources.

Commercial discovery may involve multiple jurisdictions, languages and procurement systems.

An agent may need to compare organisations operating under different legal and regulatory frameworks.

Research into autonomous agents and information retrieval provides a foundation for investigating these multi-stage information-processing tasks (Wang et al., 2024); (Lewis et al., 2020).

7.6.2 Organisational Identity

International activities can involve complex relationships between headquarters, regional offices, implementing partners and contracted suppliers.

Entity resolution is therefore an important consideration.

Knowledge graph research provides a foundation for representing organisational relationships and distinguishing connected but separate entities (Hogan et al., 2021).

7.6.3 Procurement and Accountability

Procurement processes may involve formal eligibility criteria, competitive procedures, documentation requirements and institutional approval.

An agent may assist with administrative research without possessing authority to make procurement decisions.

Research into levels of automation and AI governance provides a foundation for evaluating the distinction between automated assistance and institutional decision authority (Parasuraman et al., 2000); (ISO/IEC, 2023).

7.6.4 Research Priorities

• Cross-border entity identification.
• Multilingual information retrieval accuracy.
• Supplier eligibility verification.
• Consistency of procurement criteria.
• Documentation of evaluation evidence.
• Compliance with institutional approval boundaries.

Explore CGO Media International Organisations AI & GEO Research

7.7 Legal Services

7.7.1 Potential Applications

AI agents may assist individuals and businesses with researching legal service providers, identifying practice areas and locating published professional information.

However, selecting a legal adviser may require consideration of jurisdiction, professional authorisation, specialist expertise and the nature of the legal matter.

An agent-generated provider comparison should not be confused with legal advice.

AI risk-management principles support evaluating such systems in relation to their intended use and the consequences of inappropriate reliance (NIST, 2023).

7.7.2 Professional Verification

The evaluation may require verification of professional registration or authorisation.

Requirements vary between jurisdictions and legal professions.

A provider's claimed expertise should be distinguished from independently verifiable professional status.

Information provenance provides a relevant foundation for distinguishing unsupported claims from traceable evidence (W3C, 2013).

7.7.3 Confidentiality and Decision Authority

Legal enquiries may involve confidential or sensitive information.

An agent should not assume that permission to research legal providers includes permission to disclose details of a legal matter.

Engagement terms and professional obligations may require direct human consideration.

The distinction between information access and permission to perform consequential actions is consistent with research into human control of automation (Parasuraman et al., 2000).

7.7.4 Research Priorities

• Jurisdictional suitability.
• Professional registration accuracy.
• Practice-area classification.
• Recognition of conflicts and limitations.
• Confidentiality and information-sharing controls.
• Distinction between provider discovery and legal advice.

Explore CGO Media Legal AI & GEO Research

7.8 Manufacturing

7.8.1 Potential Applications

Manufacturing provides potential applications for agent-mediated supplier discovery, technical comparison and procurement research.

An agent may investigate production capabilities, materials, certifications, geographic coverage and supplier documentation.

However, manufacturing requirements can be highly technical.

An apparently suitable supplier may lack a necessary production process, tolerance, certification or capacity.

Multi-criteria decision analysis provides a relevant foundation for examining technical, operational and commercial trade-offs (Keeney and Raiffa, 1976).

7.8.2 Technical Capability Evidence

Technical claims may require verification through specifications, certification, engineering documentation or direct supplier confirmation.

Marketing descriptions may not contain sufficient detail to establish technical compatibility.

Information quality and provenance research provide relevant foundations for evaluating whether retrieved technical evidence is sufficiently accurate, complete and traceable (Wang and Strong, 1996); (W3C, 2013).

7.8.3 Supplier Qualification

Supplier qualification may involve quality requirements, production capacity, delivery conditions and contractual obligations.

These considerations may require specialist professional review.

Human–AI interaction research supports designing systems that communicate limitations and allow appropriate human intervention (Amershi et al., 2019).

7.8.4 Research Priorities

• Accuracy of technical capability matching.
• Verification of certification claims.
• Recognition of production constraints.
• Assessment of supplier identity.
• Handling of incomplete technical specifications.
• Accuracy of quotation and procurement information.

Explore CGO Media Manufacturing AI & GEO Research

7.9 Professional Services

7.9.1 Potential Applications

Professional services encompass a broad range of specialist business activities.

Potential agent applications include researching consultants, accounting providers, business advisers and other specialist firms.

However, service quality can be difficult to evaluate through standardised product specifications.

The suitability of a provider may depend on expertise, sector experience, delivery approach and the specific client requirement.

These comparisons may involve multiple objectives and context-dependent trade-offs (Keeney and Raiffa, 1976).

7.9.2 Evidence of Expertise

An agent may retrieve professional biographies, published research, case studies and service descriptions.

However, the presence of extensive promotional content does not independently establish professional competence.

Evidence should be evaluated according to relevance, provenance and verifiability.

Research into information provenance and documentation provides relevant foundations for distinguishing claims from their supporting sources (W3C, 2013); (Gebru et al., 2021).

7.9.3 Commercial Engagement

Professional services may require consultation and scoping before accurate pricing or suitability can be established.

An agent-generated shortlist may therefore be more appropriate than an immediate transaction recommendation.

Research into human–AI interaction supports the importance of communicating uncertainty and preserving appropriate human decision-making (Amershi et al., 2019).

7.9.4 Research Priorities

• Accuracy of specialist expertise classification.
• Relevance of published experience.
• Verification of professional credentials.
• Handling of confidential case-study limitations.
• Recognition of project-specific suitability.
• Quality of shortlist explanations.

Explore CGO Media Professional Services AI & GEO Research

7.10 Property and Real Estate

7.10.1 Potential Applications

AI agents may assist with property discovery, agency research, commercial property comparisons and related service-provider selection.

Potential tasks include identifying properties that satisfy defined requirements, comparing published information and arranging authorised enquiries.

However, property information can be time-sensitive.

Availability, asking prices, property condition and transaction terms may change.

Information quality research provides a foundation for examining the importance of currency, accuracy and completeness in these comparisons (Wang and Strong, 1996).

7.10.2 Property and Provider Identity

An agent may need to distinguish between a property, its owner, the marketing agency and the organisation responsible for a transaction.

These entities should not be assumed to be identical.

Knowledge graph research provides a relevant foundation for representing distinct entities and their relationships (Hogan et al., 2021).

7.10.3 Commercial and Legal Constraints

Property transactions may involve substantial financial commitments, contractual obligations and jurisdiction-specific procedures.

Agent-supported discovery does not eliminate the need for appropriate verification and professional review.

Research into levels of automation provides a foundation for distinguishing research assistance from authorised commercial action (Parasuraman et al., 2000).

7.10.4 Research Priorities

• Accuracy of property matching.
• Information currency and availability.
• Identification of relevant commercial parties.
• Recognition of property-specific limitations.
• Accuracy of location and specification data.
• Compliance with enquiry and booking permissions.

Explore CGO Media Property & Real Estate AI & GEO Research

7.11 SaaS

7.11.1 Potential Applications

Software-as-a-Service markets provide potential applications for agent-mediated software discovery, technical comparison and procurement research.

An agent may investigate product functionality, integration capabilities, security documentation, pricing and implementation requirements.

However, software suitability often depends on the customer's existing systems and operational requirements.

Multi-criteria decision analysis provides a relevant conceptual foundation for comparing technical and commercial requirements (Keeney and Raiffa, 1976).

7.11.2 Technical Compatibility

A product may provide the required general functionality while lacking a specific integration or deployment option.

Technical compatibility may require detailed documentation, testing or vendor confirmation.

Information quality research supports examining whether available documentation is sufficiently complete and appropriate for the decision being made (Wang and Strong, 1996).

7.11.3 Commercial and Security Evaluation

SaaS procurement may involve subscription conditions, data processing arrangements, security requirements and contractual commitments.

An agent may assist with information retrieval without replacing appropriate technical, security or legal review.

AI governance and risk-management frameworks provide relevant foundations for assessing organisational controls and the consequences of system use (NIST, 2023); (ISO/IEC, 2023).

7.11.4 Research Priorities

• Feature and functionality matching accuracy.
• Integration requirement verification.
• Security documentation interpretation.
• Subscription pricing accuracy.
• Recognition of contractual limitations.
• Consistency of software recommendations.

Explore CGO Media SaaS AI & GEO Research

7.12 Technology

7.12.1 Potential Applications

Technology markets include hardware, software, infrastructure, technical services and specialist systems.

AI agents may assist with provider discovery, technical specification comparison and solution evaluation.

However, technology requirements may involve complex dependencies between products, systems and implementation environments.

Research into autonomous agents provides a technical foundation for investigating multi-stage evaluation tasks, although it does not establish the reliability of any particular technology procurement workflow (Wang et al., 2024).

7.12.2 Technical Evidence

A provider's claimed capabilities may need to be assessed against technical documentation, compatibility requirements and relevant standards.

General marketing descriptions may not provide sufficient evidence for specialist technical decisions.

Information provenance and quality research provides a foundation for evaluating the source, completeness and reliability of technical claims (W3C, 2013); (Wang and Strong, 1996).

7.12.3 Implementation Risk

An apparently suitable solution may introduce integration, security, maintenance or operational risks.

Agent-generated recommendations may therefore require technical review before commercial commitment.

Risk-management frameworks emphasise evaluating the intended use and potential consequences of system decisions (NIST, 2023).

7.12.4 Research Priorities

• Technical requirement interpretation.
• Product and provider identity accuracy.
• Compatibility evidence retrieval.
• Assessment of implementation constraints.
• Recognition of security and operational risks.
• Quality of comparative recommendation explanations.

Explore CGO Media Technology AI & GEO Research

7.13 Travel and Hospitality

7.13.1 Potential Applications

Travel and hospitality provide potential applications for agent-mediated destination research, accommodation discovery, itinerary planning and authorised bookings.

An agent may compare locations, facilities, availability, prices and booking conditions.

However, travel information can change rapidly.

Prices and availability may differ between initial research and final booking.

Information quality research provides a foundation for examining the importance of current and accurate information in time-sensitive decisions (Wang and Strong, 1996).

7.13.2 Provider and Offer Verification

The agent may need to distinguish between accommodation providers, booking intermediaries and the organisations responsible for specific commercial terms.

The identity of the seller may affect payment, cancellation and customer service arrangements.

Entity representation and information provenance provide relevant foundations for identifying distinct commercial parties and tracing the origin of offer information (Hogan et al., 2021); (W3C, 2013).

7.13.3 Booking Authority

A request to find accommodation does not automatically authorise a confirmed reservation.

The agent may need to obtain approval for material booking conditions, including deposits, cancellation restrictions and final costs.

Research into human oversight supports distinguishing information retrieval and decision support from authorised action implementation (Parasuraman et al., 2000).

7.13.4 Research Priorities

• Accuracy of accommodation and service matching.
• Current availability verification.
• Total booking cost accuracy.
• Interpretation of cancellation conditions.
• Provider and intermediary identification.
• Confirmed reservation status accuracy.

Explore CGO Media Travel & Hospitality AI & GEO Research

7.14 Comparing Agent Discovery Requirements Across Sectors

The twelve sectors illustrate why agent-mediated discovery should not be evaluated through a single universal commercial workflow.

Some sectors involve relatively standardised product information.

Others depend on professional judgement, technical specifications or individual circumstances.

The consequences of incorrect recommendations also differ.

A routine product comparison may present different risks from a healthcare, financial or legal services decision.

Similarly, an agent preparing a supplier shortlist may require different permissions from an agent authorised to submit a purchase order.

Sector-specific research should therefore distinguish the nature of the task from the general capabilities of the AI agent.

This approach is consistent with risk-management principles that evaluate the context and consequences of system use rather than assuming uniform risk across applications (NIST, 2023).

7.15 Sector-Specific Discovery Failure Points

The Autonomous Business Discovery Model developed in Section 3 identifies several stages at which an organisation may be excluded from an agent's evaluation.

However, the reasons for exclusion may differ between sectors.

A financial provider may be excluded because its regulatory status cannot be verified.

A manufacturer may be excluded because a required technical capability is not documented.

A SaaS provider may be excluded because a necessary integration cannot be confirmed.

A travel provider may be excluded because suitable availability cannot be established.

These outcomes should not be interpreted as equivalent forms of poor organisational quality.

They may reflect genuine ineligibility, incomplete information, retrieval limitations or agent evaluation errors.

Research into retrieval systems and agent evaluation provides relevant foundations for distinguishing information access problems from task-performance failures (Lewis et al., 2020); (Zhou et al., 2024).

Researchers should attempt to distinguish these explanations where supporting evidence permits.

7.16 Sector-Specific Recommendation Measurement

Recommendation visibility may be measured across different sectors, but interpretation requires consistent task definitions.

A general recommendation task in ecommerce is not directly equivalent to a specialist legal services procurement task.

The eligible candidate population, evidence requirements and decision consequences differ.

Researchers should therefore avoid combining unrelated sector observations into a single performance measure without appropriate methodological justification.

Sector-specific denominators should be defined according to the relevant commercial tasks.

The same organisation may also be eligible for some tasks within a sector but not others.

Research into classification evaluation provides a methodological foundation for examining how definitions, reference classifications and measurement choices affect reported performance (Sokolova and Lapalme, 2009).

7.17 Proposed Sector Research Metrics

The following metrics may support structured investigation across different industries.

They are proposed research constructs rather than validated universal performance indicators.

Agent evaluation research provides a foundation for defining measurable task outcomes, while classification research highlights the importance of clear reference standards and denominators (Liu et al., 2024); (Sokolova and Lapalme, 2009).

The following five metrics are original CGO Media proposals and should not be attributed to those publications.

7.18 Cross-Sector Research Design

Future research could investigate agent-mediated discovery across the twelve sectors using a common analytical structure.

However, each sector would require its own task definitions, eligibility criteria and evidence standards.

A cross-sector study might include the following stages.

1. Define the commercial discovery tasks relevant to each sector.
2. Establish mandatory eligibility requirements for each task.
3. Identify appropriate reference sources for factual verification.
4. Define observable discovery, evaluation and recommendation outcomes.
5. Document the AI agent, tools and platform conditions.
6. Conduct repeated observations using a predefined protocol.
7. Assess eligibility and recommendation accuracy against independent reference classifications.
8. Report results separately by sector and task category.

Agent benchmarks such as WebArena and AgentBench provide relevant methodological examples of evaluating agent behaviour through defined tasks and observable outcomes (Zhou et al., 2024); (Liu et al., 2024).

However, neither benchmark independently validates the proposed CGO Media cross-sector assessment framework.

Where cross-sector comparisons are attempted, researchers should disclose differences in task complexity and evidence availability.

Observed differences should not automatically be attributed to sector characteristics without appropriate controls.

7.19 High-Consequence and Lower-Consequence Tasks

The consequences of an incorrect agent action depend on the task.

A routine product search may involve limited commercial consequences.

A financial commitment, healthcare-related decision or legal engagement may involve substantially greater risks.

However, risk should not be assigned solely according to industry.

A high-value technology purchase may be more consequential than a routine administrative task in a regulated sector.

The relevant unit of analysis is therefore the specific decision or action, including its consequences and authorisation requirements.

This approach is consistent with AI risk-management principles and research into appropriate levels of human oversight (NIST, 2023); (Parasuraman et al., 2000).

7.20 Implications for Organisational Visibility

Agent-mediated discovery may introduce additional points at which organisations are evaluated or excluded.

However, the commercially relevant evidence differs between industries.

A manufacturing organisation may need to demonstrate technical production capabilities.

A professional services provider may need to communicate specialist expertise and relevant experience.

A SaaS provider may need accessible documentation describing product functionality and integrations.

A financial services organisation may need accurate information concerning regulatory status and product eligibility.

These differences suggest that research into agent-mediated visibility should be organised around sector-specific requirements rather than generic visibility alone.

Information retrieval, knowledge representation and provenance research provide relevant foundations for investigating how organisational information may be accessed and interpreted (Lewis et al., 2020); (Hogan et al., 2021); (W3C, 2013).

Nevertheless, publishing additional information does not guarantee agent discovery, recommendation or commercial selection.

7.21 Implications for SEO and GEO

The sector analysis extends the research agenda of SEO and GEO towards the interpretation of organisational suitability within agent-mediated tasks.

Traditional search visibility may remain relevant to information discovery.

Generative answer visibility may remain relevant to source citation and recommendation.

Agent-mediated discovery introduces further questions about eligibility, evidence retrieval, comparative evaluation and authorised action.

However, these activities should not be assumed to share a single optimisation mechanism.

Different platforms may use different retrieval systems, tools and evaluation methods.

Research into retrieval-augmented generation, tool use and autonomous agents provides a technical foundation for investigating these differences (Lewis et al., 2020); (Schick et al., 2023); (Wang et al., 2024).

Research should therefore distinguish observed agent behaviour from proposed organisational practices.

7.22 Research Limitations

The sector-specific analysis has several limitations.

First, the applications described are potential use cases rather than evidence of widespread autonomous agent adoption.

Second, legal and regulatory requirements vary between jurisdictions.

Third, the information available to AI agents depends on their tools, permissions and retrieval capabilities.

Fourth, some commercial requirements cannot be fully evaluated using public information.

Fifth, sector classifications contain considerable internal variation.

Sixth, commercial recommendation outcomes may depend on individual user preferences.

Finally, the proposed sector assessment model has not been empirically validated as a universal predictor of agent recommendation behaviour.

These limitations are consistent with the broader methodological need to distinguish observed agent performance from assumptions about general capability or reliability (Zhou et al., 2024); (Liu et al., 2024).

7.23 Future Sector Research Priorities

The proposed framework provides a foundation for future observational and experimental studies.

Priority research areas include the accuracy of provider identity resolution, eligibility assessment and recommendation formation.

Further studies could investigate whether the availability of sector-specific documentation affects evidence retrieval or evaluation accuracy.

Researchers could also examine how agents respond to incomplete or conflicting commercial information.

In transaction-capable environments, studies could investigate permission compliance, confirmation accuracy and the distinction between attempted and completed commercial actions.

Agent evaluation research provides a foundation for designing controlled tasks and recording observable performance outcomes (Zhou et al., 2024); (Liu et al., 2024).

Such research should report the conditions under which observations were collected.

It should also distinguish conceptual predictions from empirically established findings.

Section 7 — Research Interpretation

The twelve-sector analysis illustrates why agent-mediated business discovery should not be treated as a uniform process.

Different industries involve different information requirements, verification methods, commercial constraints and decision consequences.

The suitability of an organisation depends on the requirements of the specific task.

Consequently, a provider may be appropriate for one commercial objective but unsuitable for another.

Agent-mediated discovery research should therefore combine a common analytical structure with sector-specific eligibility and evidence requirements.

This approach may support more meaningful comparisons than undifferentiated measures of organisational recommendation frequency.

Multi-criteria decision analysis, classification evaluation and AI risk-management research provide relevant conceptual foundations for this approach (Keeney and Raiffa, 1976); (Sokolova and Lapalme, 2009); (NIST, 2023).

However, the effectiveness of the proposed CGO Media model requires empirical investigation.

Supporting CGO Media Research

The sector-specific analysis connects with the wider CGO Media research programme examining AI search, entity authority, provider selection and commercial discovery.

• CGO Media Research Library
• CGO Media Framework Library
• AI Recommendation Authority in Generative Search
• Entity Authority in AI Search
• AI Source Selection in Generative Search
• CGO Media Research Methodology

Section 7 — Key Finding

8. Conclusions, Research Methodology, Limitations and Future Research

The emergence of artificial intelligence agents introduces a potentially important development in the relationship between digital information discovery, organisational evaluation and commercial decision-making.

Traditional search systems have primarily been examined through the retrieval, ranking and presentation of information resources. Conversational AI systems introduce additional questions concerning answer construction, source selection, entity representation and recommendation formation.

Agent-mediated systems may extend these activities by performing sequences of research, comparison, planning and authorised external actions. Research into reasoning-and-acting systems, tool use and autonomous agents provides a technical foundation for investigating these possibilities (Yao et al., 2023); (Schick et al., 2023); (Wang et al., 2024).

However, the ability to perform multiple activities within a single workflow does not mean that those activities represent the same outcome.

An organisation may be discovered without being evaluated. It may be evaluated without being recommended. It may be recommended without being selected. And it may be selected without the resulting transaction being attributable to a particular AI interaction.

This paper argues that these distinctions are fundamental to the credible study of autonomous business discovery.

It proposes a conceptual research architecture for investigating how AI agents may identify organisations, interpret evidence, assess suitability, construct recommendations and participate in commercial workflows.

The paper does not claim to establish the prevalence of autonomous purchasing, the internal algorithms of commercial AI platforms or the causal impact of agent-mediated discovery on business revenue.

8.1 Principal Research Conclusions

The analysis developed across the preceding seven sections supports several conceptual conclusions.

8.2 The Proposed CGO Media Research Architecture

The paper develops five complementary original CGO Media conceptual models.

Together, these models describe different aspects of agent-mediated commercial discovery, organisational evidence, provider recommendation, transaction execution and sector-specific evaluation.

The models are presented in the order in which they are developed across Sections 3–7.

These five models should be interpreted as original analytical structures rather than verified descriptions of proprietary AI agent architectures.

They are intended to make research questions more explicit, distinguish observable outcomes and support future empirical investigation.

External academic literature provides relevant conceptual foundations but does not independently validate the CGO Media models.

8.3 An Integrated Agent-Mediated Discovery Process

The five models can be combined into a broader conceptual process.

The process is not necessarily linear. Agents may revisit earlier stages, request clarification or terminate a workflow when sufficient information is unavailable.

Research into reasoning-and-acting systems and autonomous agent architectures provides a technical foundation for examining multi-stage workflows involving information retrieval and external actions (Yao et al., 2023); (Wang et al., 2024).

Not every task requires all thirteen stages.

A research-only task may conclude after recommendation.

A transaction-capable workflow may require additional approval and verification.

Researchers should document which stages are observable rather than infer that every stage occurred.

The thirteen-stage process is an original CGO Media synthesis. It should not be interpreted as a standardised architecture used by all AI agents.

8.4 Research Objectives

The primary objective of this paper is to establish a conceptual foundation for investigating AI agents and autonomous business discovery.

The supporting objectives are:

1. Distinguish traditional information retrieval from agent-mediated research and commercial action.
2. Define observable stages of organisational discovery and provider evaluation.
3. Examine the relationship between entity identity, evidence retrieval and commercial suitability.
4. Develop classifications for recommendation and provider-selection outcomes.
5. Identify authorisation and transaction verification requirements.
6. Examine sector-specific differences in commercial discovery.
7. Propose research metrics for future empirical investigation.
8. Identify methodological limitations and priorities for validation.

8.5 Research Methodology

8.5.1 Research Classification

This publication is a conceptual and methodological research paper.

It synthesises established ideas from information retrieval, AI agent research, knowledge representation, decision-making, software security and commercial evaluation.

It then develops five proposed analytical models for investigating agent-mediated business discovery.

The paper does not report an original controlled experiment, a representative industry survey or a proprietary dataset of commercial AI agent behaviour.

8.5.2 Analytical Approach

The research uses conceptual decomposition to distinguish activities that are frequently discussed together under the broad description of autonomous AI agents.

These activities include information discovery, entity identification, evidence evaluation, recommendation, transaction preparation, authorisation and execution.

The purpose is to identify which outcomes can be defined and measured separately.

Multi-criteria decision analysis provides a relevant conceptual foundation for separating eligibility conditions, objectives and preferences (Keeney and Raiffa, 1976).

Research into automation provides a foundation for distinguishing decision support from action implementation (Parasuraman et al., 2000).

8.5.3 External Literature

External academic and technical sources are used to establish background for the research questions.

The literature covers:

• Reasoning and acting with language models.
• Language-model agent architectures.
• Retrieval-augmented generation.
• Knowledge graphs and entity resolution.
• Information quality and provenance.
• Multi-criteria decision-making.
• Human–AI interaction and oversight.
• Tool-use evaluation.
• Prompt injection and security risks.
• AI risk management and governance.
• Autonomous agent benchmarks.

These sources support the relevance of the research topics.

They should not be interpreted as empirical validation of the five CGO Media models.

The bibliography in Section 8.14 identifies the external publications and institutional materials referenced throughout the paper.

8.5.4 Framework Development

The five proposed models were developed by separating commercial discovery activities according to their informational, representational, evaluative, transactional and sector-specific functions.

The resulting dimensions are intended to support future coding protocols, observational studies and controlled evaluations.

The models remain subject to revision as empirical evidence becomes available.

They should not be treated as validated scoring systems or universally applicable descriptions of agent behaviour.

8.5.5 Sector Analysis

The twelve-sector analysis applies a common conceptual structure to different commercial environments.

It examines how sector characteristics may affect eligibility requirements, evidence standards, recommendation suitability and transaction permissions.

The analysis does not establish sector adoption rates or comparative commercial performance.

Risk-management principles provide a foundation for examining differences between intended uses and decision consequences (NIST, 2023).

8.6 Proposed Empirical Research Programme

The next stage of research should evaluate whether the proposed models provide useful and reproducible descriptions of observed agent behaviour.

CGO Media identifies six potential research programmes.

Interactive agent benchmarks provide methodological examples of evaluating systems against defined tasks and observable outcomes (Zhou et al., 2024); (Liu et al., 2024).

8.7 Recommended Observation Protocol

Future empirical studies should use a documented observation protocol.

The protocol should specify the research environment, task population, reference evidence and evaluation criteria.

Agent evaluation research illustrates the importance of recording task conditions and externally assessable outcomes (Zhou et al., 2024); (Liu et al., 2024).

The research record should distinguish direct observations from researcher interpretations.

Where intermediate agent actions are unavailable, that limitation should be disclosed.

Researchers should avoid interpreting a final answer as proof that every proposed intermediate stage occurred.

8.8 Measurement and Reporting Principles

The proposed research programme should follow several reporting principles.

These principles are consistent with broader research into classification evaluation, AI risk management and agent benchmarking (Sokolova and Lapalme, 2009); (NIST, 2023); (Liu et al., 2024).

8.9 Research Limitations

The present paper has important limitations.

8.9.1 Conceptual Rather Than Empirical Findings

The proposed models have not been validated through an original controlled study reported in this publication.

They should therefore be treated as hypotheses and analytical structures.

8.9.2 Proprietary Agent Systems

Commercial AI platforms may not disclose their complete internal retrieval, evaluation or recommendation processes.

External researchers may therefore observe outputs without access to every intermediate decision.

8.9.3 Platform Variation

Agent capabilities differ between platforms, models, tool configurations and deployment environments.

Findings from one system should not automatically be generalised to all agents.

8.9.4 Temporal Variation

AI systems and commercial information sources may change over time.

Observations require appropriate dates and configuration records.

8.9.5 Incomplete Commercial Information

Some provider requirements cannot be verified using public information alone.

Pricing, availability, capacity and contractual conditions may require direct confirmation.

8.9.6 Sector Heterogeneity

The twelve sectors contain substantial variation between organisations, services and task types.

Sector-wide conclusions require appropriately designed sampling.

8.9.7 Legal and Regulatory Variation

Authorisation, consumer protection, professional requirements and contractual rules differ by jurisdiction.

This paper does not provide a universal legal determination of agent authority.

Relevant legal and governance instruments include the General Data Protection Regulation and the European Union Artificial Intelligence Act, where applicable (European Union, 2016); (European Union, 2024).

8.9.8 Commercial Causality

Observable agent activity does not independently establish that the agent caused a transaction or increased revenue.

Causal interpretation requires additional evidence and suitable research design.

8.10 Implications for Businesses

The proposed framework suggests that businesses may need to consider how their organisational information is interpreted within agent-mediated commercial tasks.

Relevant considerations include accurate entity identity, clear service descriptions, verifiable capabilities and current commercial information.

Knowledge representation, structured data and information provenance provide relevant technical foundations for examining these considerations (Hogan et al., 2021); (W3C, 2014); (Schema.org, n.d.); (W3C, 2013).

Where organisations provide agent-accessible commercial tools, additional considerations include authentication, permissions, transaction controls and reliable confirmation.

Security research demonstrates that systems processing untrusted external information may face additional risks (Greshake et al., 2023).

However, the existence of these capabilities does not guarantee that an AI agent will discover, recommend or select the organisation.

Business decisions should be informed by observable evidence rather than unsupported predictions about future agent behaviour.

8.11 Implications for SEO and GEO

The research extends the conceptual scope of SEO and GEO towards agent-mediated organisational evaluation.

Traditional search visibility, AI citation visibility and agent recommendation are related research topics, but they are not identical.

An organisation may appear within a search result without being selected as a source for an AI-generated answer.

It may be cited without being recommended as a provider.

It may be recommended without generating a measurable commercial transaction.

Consequently, organisations should avoid combining these outcomes into a single unsupported visibility or authority score.

Research should examine the relationship between information accessibility, entity representation, evidence retrieval and recommendation outcomes under documented conditions.

Retrieval-augmented generation and knowledge graph research provide relevant foundations for examining information retrieval and entity representation, but do not establish a universal method for influencing commercial AI recommendations (Lewis et al., 2020); (Hogan et al., 2021).

8.12 Implications for AI Agent Developers

The proposed framework also identifies questions relevant to developers of commercial AI agents.

These include how agents interpret mandatory requirements, evaluate evidence, handle conflicting information and explain recommendations.

Transaction-capable systems introduce further questions concerning authorisation, external tool security, error handling and transaction verification.

Developers may need to distinguish successful tool invocation from successful completion of the user's commercial objective.

Agent evaluation should therefore consider both task performance and compliance with defined permission boundaries.

Research into human–AI interaction, prompt injection and AI risk management provides relevant foundations for evaluating these requirements (Amershi et al., 2019); (Greshake et al., 2023); (NIST, 2024).

8.13 Future Research Questions

The paper identifies a wider research agenda.

1. How consistently do AI agents identify relevant organisations during unbranded commercial discovery tasks?
2. How accurately do agents distinguish organisations with similar names or overlapping identities?
3. What evidence is used when assessing provider eligibility?
4. How do agents handle missing or contradictory commercial information?
5. How frequently do agents recommend providers that fail mandatory requirements?
6. How stable are recommendations across repeated observations under comparable conditions?
7. How does tool availability affect provider discovery and recommendation outcomes?
8. Can agent-generated explanations be independently verified against retrieved evidence?
9. How reliably do agents respect transaction authorisation boundaries?
10. How accurately do agents report completed, pending and failed transactions?
11. How do outcomes differ between commercial sectors and task categories?
12. What evidence is necessary to establish incremental commercial value from agent-mediated discovery?

These questions provide potential directions for future CGO Media research publications.

8.14 External Academic and Technical References

The following bibliography consolidates the external academic, technical, standards and institutional sources referenced throughout this research paper.

References are presented in Harvard author–date style, with publication details and links to the original source or issuing organisation.

Each entry includes a stable HTML identifier so citations in Sections 1–8 can link directly to the corresponding reference.

These sources support the research context. They do not independently validate the five original CGO Media conceptual models.

8.15 Interpretation of the External Evidence

The external literature supports the relevance of studying tool-using language models, retrieval-based generation, knowledge representation, interactive agent evaluation and AI system governance.

Research into autonomous agents demonstrates the technical relevance of combining language-model reasoning with planning, retrieval and external actions (Yao et al., 2023); (Wang et al., 2024).

Research into knowledge graphs and information provenance provides foundations for examining organisational identity and evidence relationships (Hogan et al., 2021); (W3C, 2013).

Research into interactive agent benchmarks demonstrates the importance of evaluating observable task outcomes (Zhou et al., 2024); (Liu et al., 2024).

However, these publications do not establish that commercial AI agents consistently follow the exact discovery, evidence evaluation, recommendation or transaction stages proposed in this paper.

Nor do they establish that agent-mediated business discovery has replaced traditional search behaviour.

The five CGO Media models are proposed conceptual contributions intended to support future empirical investigation.

Their predictive and explanatory value remains to be tested.

8.16 Supporting CGO Media Research

This paper forms part of the wider CGO Media research programme examining AI search, entity authority, generative recommendation, commercial discovery and search measurement.

• CGO Media Research Library
• CGO Media Framework Library
• CGO Media Research Observations Library
• AI Recommendation Authority in Generative Search
• Entity Authority in AI Search
• AI Source Selection in Generative Search
• CGO Media Research Methodology
• Latest CGO Media Research

8.17 Research Disclosure

8.18 Author and Research Responsibility

8.19 Recommended Citation

8.20 Publication Information

8.21 Final Research Conclusion

AI agents introduce a potentially important development in digital business discovery.

Their significance lies not simply in generating more detailed answers but in the possibility of coordinating research, provider evaluation, recommendation and authorised commercial actions.

However, these activities represent different outcomes and involve different evidence requirements.

Organisational discovery does not establish provider suitability.

Provider suitability does not establish recommendation.

Recommendation does not establish commercial selection.

Commercial selection does not establish transaction completion.

And transaction completion does not independently establish incremental commercial value.

The five original CGO Media conceptual models proposed in this paper provide a structured foundation for investigating these distinctions.

Future research should test the models against observable agent behaviour, independent reference evidence and documented commercial task conditions.

Such investigation may contribute to a more rigorous understanding of how AI agents influence organisational visibility, provider evaluation and commercial decision-making.

Final Research Statement