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Agentic Commerce

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Buyer-Agent Selection Test Lab Agent

Find gaps in your offer proof.

Test whether an AI assistant can understand your offer and match it to a buyer's needs.

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Use Buyer-Agent Selection Test Lab

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Selection test reportIllustrative demo

Test Atlas against EU hosting, £2,000 annual budget, 14-day launch and SSO.

  1. Froze four criteria and E-4 source snapshot.

What it does

See whether an AI assistant can answer realistic buyer questions.

Test whether an AI assistant can understand your offer and match it to a buyer's needs. Built for merchants testing whether their public evidence answers buyer-agent selection criteria.

  1. Define the buyer tests

    Provide the offer and a few realistic buyer requests you want to test. Real enquiry patterns and past missed requirements can make the tests more useful.

  2. Assess the offer evidence

    Run the sample requests and check which requirements the assistant can answer from your information.

  3. Compare the test results

    Get a results summary and specific suggestions for making your offer clearer.

How it works

Test whether an AI assistant can understand your offer and match it to a buyer's needs. Built for merchants testing whether their public evidence answers buyer-agent selection criteria.

Agent architectureTools · knowledge · outputs · oversight
React Flow
Approved documents and context
Frozen buyer brief suite and scoring rubric Approved public offer evidence snapshot Optional: anonymised buyer enquiries and reviewed failed matches, to build realistic evaluation cases beyond the obvious happy path. Optional: versioned offer snapshots and prior scored runs, to compare whether a content change improved answerability.
See your offer through buyer requests
Review how an assistant answers realistic purchase questions using your available offer information.
Make improvements based on test results
Use specific gaps and unclear wording found in the tests to focus the next content changes.
Contentful · Read published offer evidence (read)
Freeze the merchant evidence used by every paired test.
Anthropic · Run configured synthetic buyer tests (draft)
Use a pinned accessible model with explicit fixture instructions; not observation of real buyers.
Google Gemini · Run independent configured comparison tests (draft)
Record model version, sampling configuration and provider errors separately.
Human review and permissions
Product owner confirms factual corrections before a frozen-suite retest. If something fails, pause the affected request and check what actually happened. Correct the source information, repeat the checks and renew approval if the proposed action changed. Keep a record of the issue.

  1. 01Gather the context

    Bring together the relevant information from Contentful, Anthropic, Google Gemini.

  2. 02Check what matters

    Run the sample requests and check which requirements the assistant can answer from your information.

  3. 03Prepare the result

    Get a results summary and specific suggestions for making your offer clearer. What has been checked, what still needs a decision and whether any requested action actually completed. Keep private internal notes out of the buyer's response.

  4. 04Review proposed changes

    Product owner confirms factual corrections before a frozen-suite retest. If something fails, pause the affected request and check what actually happened. Correct the source information, repeat the checks and renew approval if the proposed action changed. Keep a record of the issue.

Integrations

Content management

Examples:
  • Contentful
  • WordPress

Model providers

Examples:
  • Anthropic
  • Google Gemini

Choose the platforms your team already uses—you don’t need every example shown. Confirm the required features and configure access during implementation.

What you get

Find out how AI assistants interpret your offer and which content gaps to address before rerunning buyer tests.

See your offer through buyer requests
Review how an assistant answers realistic purchase questions using your available offer information.
Make improvements based on test results
Use specific gaps and unclear wording found in the tests to focus the next content changes.

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