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AI Agent Product Evaluations

Find out whether coding agents can successfully use your product.

Run an AI coding agent, acting as a developer in your ICP, through discovery, signup, setup, and a real build task. See where the product is legible to agents—and where it fails without anyone filing an issue.

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Problem

Coding agents are becoming part of the adoption path.

A developer may ask an agent to compare tools, read the docs, install the SDK, configure authentication, generate code, or troubleshoot an error.

When the agent cannot understand the product, the failure is often invisible. No support ticket. No interview. No useful reason attached to the abandoned attempt.

Readiness check

Check whether agents can find the basics.

The credit-priced static readiness check reviews signals such as:

  • llms.txt availability;
  • documentation access;
  • MCP presence;
  • clear setup instructions;
  • machine-readable product context; and
  • obvious blockers to agent-led discovery.

The readiness check does not launch a browser session or complete a product task.

Check agent readiness

Full evaluation

Give the agent a real job to complete.

A paid evaluation runs an agent, configured to act as a developer in your ICP, through a defined product journey. The agent attempts to understand the product, sign up, configure it, and build something useful.

The result shows:

  • what the agent discovered;
  • which sources it trusted;
  • the instructions and examples it followed;
  • where it misinterpreted the product;
  • commands and steps attempted;
  • errors and blockers encountered; and
  • whether it completed the assigned outcome.

AI Agent Product Evaluations

How it works

  1. Choose the target developer profile and task.

  2. Define the expected successful outcome.

  3. Review the credit price and start the run.

  4. Inspect the complete evaluation and findings.

  5. Use the findings to improve product context, docs, examples, onboarding, and positioning.

AI Agent Product Evaluations

How it connects

FAQ

Is this the same as a static AI-readiness audit?
No. The readiness check reviews static signals. The full evaluation gives an agent a real task and observes the complete attempt.
Which agent performs the evaluation?
The selected evaluation configuration determines the agent environment and task.
Can the evaluation use a private product?
Private-product access depends on the supported authentication and test-environment requirements.
Will the agent make changes to production?
The task must define the allowed environment and actions. Use a safe test account or sandbox whenever the product supports one.

See your product through the next developer interface.

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