How to Select an AI Agent Evaluation Platform

Begin with the agent and the decision

Record the workflows, models, languages, tools, data sensitivity and deployment environments in scope. Define whether the platform will support development, procurement comparison, release approval, production monitoring or all four. A good product for prompt experiments may be insufficient when the decision depends on external state, permissions and verified business actions.

Examine scenario and dataset management

Check how cases are imported, generated, versioned, reviewed and separated between development and held-out use. Ask whether production traces can become candidate cases without becoming automatic ground truth. Confirm that expected outputs, accepted alternatives, severity, provenance and data-retention rules can be recorded.

Inspect trace and environment support

The platform should expose relevant model calls, retrieval, tool inputs, tool outputs, handoffs and timing. Establish whether it runs the application or merely receives traces. Ask how mocks, sandboxes, seeded data, asynchronous events, permissions and final-state assertions will be implemented. These capabilities may belong to a separate test harness.

Evaluate graders and human review

Look for deterministic checks, custom code, model judges, pairwise comparison and structured human annotation. Confirm that judge prompts, models and thresholds are versioned. The platform should let reviewers inspect disagreements and preserve calibration evidence rather than showing only an aggregate score.

Test reproducibility and integration

Require comparable experiments, stable case identifiers, exports, APIs and CI integration. Run a proof with a known failure, a changed tool response and a grader disagreement. Confirm that another reviewer can reconstruct the result and that the organisation can retain its evaluation assets if the product changes.

Assess operating fit and ownership

Consider hosting, data residency, access control, audit logs, model-provider choice, cost and support. Define who owns the test collection, environment, graders and release decision. A platform supplies infrastructure. Management remains responsible for the claim being tested and the reliance placed on the result.

Example: testing what the platform does not supply

A finance team evaluates an invoice agent. The platform stores cases, runs versions and displays model and tool traces, but it does not contain an ERP that can reproduce purchase orders, approval limits or final payment state. The organisation therefore builds a separate harness with seeded records and mock payment endpoints. One endpoint acknowledges a request and withholds settlement. The platform captures the experiment; the harness establishes whether a duplicate instruction was created. Treating the platform dataset as the operational environment would miss the decisive failure.

The platform selection evidence

Marketways uses a scored proof rather than a feature checklist. The proof runs known ordinary, failure and grader-disagreement cases; records setup effort and missing infrastructure; and tests exports, access, versioning and reconstruction by another reviewer. Management receives a capability map, architecture decision, ownership model, cost view and conditions for adoption. Product claims are labelled separately from observed proof results. The organisation retains its scenarios, expected behaviour and release logic even if the chosen platform later changes.

What to read next

• AI agent evaluation platforms compared: review documented provider capabilities
• Test data and operational data: separate product data management from operational fixtures
• Validate an LLM judge: test a platform's automated scoring