How to Evaluate an AI Agent Before Giving It Real Work

Evaluate an AI agent by checking whether it completes the intended task under the right constraints. A fluent response, a valid JSON object, or a successful tool call is not sufficient evidence that the business outcome is correct.

Start with observable acceptance criteria and a test environment where actions can be inspected. Include cases where the correct behavior is to ask, stop, or escalate.

Define what success means for one job

Consider an assistant that investigates damaged shipments and prepares a replacement recommendation. It must identify the correct order, consult the applicable policy, cite the evidence, and leave the request ready for a reviewer.

It must not create a replacement order unless that action is explicitly part of its authorized workflow. A test that grades only the quality of the final message would miss an unintended mutation made during investigation.

Specify both the desired result and prohibited side effects. Where a system can take actions, inspect the application state before and after the run.

Build a compact task set

Use historical cases you are permitted to process, supplemented with clearly synthetic fixtures for controlled edge cases. Remove unnecessary personal data.

Keep some cases separate from the examples used to tune the agent. Otherwise, a prompt may improve on familiar cases without improving on new requests.

Match the check to the claim

Use deterministic checks for exact fields, permitted tool arguments, operation counts, and application state. Use human review for nuanced evidence quality, appropriateness, and ambiguous policy interpretation.

An LLM-based grader can help review larger sets, but it needs an explicit rubric and calibration against human judgments. Do not let the same system's confident self-assessment become the only evidence of its correctness.

Anthropic's evaluation guidance distinguishes several grading methods and the role of execution traces. Use traces to diagnose errors, while verifying outcomes against the relevant system of record.

Avoid a misleading overall score

Suppose a fictional test run has 50 tasks. The agent handles 48 acceptably and performs a forbidden action in two. A headline score of 96% hides the most important release problem.

Report results by category: task completion, incorrect side effects, permission violations, unsupported claims, escalation, latency, and cost. Define blocking conditions for the particular workflow rather than assuming a universal accuracy percentage is enough.

Repeat selected tasks to observe variation. One successful attempt does not establish that a case is consistently handled. Record the number of trials and the configuration so comparisons remain meaningful.

Make failures useful

For each failure, save the task, expected result, model and tool versions, relevant evidence, and execution trace under appropriate access controls. Identify whether the problem came from missing context, tool behavior, policy ambiguity, or model reasoning.

Fix the responsible component and re-run both the failed case and related cases. Adding one instruction can improve a special case while damaging a routine one.

Keep the evaluation set versioned. Re-run it when changing the model, context assembly, tool schemas, retrieval, permissions, or business rules.

Use the pilot to validate operations too

Assign an owner to unresolved tasks and measure the time spent reviewing or correcting them. Test dependency outages and revocation, not only ordinary requests. Confirm that the agent can stop with an honest incomplete status.

Use the resulting evidence to decide whether to expand access, narrow the task, or redesign the workflow. Continue with production failure patterns and human approval and recovery. Dream Hatch Labs can help implement and evaluate your agent.