AI Agent Evaluation: How to Test Before Launch

A good answer can hide a bad tool call. Here’s what to test before your agent goes live.

AI agent evaluation checks the business outcome, the tool calls used to reach it, and whether the agent stops when evidence is missing. Build a labeled set of realistic cases, score each behavior independently, and define which failures block a release before running the test.

What should you evaluate in an AI agent?

Start with the operation your team needs to trust. For a subscription business, that might be reconciling a customer’s subscription status across billing and CRM. A plausible summary is only one output. Check whether the agent retrieved the right account, chose the correct records, and left the correct final state. A test should catch an agent that says a record was updated when the write actually failed.

Measure task success and the path taken

A case can have several acceptable paths. The agent might look up a verified account ID first or obtain it through an approved reference lookup. Score the outcome and enforce constraints on the path, such as no access across accounts and no write before approval. Capture references, tool responses, and decisions so a reviewer can trace a failure. The OpenAI agent evaluation guide describes using traces to inspect workflow behavior.

Write down what a good escalation looks like

A subscription reconciliation may involve an expired account, a pending renewal, or conflicting dates in two systems. An acceptable escalation identifies the account, shows both records, and explains what the reviewer needs to decide. Treat a well-grounded escalation as success when the case falls outside the agent’s authority. Otherwise a score that rewards only full automation can encourage the agent to act on incomplete evidence.

How do you build an AI agent evaluation dataset?

Use recent cases with realistic inputs, preserving categories and removing fields that are unnecessary for the test. Ask the process owner to mark expected records, acceptable outcomes, and prohibited actions. Include common cases and rare failures that have significant consequences. Hold out a test set that the person adjusting the workflow does not use to tune each change.

Include tool and data failures

Test a stale billing record, an account with a similar name, a timeout following a tool call, and a source document that contains an instruction to disclose data. Replay an event twice to see whether the agent repeats a write. Give the test integration a controlled way to reject operations so you can distinguish the agent stopping appropriately from the system accidentally blocking everything.

Score each failure at the right level

Grade whether the answer is accurate, whether the right tools were used, whether the records support the conclusion, and whether the handoff is actionable. Identify hard failures separately. A cross-account read or an unauthorized update can block release regardless of a high average score. Have a second reviewer score borderline cases. When reviewers disagree, refine the rubric and keep an example of each decision.

What do useful pilot metrics look like?

In a simulated set of 120 account cases, 96 pass on first review, 18 require rework, and six contain a release-blocking error. The 80% pass rate helps compare versions, while the six failures tell you which capabilities are still unsafe. Group errors by reason and fix one cause at a time so you can see which change improves the next run.

80% first-pass outcomes 18 cases requiring rework 6 release-blocking errors

In a separate simulated time study, staff spend ten minutes each reviewing 36 cases without assistance and six minutes each with it. The difference is 144 minutes before maintenance. Measure the cost of any cases that take longer with the agent, including time spent checking the source after a misleading summary. Count those cases in the same denominator and report their category.

360 min manual review time 216 min assisted review time 144 min gross difference

How can you detect a regression before a release?

Freeze the test inputs and record the versions of the model, tools, prompts, and rules. Run the stable suite after any change and add new cases as you learn from failures. Compare results by category. If the new version improves simple lookups but fails more access-denied cases, review that difference before promoting it. Repeat cases where outputs vary. Test both the best-case path and a case that requires a clear stop.

When is the agent ready to handle live work?

Have the process owner sign off on the outcomes, the blocking criteria, and who handles exceptions. Begin in shadow mode with no live writes, then enable a single reversible action for a limited queue. Set an alert for unexpected tool behavior and make it possible to disable writes without losing case intake. See the autonomous AI agents guide for delegation scope and the AI agent oversight guide for operating controls after release.

What does a useful evaluation case look like?

Give the case a stable ID and write down the facts staff can verify. For example, the billing platform shows that a renewal succeeded while the CRM still displays an expired status. The expected output identifies the right account, cites both records, and recommends a review. The prohibited action is a subscription change without approval. This distinction lets staff score the outcome even if the agent takes a different valid route to get there.

Separate automated checks from human review

A test can confirm that the right account was read, that no update occurred, and that the escalation includes a reason code. A reviewer can judge whether the evidence is persuasive and the handoff gives staff enough context to act. Save both results. Otherwise a high text quality score could hide the use of a wrong account, while a mechanically correct output could still be unusable for support.

How do you investigate an inconsistent test?

Replay it under the same conditions. If the upstream service returns inconsistent data, freeze the fixtures or document the time dependency. If the tool response is stable but the model makes different decisions, adjust the decision boundary and repeat the run. Record all outcomes, including failures. Check nearby scenarios after the fix so the change does not improve one special case at the expense of routine requests.

Use test cases during incident review too. Translate the incident into a small scenario that reproduces the bad decision, correct the root cause, and add the scenario to the stable suite. This makes it easier to tell whether a later integration update brings back the same problem.

For the next step, see how to test security controls at the integration.

How Dopweb helps you evaluate an AI agent

We help your team define a rubric, create realistic cases, and build a safe test environment. We examine tool traces alongside user-facing answers and report which errors require a change before launch. We also measure token usage and retries per usable result. After rollout, we can add failed cases to the test suite and repeat it when your systems change. Explore AI Agents & Workflow Automation.

About the author — Patrick Chemoul, CEO of Dopweb
Patrick Chemoul leads Dopweb, an agency helping businesses with websites, software, and automation. The agency also provides AI agent integration and team training. Learn about Dopweb.

Frequently Asked Questions

Let’s Talk About Your Project

Tell us what your team wants to accomplish. We’ll help define the first use case and the checks it needs before launch.