How to Test AI Agents Before Production

Collect 50-100 real or realistic test cases that cover the agent's expected workload. Include typical cases (the 80% happy path), edge cases (unusual inputs, incomplete data, ambiguous requests), adversarial cases (prompt injection attempts, out-of-scope requests), and failure scenarios (API timeouts, missing data, conflicting instructions).

Each test case should have a defined expected behavior — not an exact expected response, but a set of criteria. 'Agent should identify this as a billing question, check the customer's account, and provide the correct balance' is testable. 'Agent should respond with exactly this sentence' is not, because it's too brittle for probabilistic outputs.

Use a separate LLM as an evaluator to score agent responses at scale. The evaluator receives the test input, the agent's response, and a rubric (criteria for a good response), then outputs a pass/fail with reasoning. This approach lets you run hundreds of evaluations without human reviewers for every test.

Tools like LangSmith, Braintrust, and custom evaluation scripts make this pipeline manageable. Run evaluations automatically whenever you update the agent's prompt, tools, or model. Regressions that would take days to discover manually get caught in minutes.

Beyond response quality, verify that the agent calls the right tools in the right order. If the agent should check the CRM before responding to a customer inquiry, test that it actually does — not just that the final response sounds correct. An agent that gives the right answer without checking the database is guessing, and guessing works until it doesn't.

Log every tool call during testing and verify the sequence against expected behavior. Track which tools are called, with what parameters, and in what order. This is especially important for multi-step workflows where skipping a step or calling tools out of order produces subtle errors.

Attempt to break the agent deliberately. Send prompt injection attacks that try to override the system prompt. Ask for information the agent shouldn't share. Request actions outside the agent's scope. Feed it malformed data, excessively long inputs, and inputs in unexpected languages.

Every safety test should confirm the agent refuses gracefully, doesn't leak system prompt contents, doesn't execute unauthorized actions, and doesn't expose sensitive data. Document every failure as a security finding and address it before deployment.

Before going fully live, run the agent in shadow mode — processing real inputs but holding all outputs for human review before they're sent. This catches issues that synthetic test cases missed because real-world inputs are always messier and more varied than test suites anticipate.

Shadow mode typically runs for 1-2 weeks. Review a representative sample of outputs daily. Track the agent's accuracy, safety compliance, and cost metrics against your production thresholds. When the shadow metrics consistently meet your standards, promote to full production with monitoring alerts configured.