Testing Agentic AI Systems
Building an AI agent is easy. Making sure it does not go rogue is hard. You need a strict testing framework to move from prototype to production.
Follow these eight stages to secure your agent:
Stage 1: Component tests Write unit tests for every layer. Test your research agent, your search tools, and your memory. Use mock data approved by your experts. Stub your external APIs like Shopify or Meta. If an API is down, your test should not fail because of it.
Stage 2: The prompt repository Build a library of sharp prompts. Tag them by business area. Include failure cases like prompt injection and empty tool responses. Test multi-turn conversations to ensure memory works. Check that user data does not leak between sessions.
Stage 3: Coverage and trajectory Check if every tool actually fires. Then, check the path the agent took. It is not enough to fire a tool. The agent must use the right tool, with the right arguments, in the right order.
Stage 4: Versioned runs Stamp every run with a version number. Store every response. Run each prompt several times to account for model randomness. Track your pass rate, cost, tokens, and latency. Accuracy is a business trade-off against speed and price.
Stage 5: Ground truth store Keep verified answers for every prompt. Decide who can change these answers. If you do not update your ground truths when your product changes, your tests will fail correctly.
Stage 6: The evaluator Score runs against your ground truth. Use an LLM judge to check precision and correctness. Watch for judge bias. Compare LLM scores against human labels to ensure accuracy.
Stage 7: Human review Create a dashboard for low-scoring cases. Let humans correct the errors. Use these human corrections to train your LLM judge.
Stage 8: CI/CD integration Run component tests on every pull request. Run the full suite every night. Set a threshold that blocks deployments if scores drop.
Optional learning community: https://t.me/GyaanSetuAi