AI Agent Testing Strategies: Ensuring Reliability in Production

Why AI Testing Is Different

Conventional tests use binary assertions. AI agents produce outputs on a quality spectrum. Non-determinism means the same input produces different outputs. Semantic correctness cannot be reduced to string equality. And LLM calls are too expensive to run thousands as unit tests.

The Testing Pyramid

Layer 1: Unit Tests with Mocks

Mock the Anthropic client to test output parsing, tool routing, and error handling without LLM calls. Assert on structure (correct keys in JSON), routing (right tool selected), and error paths (rate limits handled).

Layer 2: LLM-as-Judge

For semantic quality, a separate Claude call evaluates outputs against defined criteria. Score each criterion 1-5 and set a pass threshold. Run against 20-50 golden dataset inputs on every PR that changes prompts or agent logic.

Layer 3: Production Sampling

Sample 5% of production requests for quality evaluation. Run evaluations asynchronously to avoid user-facing latency impact. Alert when quality scores drop below threshold -- early warning for prompt drift and model behavior changes.

CI/CD Integration

Trigger eval runs on PRs that modify prompts, agent logic, or tool implementations. Fail the PR if pass rate drops below 80%. This gates quality regressions the same way unit test failures gate code regressions.