Agentic AI Testing: Methods & Best Practices

What this post added

This post introduces agentic AI testing, highlighting its differences from traditional model testing due to non-determinism, statefulness, and error propagation through tool use and multi-agent interactions. It categorizes agentic testing methods into tool-level, trajectory, end-to-end, simulation-based, LLM-as-judge, adversarial, regression, and failure analysis. It also details essential observability instrumentation for agentic systems, including tracing agent invocations, LLM calls, tool executions, and memory/retrieval operations, with considerations for PII and multi-agent tracing. Finally, it outlines infrastructure considerations for production agent testing, emphasizing concurrency and statefulness.