Agentic AI Testing: Methods & Best Practices

This post introduces agentic AI testing methods and best practices, highlighting the unique challenges of testing agents compared to standalone models due to non-determinism, tool use, memory, and multi-agent systems. It proposes a taxonomy of testing methods including tool-level/component testing, trajectory/path evaluation, end-to-end task completion, simulation-based testing, LLM-as-judge, adversarial testing, regression testing, and failure analysis. The post also details essential observability instrumentation for agent traces, focusing on capturing agent invocations, LLM calls, tool executions, and memory/retrieval operations, along with considerations for PII and multi-agent systems. Finally, it discusses infrastructure considerations for production agent testing, emphasizing stateful concurrency, latency goals for task completion, and scaling inference and tool serving.