Agentic AI testing guide: methods & best practices

Agentic AI testing is distinct from traditional model testing, as it involves evaluating the entire decision-making process, tool calls, and state management, rather than just model outputs. Unlike standalone models, which are deterministic, agents can produce different valid outcomes from the same input due to their interactions with live data and tools, making exact-match testing insufficient. The testing of agents requires methods that assess behavior, capabilities, reliability, and safety through various approaches, including tool-level testing, trajectory evaluation, and simulation-based testing. Observability is crucial for diagnosing issues, requiring detailed traces of agent operations like tool executions and memory retrievals. Effective testing infrastructure must support stateful agent sessions, manage concurrency, and ensure safety controls. Redis Iris is highlighted as a unified data layer solution that provides fast access to context and memory, optimizing agent performance and reliability in production environments.

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