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Amazon shares a comprehensive framework for evaluating agentic AI systems built at scale across its organizations. The framework has two core components: an automated evaluation workflow (trace ingestion → metric generation → dashboarding → monitoring/HITL) and a layered evaluation library covering final response quality, task completion, tool use, memory, multi-turn conversation, reasoning, and safety. Three real-world use cases illustrate the approach: the Amazon shopping assistant (tool-selection accuracy across thousands of APIs), the customer service agent (intent detection correctness and routing), and the seller assistant multi-agent system (inter-agent coordination metrics). Key lessons include the need for holistic multi-dimensional evaluation, application-specific metrics, human-in-the-loop validation for high-stakes decisions, and continuous production monitoring to detect agent decay.

Evaluating AI agents: Real-world lessons from building agentic systems at Amazon

Evaluating real-world agent systems used by Amazon