Bottom Line: Agent-EvalKit automates the evaluation of AI agents through structured test-case generation, observability instrumentation, and combined code and LLM-based metrics directly in the development environment.
AWS provides Agent-EvalKit, an Apache-2.0-licensed toolkit for systematic evaluation of autonomous AI agents. The solution integrates directly into Claude Code and other AI coding assistants to capture tool calls, intermediate steps, and hallucinations along the execution path.
Traditional software testing compares outputs against expected results. For autonomous AI agents, this is insufficient: an agent can deliver a coherent, structured answer while simultaneously hallucinating facts or initiating tool calls with incorrect parameters. These errors lie beneath the final answer and require evaluation of the complete execution path: which tools were called? What data did they return? Does the answer correctly reflect that data?
Agent-EvalKit builds this evaluation infrastructure directly into the IDE. Developers describe their evaluation objectives in natural language and provide them to Claude Code or other integrated AI assistants as slash commands (such as `/evalkit.plan` or `/evalkit.data`). The assistant then reads the agent source code, tool definitions, and system prompts and progresses through six phases: objective planning, test-case generation, evaluation, and recommendations with references to specific code lines. The assessment combines code-based evaluators (fast, reproducible) with LLM-powered judges for nuanced analysis.
Essential evaluation dimensions are fidelity to tool return values, correctness of tool calls including parameters, and coherence of the output. No single metric captures all three; Agent-EvalKit checks each dimension separately and generates concrete improvement suggestions instead of lifeless dashboards. The toolkit works with Strands Agents SDK and Amazon Bedrock and is available as open source.
Source: aws.amazon.com · Published June 11, 2026
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