AWS has released Agent-EvalKit, an open-source toolkit designed to help developers evaluate AI agents more systematically. The tool integrates with AI coding assistants like Claude Code, Kiro CLI, and Kilo Code, allowing teams to assess their agents’ behavior throughout the development lifecycle. By embedding evaluation within the development environment, the toolkit streamlines the process of identifying and addressing issues in AI agent performance.
Agent-EvalKit operates through six distinct phases, each producing artifacts that feed into the next. These phases include planning the evaluation, generating test cases, instrumenting the agent with tracing capabilities, running tests, evaluating results, and generating a report with actionable recommendations. The toolkit supports frameworks like Strands, LangGraph, and CrewAI, and it uses OpenTelemetry-compatible tracing to capture the agent’s full execution path. Developers can guide the evaluation process using natural language commands, ensuring that specific quality dimensions are prioritized.
The source text explains that Agent-EvalKit addresses the limitations of traditional output-level testing, which cannot fully capture the behavior of AI agents that autonomously choose tools and sequence operations. By tracing the agent’s execution, the toolkit identifies issues such as hallucinations or skipped verification steps that may not be evident in the final response. This structured approach helps developers translate evaluation scores into specific code changes, improving the reliability and performance of AI agents.
Source: awsml
