Evaluate AI agents systematically with Agent-EvalKit
📰 AWS Machine Learning
Systematically evaluate AI agents with Agent-EvalKit, a open-source toolkit that integrates with AI coding assistants
Action Steps
- Install Agent-EvalKit using Apache 2.0 license
- Integrate Agent-EvalKit with AI coding assistants like Claude Code, Kiro CLI, or Kilo Code
- Build an AI agent using the Strands Agents SDK and Amazon Bedrock
- Configure Agent-EvalKit to evaluate the AI agent across six phases
- Run the evaluation phases to assess the AI agent's performance
- Analyze the evaluation results to identify areas for improvement
Who Needs to Know This
AI/ML engineers and researchers can use Agent-EvalKit to evaluate and improve their AI agents, while developers can integrate it with AI coding assistants to streamline the evaluation process
Key Insight
💡 Agent-EvalKit provides a structured approach to evaluating AI agents, enabling developers to identify and improve their agents' performance
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🤖 Evaluate AI agents systematically with Agent-EvalKit! 🚀
Full Article
Agent-EvalKit is an open-source toolkit (Apache 2.0) that makes this evaluation infrastructure available by integrating with AI coding assistants, including Claude Code, Kiro CLI, and Kilo Code. This post walks through how Agent-EvalKit works across its six evaluation phases, using a travel research agent built with the Strands Agents SDK and Amazon Bedrock as a running example.
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