Show HN: Chaos Engineering for AI Agents (github.com)

The new tool "agent-chaos" has been introduced to the AI community, addressing a crucial gap in testing AI agents before they hit production environments. By simulating failures and disruptions, it helps teams anticipate issues like API rate limits, unexpected server errors, and data format corruption that often occur once the AI is deployed. Unlike traditional chaos engineering tools, which focus on infrastructure, agent-chaos targets the specific vulnerabilities of AI agents, allowing developers to see how their systems respond to various error conditions. This tool is particularly significant as it enables a deeper evaluation of AI performance beyond typical success scenarios. By integrating with evaluation frameworks like DeepEval, agent-chaos allows for precise assessments of agent responses under adverse conditions. The package offers a range of chaos injectors for both LLM and tool failures, with the capability to execute fuzz testing to uncover edge cases. This proactive approach not only enhances the reliability of AI agents but also fosters confidence among developers, ensuring their systems can handle real-world unpredictability effectively.