📄 中文摘要
AI 代理的行为往往是黑箱，难以调试。它们读取文件、做出决策、调用工具，只有在出现故障时才能了解发生了什么。为了解决这一问题，agentrace 应运而生，提供了结构化的可观察性。作为一个 MCP 服务器，agentrace 为 AI 代理提供了七种追踪工具，包括开始追踪、记录操作、记录决策等。这些工具使得开发者能够清晰地追踪代理的执行路径，理解其推理过程，并重放其决策，从而提升调试和优化的能力。
我为 AI 代理构建了一个可观察性工具——追踪它们的行为和原因
出处: I built an observability tool for AI agents — trace what they do and why
发布: 2026年3月28日
📄 English Summary
I built an observability tool for AI agents — trace what they do and why
AI agents often operate as black boxes, making debugging difficult. They read files, make decisions, and call tools, revealing their actions only when something goes wrong. To address this issue, agentrace was developed, providing structured observability for AI agents. As an MCP server, agentrace offers seven tracing tools, including starting a trace session, logging actions, and recording decisions. These tools enable developers to clearly trace an agent's execution path, understand its reasoning process, and replay its decisions, thereby enhancing debugging and optimization capabilities.