Judgment Labs 正式开放，为 AI 智能体提供持续改进工具链
Judgment Labs is now GA 👀
Judgment Labs 已正式开放（GA），定位为 AI 智能体的持续改进工具链，智能体集群可遍历生产轨迹来发现故障、分类排查并测试修复。其 Judgment MCP 可与 Claude、Codex、Cursor 或任意 MCP 客户端配合使用。发布者 Alex Shan 称，长时运行的智能体系统会产生大量轨迹数据，其中记录了用户需求、智能体出错时机以及智能体与外部世界的交互方式。
查看原帖今天，@JudgmentLabs 正式开放通用访问。 我们相信，长时间运行的智能体系统所产生的轨迹数据将超出世界目前所能处理的范围。 智能体轨迹中隐藏着丰富的记录，揭示了如何让这些系统变得更好：用户提出了什么需求、智能体在何时出错，以及智能体如何与外部世界交互。 Judgment 是面向长时程智能体性能最强的监控与改进层。 我们非常期待看到你的作品！
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Today, @JudgmentLabs opens for general access. We believe long running agent systems will generate more exhaust data than the world can currently process. Hidden within agent traces are rich records of how to make these systems better: what users ask for, when agents stumble, and how agents interact with the external world. Judgment is the most performant monitoring and improvement layer for long-horizon agents. We’re excited to see what you build!
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Judgment Labs is now GA 👀
It’s a “continuous-improvement stack” for AI agents. Agent swarms can go through production traces to spot failures, triage them, and test fixes.
The Judgment MCP works with Claude, Codex, Cursor, or any MCP client.
Curious to see how much time it saves when you run agents at scale.