Judgment Labs is now generally available, providing a continuous improvement toolchain for AI agents
Judgment Labs has officially opened for general access (GA), positioned as a continuous improvement toolchain for AI agents; agent clusters can traverse production traces to discover failures, classify and investigate them, and test fixes. Its Judgment MCP can be used with Claude, Codex, Cursor, or any MCP client. Publisher Alex Shan said that long-running agent systems generate large amounts of trace data, which records user requirements, when agents go wrong, and how agents interact with the external world.
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The original text explains the product positioning and the MCP integration method, so readers can judge whether it can be used for troubleshooting their own agents.
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View original postToday, @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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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.