A proposed debugger finds why an AI agent failed
The proposed tool is a debugger for AI agents running in real products. It would read the agent’s traces and point to the likely cause of a failure, instead of only showing that the run was slow or costly. The target cases include , agents that call tools, and .
The tool would look for causes such as poor retrieval, the wrong tool being called, or the agent moving away from its goal. Existing tools such as Langfuse and Langwatch already show traces and , but teams may still need to inspect logs by hand to understand what broke. The proposed value is to find quiet problems automatically and show evidence for the exact step where the failure began.
The open questions are whether teams feel this pain often, whether they want a separate product, or whether they would rather see this added to the tools they already use.
Key points
- The idea is a debugger for AI agents already running in production.
- It focuses on why an agent failed, not just whether it was slow or expensive.
- It targets , tool-using agents, and .
- It aims to identify problems like poor retrieval, wrong , or drifting away from the goal.
- The main product question is whether this should be a standalone tool or a feature inside existing tools.