AI BriefingHow to Debug Coding Agents Using LangSmith Traces
Key point
LangSmith visualizes the execution process of various coding agents as standardized Traces, helping with debugging and performance improvement.
Details
When a coding agent generates incorrect code or behaves differently from what was intended, simply checking the output makes it difficult to identify the root cause. Understanding which tools the agent called and which subagent it gave incorrect instructions to is the core of debugging.
LangSmith integrates the execution processes of various fragmented coding agents such as Claude Code, Cursor, and GitHub Copilot Chat into a single Observability Layer. It converts the different data formats of each tool into a standardized Trace structure, so even when using multiple agents, you can analyze them in a consistent way.
The key information you can check through LangSmith's Trace includes the following:
- Conversation turns between User/Assistant
- Input, output, cache, token usage, and cost of Model Calls
- Tool Call, Shell Command, and MCP activity
- Subagent calls and error/retry history
- Execution time and metadata
Through this, developers can not only view the final result (Diff) but also reconstruct the entire workflow, such as how the agent modified the code after a test failure. Error patterns identified this way can be converted into agent Rules to improve future performance.
This summary was generated automatically by AI. Check the original for the author's claims and context. Copyright belongs to the original author.
Our guide explains how the AI works. Report summary errors, attribution issues, or removal requests via Contact.