This article explores how LangSmith traces can help developers debug and optimize coding agents like Claude Code, Codex, and Copilot. It details how tracing tool calls, subagents, and errors can uncover hidden issues in AI-assisted development workflows. By Hari Harish.
When building a CSV export feature using a coding agent, a developer encountered persistent errors due to a subagent using outdated pagination logic. This experience highlights the challenges of debugging AI-assisted development workflows where multiple agents interact and make autonomous decisions. LangSmith traces provide visibility into these interactions, enabling developers to pinpoint issues quickly and avoid costly trial-and-error debugging.
Few points to note:
- LangSmith traces reveal the full execution path of coding agents, including subagent interactions and tool calls.
- Tracing helps identify deprecated or misconfigured components that cause agent behavior to deviate from expectations.
- Debugging with traces reduces time spent on re-explaining requirements and accelerates problem resolution.
- Visualizing agent workflows improves understanding of how AI models interact with codebases.
- Traces provide insights into token usage, costs, and retry patterns that impact development efficiency.
LangSmith traces are an essential tool for developers working with AI-powered coding assistants. By providing deep visibility into agent behavior, they help reduce debugging time, improve code quality, and enhance the overall developer experience. This article is particularly valuable for DevOps engineers, software architects, and developers integrating AI into their workflows. Good read!
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