traces

A developer builds a debugging tool for AI agents that compares replays against reference runs to identify where behavior first drifted, expressing frustration with manual trace debugging.

The tweet recommends an article on agent architecture in production, highlighting the use of Traces to diagnose issues and implement an iterative improvement loop.

Yohei Nakajima highlights that building an agent on activegraph automatically produces first-class traces, unlike bolted-on solutions, demonstrated with a coding agent experiment.

A tweet by Martin Casado highlighting a solution to the difficult problem of exposing traces at scale to AI agents, balancing cost and AI leverage.

Adam Łucek discusses how LangChain uses trace data to build evaluations for production agents.

Respan introduces an AI observability platform that automatically catches issues in traces, aiming to replace manual debugging for agent-based workflows.

Engine is a new tool that connects agent observability traces to automated fixes and evaluations, closing the agent improvement loop for engineering teams.

LangSmith Engine is an agent that sits on top of traces, automatically identifies issues, and suggests action items like code changes or evaluators to add.