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LangChain introduces Trajectories in LangSmith, a chronological view of agent sessions that simplifies debugging by aggregating messages from humans, AI, and tools in order for easier navigation and analysis.
The paper proposes EvalXRL, a benchmark for evaluating Explainable Reinforcement Learning methods by using an LLM coding agent to diagnose and fix bugs in RL agents.
A developer asks how others debug AI agents that make wrong decisions due to stale information, questioning the effectiveness of current tracing tools like LangSmith, LangFuse, and Phoenix.