concluding my 'taking deep agents to production' series with arguably the most important component: observability. when you deploy a deep ag…

concluding my "taking deep agents to production" series with arguably the most important component: observability. when you deploy a deep agent with LangSmith, you automatically get traces for every run: a full record of every LLM call, tool call, and middleware hook. for long-running agents, you can use agents like Polly, the LangSmith assistant, to reason over long traces and identify where a trajectory went wrong. traces are observational: they tell you what happened. time travel is experimental. built into the deep agent runtime, it's how you explore what an agent trajectory would look like if the agent had different context at some point. pick any checkpoint in a run's history, modify the state, and resume. the fork runs forward as its own branch, the original stays intact, and the full agent loop re-triggers. the combination of traces and time travel is powerful for the agent improvement cycle!

Source: Harrison Chase (X) | 2026-05-08