Autonomous Agent Improvement with LangSmith Engine
Source
LangChain
Author
LangChain
Date
Why it matters
If you run agents in production and your improvement loop is ad-hoc, this lays out a repeatable lifecycle — flag failures in real traces, generate and test candidate fixes as experiments, then watch for regressions after deploy — with a runnable recruiting-agent example rather than abstract advice.
A LangChain Academy course that walks through the Agent Development Lifecycle (ADLC), showing how LangSmith Engine can autonomously flag errors in production agent traces, propose code fixes, validate them with test experiments, and monitor deployed changes for regressions using a sample recruiting agent.
Transcript
🎓 New LangChain Academy Course: Autonomous Agent Improvement with LangSmith Engine
In our latest course, we'll show you how to use Engine to:
✅ Identify and prioritize issues from traces
✅Draft fixes
✅Propose evals to prevent regressions
https://t.co/47ZLrxqU1R https://t.co/RoRDI7yukR
