Tune the Harness, Before Tuning the Model with LangChain | Nemotron Labs

When an agent fails, the instinct is to fine-tune the model. Often the actual bug is in the harness around it — the prompts, tool descriptions, and middleware the model never sees during training. This live tutorial shows how to fix that instead: run an eval, find a real failure, patch it with a harness profile change, then validate the fix holds up. We'll do this end to end on LangChain Deep Agents and NVIDIA Nemotron 3 Ultra, with a guest engineer from LangChain. What you'll learn: - How to run the LangChain Deep Agents eval benchmark against a model and read the failure traces - How to write and register a harness profile change (middleware, prompts, tool descriptions) to fix a specific failure - How to validate a harness change against a hold-out set so you don't overfit to your evals - How to run the agent inside NVIDIA OpenShell using the NemoClaw blueprint for LangChain Deep Agents Bring your questions about harness debugging or evaluation design — our guest from LangChain will be answering live.