Create a LangChain Deep Agents Harness Profile for NVIDIA Nemotron 3 Ultra to Improve Performance

Summary

The tutorial shows how to create and validate a LangChain Deep Agents harness profile for NVIDIA Nemotron 3 Ultra using an evaluation benchmark rather than fine-tuning the model. It starts with a baseline, analyzes failed trajectories, and iterates on prompts, exclusions, or added middleware while rerunning tests to detect regressions. One failure occurred when read_file returned the first page of a large file without signaling that more content existed, so the model answered before paginating to the line containing opal-fox-91. The proposed ReadFileContinuationNoticeMiddleware appends a continuation notice when a full page is returned, raising the reported benchmark score from 94/127 to 96/127 and resolving all three read_file tests. The source also describes automating this propose, verify, and retain-or-reject loop, with safeguards against overfitting and regressions.

Takeaways

1. 1

   The read_file failure occurred because a full first page contained no signal that the file continued, causing Nemotron 3 Ultra to answer before reaching the target line.

2. 2

   ReadFileContinuationNoticeMiddleware appends a notice when the returned line count reaches the per-read limit, directing the model to call read_file again with an increased offset.

3. 3

   The reported profile change improved the evaluation score from 94/127 to 96/127 and resolved all three failing read_file tests; repeated runs matter because the benchmark is stochastic.