Create a LangChain Deep Agents Harness Profile for NVIDIA Nemotron 3 Ultra to Improve Performance | NVIDIA Technical Blog
Jul 8, 2026

Summary
This post details the process of creating a LangChain Deep Agents harness profile for NVIDIA Nemotron 3 Ultra to improve agent accuracy without fine-tuning. It introduces the concept of harness engineering as an alternative to model fine-tuning, focusing on adapting the agent's interaction with the model. The post outlines a methodology involving running evaluation benchmarks, analyzing failures, proposing harness profile changes (e.g., adding middleware like ReadFileContinuationNoticeMiddleware to guide tool usage), and re-validating the improvements. It also discusses automating this harness engineering loop using agentic proposers and managed services like LangSmith Engine.
More from the Agentic AI Infrastructure Acceleration with BlueField DPUs trail
- Building Reliable Data Analytics Agents: Lessons from the KDD Cup | NVIDIA Technical Blog
- Validate AI Factory Changes with Digital Twins and AI Agents | NVIDIA Technical Blog
- Scaling Decision Optimization to 100 Million Variables and Beyond with mPDLP in NVIDIA cuOpt | NVIDIA Technical Blog
- Build Applications on NVIDIA BlueField Faster with NVIDIA DOCA Agent Skills | NVIDIA Technical Blog
- Tracing Agent Harness Behavior with NVIDIA NeMo Relay | NVIDIA Technical Blog