A tutorial on creating a LangChain Deep Agents harness profile for NVIDIA Nemotron 3 Ultra to improve agent performance without fine-tuning. The approach involves running evaluation benchmarks to identify failures, writing custom middleware to fix them (e.g., a ReadFileContinuationNoticeMiddleware for pagination issues), and validating improvements against the full benchmark suite. The post also covers automating this process using a 'ralph loop' — an agentic self-correcting cycle that proposes, verifies, and rolls back harness profile changes — with a reference implementation available in the NemoClaw community repository. Results show benchmark scores improving from 94/127 to 96/127 with the middleware fix.
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