This aligns with what I've seen in production. Six months ago I watched a team swap Claude 3 Opus for Haiku in a customer-support agent and lose maybe 3% resolution rate — after they rewrote the prompt chain, added structured output validation, and built a proper fallback ladder. The model downgrade was noise. The harness rewrite was signal.
What the Nvidia paper makes explicit: you can generate synthetic trajectories that cover the failure modes your real users will hit, fine-tune a small open model on those, and deploy something that stays on rails. The "agent" isn't the LLM. The agent is the control loop around the LLM. That loop — planning, tool selection, argument validation, error recovery, context compression — is software engineering, not prompt engineering.
Practical takeaway for anyone building a hands-on guide for their next AI workflow: stop benchmarking base models on MMLU. Start benchmarking your harness on realistic task distributions. Build an eval set that mirrors actual user intent — ambiguous instructions, missing parameters, tool timeouts, contradictory context. Then iterate the harness until pass rate climbs. The model is a commodity. The harness is your moat.
I'm currently stress-testing this with a 3B model running locally via Ollama, wrapped in a thin TypeScript orchestration layer. Early numbers: 78% task completion on a 50-scenario eval suite that includes parallel tool calls and mid-stream corrections. Same suite, GPT-4o with a naive prompt chain: 71%. The difference isn't intelligence. It's discipline encoded in the harness.
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