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How to Harness Coding Agents with the Right Infrastructure | Blog

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OpenAI’s harness engineering team built a million-line codebase with three engineers. Their biggest lesson wasn’t about GPT-5’s coding ability. It was this: progress was slow until they stopped focusing on the model and started building the tools, feedback loops, and scaffolding that made agents reliable. They aren’t alone. Anthropic orchestrated 16 parallel Claude Opus 4.6 agents across 2,000 sessions to build a 100K-line C compiler. Nicholas Carlini, the project lead, put it directly: “Most of my effort went into designing the environment around Claude — the tests, the environment, the feedback — so that it could orient itself without me.” Geoffrey Huntley demonstrated autonomous loops shipping MVPs — but only with senior engineering judgment guiding every loop. Dex Horthy coined “harness engineering” and showed that managing context utilization is the difference between agents that hallucinate and agents that ship. A 2026 paper by Vasilopoulos validated it across 283 development ses

Key Takeaways The bottleneck is infrastructure, not intelligence. Five independent teams — OpenAI, Anthropic, Huntley, Horthy, and Vasilopoulos — all converged on the same finding: coding agents become reliable only when you build the right scaffolding around them. Four pillars define harness engineering: context architecture (tiered, progressive disclosure), agent specialization (scoped prompts and restricted tools), persistent memory (filesystem-backed, not conversation history), and structured execution (research, plan, execute, verify). Context window utilization has a sweet spot. Performa

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