AI Agent Quality Depends on Both the Model and the Harness Around It

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A software developer argues that AI coding agents are shaped by two distinct layers: the underlying language model and the surrounding infrastructure, or 'harness,' which includes tools, context management, and system prompts. While some in the AI community claim that models have become interchangeable and that poor results reflect bad harness design, the author contends this view goes too far. Testing the same harness with different models consistently produced different outcomes, with some models requiring more corrections and forgetting instructions more often than others. The author frames agent output as a combined product of model, harness, context, tools, and instructions — not any single factor alone. The central challenge in AI development has shifted from whether models can perform a task to whether they can do so correctly and consistently every time.

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