When the same language model is plugged into different agent frameworks, results often vary dramatically. This variance usually stems not from the model itself but from the harness—the surrounding system that defines prompts, tool schemas, memory, and execution loops. Engineers frequently misattribute failures to the model when the real culprit is a poorly designed harness. This article proposes a clear boundary: the model is responsible for reasoning and generation, while the harness controls context, tool access, and interaction patterns. By systematically testing each layer in isolation, teams can pinpoint bottlenecks and improve agent reliability. For example, if a model fails on a task in one framework but succeeds in another, the issue likely lies in how the harness formats instructions or manages context windows. This mental model is essential for anyone building production-grade agents, as it prevents wasted effort on fine-tuning when the fix is a harness adjustment. The principles apply across frameworks, making this a durable reference for agent architecture discussions.
A practical framework for debugging LLM agent performance by separating model capabilities from harness design choices.