Agent Harness Engineering
The article introduces the concept of 'harness engineering' in the context of AI agents, arguing that the scaffolding around a model is as important as the model itself. It suggests that building robust tools, feedback loops, and sandboxes is the key to preventing AI agent failures.
Why it matters
As AI development shifts from model-centric to agent-centric, the engineering of reliable 'harnesses' becomes critical for practical, long-running AI applications.
A coding agent is the model plus everything you build around it. Harness engineering treats that scaffolding as a real artifact, and it tightens every time the agent slips.
Roughly: anytime you find an agent makes a mistake, you take the time to engineer a solution such that the agent never makes that mistake again.
We’ve spent the last two years arguing about models. Which one is smartest, which one writes the cleanest React, which one hallucinates less. That conversation is fine as far as it goes, but it’s missing the other half of the system. The model is one input into a running agent. The rest is the harness : the prompts, tools, context policies, hooks, sandboxes, subagents, feedback loops, and recovery paths wrapped around the model so it can actually finish something.
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