Expertise
Agent Harnesses
What an agent may touch, and how it proves what it did
Overview
A harness is the structure around a coding or content agent: the skills it can call, the hooks that run before and after its actions, the tools it is allowed, and the evidence it must return. The model sits inside it; the harness decides what the model can do.
Positioning
The quality of agent work is set by the harness more than by the model. A strong model in a loose harness produces confident, unreviewable output. A modest model in a tight one produces work a person can check.
What this means in practice
In practice this means writing skills that encode the repository's conventions, hooks that stop an agent before it drifts (a session-start check that local and remote have not diverged, for instance), tool allow-lists per task, and a required evidence format: tests run, files touched, open questions.
Why this matters
Teams that skip the harness end up reviewing everything by hand, which is slower than not using agents. Teams that build it review only what the harness could not settle.
What we deliver
Skills and hooks committed to your repository, with tests
Per-task tool and file allow-lists
A required evidence format for every agent run
Knowledge paths
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