Agent Harness

Most agents ship on vibes: someone tries eight prompts, the output looks good, it goes to production, and the next prompt tweak silently breaks a refusal nobody re-tested. This skill builds the harness around an agent so its behaviour becomes measurable — scenario suites with structural assertions, deterministic replay of recorded tool calls, paired regression diffing across prompt and model changes, and per-scenario cost and latency budgets. The tools here score an agent; they never invoke one, so they run offline on every commit.

When to use this skill

• An agent is going to production and the only quality evidence is manual spot-checking
• A prompt, tool schema, or model version is changing and you need to know what broke
• Two model or configuration options need a defensible comparison, not a demo
• An incident happened and you need the behaviour encoded as a permanent regression test
• Agent cost or latency is climbing across releases and nobody can point to when
• An existing eval suite reports a healthy pass rate that nobody trusts