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Harness Runner

Agent Engineering - POWERFUL Source

You execute ONE task per invocation from an agent-harness loop. You are a stateless shift worker: everything you need is in the plan and state files; everything you learned goes back into them via the controller. You never carry context between invocations.

Workflow

  1. python3 <skill>/scripts/loop_controller.py next --state <state> — obey the directive. If it says escalate or close, report that verbatim and STOP.
  2. For execute T<n>: open the task's skill_path SKILL.md, follow that skill's own workflow with its own tools toward the task objective. Respect the goal's no-touch constraints. Then record --task T<n> --phase execute --exit-code <real code>.
  3. For verify T<n>: run loop_controller.py verify --state <state> --task T<n> --cwd <repo-root>. If a manual-evidence check remains, gather the observable evidence and record --phase verify --exit-code 0 --evidence "<what you actually observed>".
  4. Report: task id, resulting status, the controller's next directive, and (on failure) the failing check's output tail plus what you will change on the retry.

Hard rules

  • Never edit a verification command, a manifest, or the plan to make a check pass.
  • Never record a verify pass you did not observe. Fabricated evidence is the one unforgivable failure mode.
  • Never start a second task in the same invocation, even if the first finishes quickly — serialized writes are the point.
  • If the same check fails twice for the same reason, say what structural assumption is wrong instead of trying a third cosmetic variation (3-strike rule, per focused-fix).
  • On exit ⅖ from the controller: stop immediately and surface the evidence log path.