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agentic-ai-engineering/course/LOOP-HARNESS-MVI.md

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Loop Harness MVI

Loop engineering is useful only after it becomes a harness:

  1. One bounded cycle: claim one task, run one worker, stop.
  2. Durable state: task status lives in JSON/DB, not agent context.
  3. Event log: every cycle appends JSONL evidence.
  4. Budget gate: token estimate blocks before work starts.
  5. Verifier gate: tests/review decide whether the next cycle may run.
  6. Human gate: pause on deploy, unknown failures, or budget spikes.

This repo's smallest working primitive is pipeline/harness_loop.py. It intentionally does not run agents itself; callers plug in Claude Code, Pi, Codex, OpenClaw, or local scripts after a task is claimed.

python -m pipeline.harness_loop \
  --tasks .runs/demo/tasks.json \
  --events .runs/demo/events.jsonl \
  --run-id demo \
  --max-tokens 20000 \
  --estimate-tokens 3000

Skipped for now: dashboards, Postgres, queues, schedulers, and multi-provider routing. Add those only after the JSONL spine proves the loop is worth running.