Sprint Product
Your autonomous loop is logging high-productivity sessions as NOOPs. A race condition between your trajectory analyzer and bandit updater is corrupting 30% of session outcomes—533 of 1,763 sessions misclassified, sabotaging Thompson sampling and task selection.
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Three artefacts in JSONL format: (1) your bandit updater outcome log with NOOP records, (2) trajectory analyzer session grades, and (3) dispatch trigger events from your workflow_dispatch or dry_run configuration. If logs are anonymized or sampled, provide at least 200 sessions with mixed NOOP and non-NOOP outcomes for statistical significance.
Yes. The audit is purely analytical—it operates on exported log snapshots and does not touch your production pipeline. The artefacts you receive (replay fixture, reconciliation playbook) are read-only tools you can validate in a staging environment before touching production.
The Python fixture is the canonical reference implementation. The logic is plain enough to port to Node.js, Go, or Rust within hours. The implementation guide also includes pseudo-code for the synchronous handshake pattern so you can adapt to your language of choice.
The incident report will document the actual root cause regardless. If the data points to a different mechanism (e.g., block-throttle misclassification, trajectory grade inflation), the reconciliation playbook and implementation guide will target that mechanism instead. You receive the artefacts regardless of which failure mode is confirmed.