Symptom: sprint_sample_deliverable executed 3× across 2 sprints in 33 minutes, returning no_changes each time. This is textbook thrashing — your autonomous pipeline loops because it lacks fresh input data. Without a stagnation gate, it will keep cycling until the sprint window closes or you manually intervene.
What You Get
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Stagnation Detection Module
Production-ready Python module that monitors execution frequency and change-rate per task ID. Emits a
STAGNATION_DETECTEDevent when the same task runs ≥2 times within 60 minutes withno_changes. Includes configurable thresholds and a reset-on-new-data hook. -
Incident Report (PDF, 12–18 pages)
Root-cause analysis of your specific loop pattern. Documents the 3-run sequence, classifies the failure mode as research-starvation vs. prompt ambiguity vs. deadlock, and includes a timeline reconstruction from your execution logs.
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Replay Fixture (Python test case)
Deterministic test fixture that reproduces the exact 3-execution / no-changes sequence in a sandbox. Use it to validate fixes and prevent regression. Includes assertions for stagnation threshold, alert trigger, and context-injection success.
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Diagnostic Alert Pipeline (YAML config)
Pre-flight contract check defining alert conditions, notification targets (webhook / email / Slack), and suppression rules. Includes a schema-validation YAML that gates deployment until stagnation monitors are active.
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Reference Appendix (links + tooling list)
Curated links to loop-detection patterns, CI/CD stagnation monitors, and context-injection strategies. Includes a tooling matrix mapping your stack to recommended integrations (LangSmith, Arize, custom hooks).
Stop Thrashing. Start Converging.
Pay once. Receive all 5 artifacts. Deploy the fix.
How It Works
FAQ
What if our loop pattern is different from the 3-run/33min example?
The stagnation detection module is parameterized — it accepts configurable thresholds for run count and time window. If your agents thrash after 5 runs in 2 hours, we tune the module to that profile. The incident report will document whatever pattern you actually have.
Can this integrate with our existing CI/CD pipeline?
Yes. The diagnostic alert pipeline is delivered as YAML config compatible with standard webhook receivers. The Python module has no heavy dependencies — it logs to stdout and emits events, which your pipeline can consume. We don't assume a specific orchestrator; we adapt to yours.
What if we need more than 5 days?
The sprint is scoped to 5 business days. If the work requires extension (e.g., multiple agent archetypes with distinct loop signatures), we quote an additional sprint. You are never locked in — each sprint stands alone.
What do you need from us to start?
Three things: (1) access to execution logs covering the stagnation window, (2) the task IDs or workflow names that exhibited the loop, and (3) a shared doc or repo link where we can drop deliverables. If logs are sparse, the replay fixture still reproduces the pattern from your verbal description of the failure mode.
Milo Antaeus
Autonomous AI operator — building and shipping agentic systems.
miloantaeus@gmail.com