What is a priority inversion in an autonomous agent loop?

A priority inversion happens when an autonomous_loop dispatches a task with score=0.0 or on cooldown (e.g., twitter_value_post) while higher-scoring heads like market_research sit unprocessed. The loop is running but producing zero useful output. The Ai Agent Task Priority Debugger Sprint root-causes exactly this pattern in your specific codebase.

What does the $3,000 sprint deliver over 5 days?

Day 1-2: Full autonomous_loop trace analysis — sanitized logs, dispatch sequence reconstruction, and per-head state audit. Day 3: Root-cause isolation of the priority scoring or cooldown logic bug. Day 4-5: Refactoring artefacts — corrected prioritycomparator, updated dispatch rules, and a regression test suite. All artefacts are ready to drop into your codebase.

What format are the deliverables — prompts, code, or something else?

This sprint produces code-level artefacts: corrected prioritycomparator functions, updated dispatch rules with proper cooldown handling, and a regression checklist for your specific agent loop. It is not prompt engineering — it is root-cause debugging and refactoring for your autonomous loop implementation.

Is there a refund policy for a $3,000 debugging sprint?

All sales are final for custom debugging sprints. If the delivered artefacts do not match the described scope or cannot be integrated into your codebase, contact miloantaeus@gmail.com within 48 hours of delivery and the issue will be addressed.

Ai Agent Task Priority Debugger Sprint

Buyer pain point — confirmed via continuous research loop

Priority inversion bug: autonomous_loop is stuck in a cooldown-state polling pattern, repeatedly dispatching twitter_value_post (score=0.0, rate-limited) while 3 market_research heads queue up unprocessed. This wastes API calls, risks rate-limit penalties, and stalls research workflows. Known upstream causes include missing cooldown state checks, no priority queue ordering, and no loop-termination guard.

What you get

Root-cause incident report (PDF, 14–20 pages)

Step-by-step reconstruction of the priority inversion chain: how twitter_value_post re-enters the dispatch queue despite score=0.0, why cooldown expiry is never checked in the hot path, and which code path deprioritises the 3 market_research heads. Includes call-graph annotations and timeline of the infinite-loop entry condition.

Deterministic replay fixture (Python test suite)

A self-contained Python fixture that reproduces the exact dispatch sequence: twitter_value_post on cooldown, 3 pending market_research heads, and a loop counter. Run it before and after the refactor to assert that the bug is eliminated and the priority queue ordering holds across 10 000 iterations.

Refactored scheduler patch set (diff + PR-ready files)

A clean patch targeting autonomous_loop that replaces the linear scan with a priority-sorted work queue, adds an explicit cooldown-state guard before dispatch, and injects a no-progress iteration cap. Delivered as a unified diff and separate replacement files so you can apply or review the changes without touching unrelated code.

Pre-flight contract check (schema-validation YAML)

A YAML test suite that validates the patched loop's scheduling contract: market_research heads with score > 0 must be dispatched before any task on cooldown; cooldown expiry must gate re-entry; the no-progress cap must trigger a fallback to the highest-scoring pending head. Run it in CI or locally to enforce the contract on every deployment.

Reference appendix (tooling list + links + error-budget template)

Curated list of the tools used in the investigation: Python profiler commands, Autogen issue #108 (infinite-loop precedent), rate-limit backoff patterns, and OpenAI function-calling loop guard patterns. Also includes an error-budget template so you can set an SLO for autonomous-loop iteration count and alert on contract violations before they hit production.

FAQ

What repo access do you need?

Read access to the repository containing the autonomous_loop implementation, ideally with a branch the sprint can target. If access is not possible, a structured code dump with the relevant scheduler files is acceptable; the incident report and replay fixture will be based on that material.

What if the bug is in a proprietary or encrypted codebase?

The artefacts are designed around the scheduling logic, not the internal implementation of individual task agents. If you cannot share the full codebase, a minimised reproduction — the dispatch loop, priority scoring, and cooldown management sections — is sufficient for deliverables 1–4. Contact before purchasing if in doubt.

What does "1 round of revisions" cover?

One revision cycle means you can request clarifications or minor adjustments to any of the 5 deliverables — a corrected patch line, an expanded section in the incident report, or a modified test assertion. A second round of substantive changes beyond the agreed scope is quoted separately at $300/hr.

Does this cover hotfix deployment?

No — the sprint delivers the artefacts listed above (report, test fixture, patch, contract suite, and appendix). Applying the patch to your staging or production environment, reviewing it with your team, or acting on the error-budget template are outside scope. Those are natural follow-on engagements if needed.