Breaking the Deadlock: Implementing the ProofShare TaskRequestProofs Fix

In any distributed proving system, the most insidious bugs are not the ones that crash the program — they are the ones that silently freeze it, leaving no error message, no crash dump, and no obvious culprit. On a production Filecoin Curio node running the ProofShare protocol, such a bug had taken hold. The TaskRequestProofs task, responsible for polling a remote service for proof work and feeding it into the local proving pipeline, had locked up permanently. No new proofs were being generated. No error was being logged. The system was alive but completely frozen.

This article examines a single message ([msg 1919]) in the opencode session where the AI assistant transitions from planning to implementation, beginning to apply a set of surgical fixes to resolve this deadlock and several related issues in the ProofShare subsystem.

The Context: A Production Deadlock

The story begins with the user's report in [msg 1907], describing a deadlock in TaskRequestProofs. The core mechanism was a circular dependency between two components. The Do() method in task_request.go runs a polling loop that calls CreateWorkAsk to request new work "asks" (slots) from a remote proof service. The service, however, imposes rate limits — it may only allow 1–2 concurrent asks per provider. When CreateWorkAsk hits an HTTP 429 (Too Many Requests) response, it enters an infinite retry loop with exponential backoff capped at 5 minutes. The problem is that the polling loop cannot advance past CreateWorkAsk — it is stuck there indefinitely.

Meanwhile, the system has active asks that are being matched to work by the remote service. But the polling loop never reaches the next iteration where it would call PollWork to discover this matched work and insert it into the proofshare_queue database table. Without entries in proofshare_queue, the PSProve proving tasks never run, which means they never free up the service's ask slots. The service therefore continues returning 429 forever. The deadlock is complete and self-sustaining.

The user confirmed this diagnosis in [msg 1910]: restarting the locked-up node broke the deadlock because the fresh PollWork call on startup discovered the already-matched requests and inserted them into the queue, allowing proving tasks to finally execute.

From Analysis to Plan

The assistant spent several messages ([msg 1908] through [msg 1915]) in read-only exploration mode, tracing the exact code paths in task_request.go, provictl.go, and task_prove.go. The analysis produced a five-point fix plan:

  1. Fix 1 (Critical): Add an ErrTooManyRequests sentinel error to provictl.go and make CreateWorkAsk return it immediately on HTTP 429 instead of retrying forever.
  2. Fix 1b (Critical): Handle ErrTooManyRequests in task_request.go's Do() loop by breaking out of the ask-creation inner loop and falling through to the next poll iteration.
  3. Fix 2 (Medium): Clean up completed proofshare_queue rows older than two days to prevent unbounded table growth.
  4. Fix 3 (Medium): Scope the dedup SELECT service_id FROM proofshare_queue to only non-submitted rows.
  5. Fix 4 (Minor): Change orphan cleanup from DELETE to UPDATE to preserve fetched work.
  6. Fix 5 (Minor): Hoist address resolver creation out of the inner loop. However, the user pushed back on one aspect of the initial plan ([msg 1916]): "We still want exponential backoff if there were no asks created, correct?" This was a critical refinement. The assistant's initial proposal relied on the natural 3-second poll interval as backoff, but the user correctly identified that hammering a rate-limited service every 3 seconds would be abusive. The assistant refined the design in [msg 1917] to track whether any progress was made in a given iteration (new work inserted OR at least one ask created) and apply exponential backoff on the outer poll sleep only when no progress occurred, resetting to the fast 3-second cadence as soon as progress resumed. The user's response was a single word: "implement" ([msg 1918]).

The Subject Message: Implementation Begins

Message [msg 1919] is the assistant's response to that directive. It is the pivot point between analysis and action. The message contains two components: an "Agent Reasoning" block where the assistant organizes its approach, and a structured todo list that formalizes the implementation plan into discrete, trackable units.

The reasoning section reads:

"Let me implement the fixes. Let me create the todo list and start working through each fix."

This is deceptively simple. The assistant is making a deliberate architectural decision about how to structure the implementation work. Rather than diving into code changes ad-hoc, it creates a prioritized todo list that maps each fix to a file and a status. The first fix — adding the ErrTooManyRequests sentinel to provictl.go — is marked "in_progress" while all others are "pending." This communicates a clear execution order: fix the deadlock first, then address the secondary issues.

Why This Message Matters

On the surface, this message is merely a todo list. But it represents several significant decisions and assumptions:

Execution strategy: The assistant chooses to implement all fixes in a single batch rather than one at a time with user approval between each. This is appropriate given the user's explicit "implement" command and the fact that the fixes are independent but all touch the same subsystem.

Prioritization: The fixes are ordered by criticality and dependency. Fix 1 (the 429 sentinel) and Fix 1b (the backoff-aware handling in Do()) are the core deadlock resolution. Fixes 2–5 are important but not blocking — the system would function without them, just less efficiently or with more resource consumption. This ordering reflects sound engineering judgment.

Scope management: The assistant does not attempt to fix every issue in the ProofShare system. The todo list is bounded and specific. This prevents scope creep and keeps the implementation focused on the reported bugs.

Assumptions baked in: The assistant assumes that the deadlock analysis is complete and correct — that there are no other paths by which the system could deadlock. It assumes that the ErrTooManyRequests sentinel approach is the right semantic (rather than, say, a retry-with-cap approach in CreateWorkAsk itself). It assumes that the user's backoff refinement has been fully incorporated into the plan. These are reasonable assumptions given the preceding conversation, but they are assumptions nonetheless.

Input and Output Knowledge

To understand this message, a reader needs knowledge of several domains: the Go programming language and its error handling conventions; HTTP semantics, particularly the 429 status code and rate-limiting patterns; database query optimization (scoping SELECTs with WHERE clauses); the harmony task system's lifecycle (how tasks are created, polled, and cleaned up); and the ProofShare protocol's ask/request/response model.

The message creates new knowledge in the form of a structured implementation plan. It transforms the abstract understanding of the deadlock (gained through code reading and analysis) into concrete, actionable steps with file paths, function names, and SQL statements. This plan becomes the blueprint for the subsequent code edits.

The Thinking Process

The assistant's reasoning in this message is visible primarily through the structure of the todo list. The list reveals a methodical, engineering-minded approach: identify the root cause, design the fix, get user approval on the design, then execute in priority order. The assistant does not rush to edit code; it first organizes the work, ensuring nothing is forgotten.

The choice to mark Fix 1 as "in_progress" while leaving the rest "pending" is a subtle but important signal. It tells the reader (and the system tracking the session) that the implementation has begun but is not complete. This creates a natural checkpoint: if something goes wrong during Fix 1, the assistant can course-correct before touching the other files.

Conclusion

Message [msg 1919] is the moment when analysis becomes action. After a thorough investigation spanning multiple messages, code reads, and a design refinement prompted by the user, the assistant finally begins writing code. The todo list is the bridge between understanding and implementation — a structured plan that ensures each fix is applied in the right order, to the right file, with the right priority.

In the broader context of the opencode session, this message is part of a larger narrative about debugging distributed proving systems. The deadlock in TaskRequestProofs was not a simple bug — it was a systemic failure involving rate limiting, polling loops, database inserts, and task scheduling. Fixing it required tracing the exact sequence of events, understanding the service's behavior, and designing a fix that breaks the circular dependency without introducing new problems. The assistant's methodical approach — explore, analyze, propose, refine, implement — is a model for how to tackle complex production bugs in distributed systems.