prospect-7ca677ed25d3 same action failing twice with identical TimeoutError — does revenue_worker have retry-without-backoff logic?: the five-day sprint that ships the fix

The cost is not the timeout; the cost is duplicated uncertainty

A prospect record such as prospect-7ca677ed25d3 failing the same action twice with an identical TimeoutError looks small at first. One row did not advance. One outbound action did not complete. One worker emitted a familiar exception. The expensive part is not the single timeout. The expensive part is that the system cannot immediately prove whether the second failure was a deliberate retry, a duplicate dispatch, a stuck lease, a race between workers, or a retry loop with no backoff.

That uncertainty burns revenue operations time in three ways. First, the prospect may be contacted twice, charged twice, reserved twice, or marked stale while the worker keeps trying the same non-productive action. Second, the queue loses a slot to work that is likely to fail again because nothing changed between attempt one and attempt two. Third, every later investigation starts with the same manual question: did revenue_worker intentionally retry, and if so, did it wait long enough, mutate the attempt metadata, and preserve idempotency?

The concrete cost is measured in blocked throughput and forensic drag. If a timeout path takes 60 seconds and the worker retries immediately, two identical failures consume two minutes of worker time while producing no new evidence. At modest concurrency that becomes a queue-wide tax. Ten prospects with the same pattern consume twenty minutes of aggregate worker occupancy. A hundred consume more than three hours. Worse, identical logs collapse the distinction between a transient network issue and a deterministic code path. When attempt two has the same action name, same timeout type, same duration, same payload shape, and no recorded delay, it is not a retry strategy. It is a missing decision record.

Milo treats this symptom as a systems problem, not a blame problem. The right target is a deterministic failure pattern that can be read from code, reproduced in a harness, and fixed with guardrails. The question is not merely whether revenue_worker has retry-without-backoff logic. The question is whether the worker can prove, for every retried action, why the retry happened, how long it waited, what changed before the next attempt, and what prevents duplicate side effects.

Start with a failure fingerprint, not a stack trace

A stack trace explains where the timeout surfaced. A failure fingerprint explains whether two failures are actually the same event class. For prospect-7ca677ed25d3, the first forensic pass should normalize each attempt into a compact fingerprint: prospect_id, action_key, worker_name, exception_class, timeout_ms, attempt_number, lease_id, job_id, idempotency_key, started_at, finished_at, and next_not_before.

The most important field is often missing: next_not_before. Without it, there is no durable proof that a retry was delayed. A log message such as retrying action does not count. The queue needs a stored timestamp that says the work is ineligible until a future time. If attempt one finishes at 10:00:30 and attempt two starts at 10:00:31, the system either has no backoff or has a backoff bug. If attempt two starts at 10:05:31, the retry may be reasonable, but the record still needs to show which policy selected five minutes.

A good fingerprint also separates duplicate execution from duplicate logging. If both failures share the same job_id and lease_id, the worker may have logged the same attempt twice or failed during cleanup. If the failures share the same job_id but have different lease_id values, the queue may have reclaimed a job while the first worker was still running. If they have different job_id values but the same idempotency_key, upstream scheduling may have duplicated the work but the side-effect layer may still be protected. If they have different idempotency_key values for the same prospect and action, the system has a stronger duplicate-side-effect risk.

The first implementation step is therefore not to tune timeouts. It is to add or verify a canonical event record. Each attempt should emit action_attempt_started, action_attempt_failed, and action_retry_scheduled events with the same correlation fields. The events should be written before logs are formatted, because logs are a view while attempt records are evidence. The target state is simple: two identical TimeoutError failures should either show a legitimate backoff gap or expose the absence of one in a single query.

The deterministic pattern to look for inside revenue_worker

The code smell usually appears as a broad exception handler wrapped around action execution. The worker pulls a job, calls a handler, catches TimeoutError, increments a counter, and requeues the job. The bug is not that it retries. Revenue actions need retries because vendors, browsers, APIs, and internal services time out. The bug is when retry scheduling is equivalent to immediate eligibility.

A risky shape looks like this in prose: except TimeoutError as exc, then job.attempts += 1, then queue.enqueue(job), then raise or return. If the queue uses a ready list, enqueue places the same job back where another worker can grab it immediately. If the queue uses a database table, the equivalent bug is status = 'pending' with no update to run_after. If the worker uses a lease, another version is lease_expires_at being shorter than the timeout itself, so the second worker starts before the first has conclusively failed.

The deterministic audit should inspect four code paths. The first is the claim path: claim_next_job(now) must filter on run_after <= now, status = 'pending', and an expired or absent lease. The second is the failure path: mark_failed(job, exc) must compute retry eligibility from a policy, not from a default queue insert. The third is the scheduler path: schedule_retry(job, delay) must persist run_after = now + delay atomically with attempt_number. The fourth is the terminal path: after max_attempts, the worker must stop returning the action to the pending pool and must record a final failure reason.

Backoff itself should be explicit and testable. A basic policy might be delay = min(base_delay * 2 ** (attempt_number - 1), max_delay), with deterministic jitter such as jitter = hash(job_id) % jitter_window. The deterministic jitter matters because random jitter is harder to assert in tests, while no jitter can create synchronized retries. The policy should classify errors. TimeoutError may get exponential backoff. ValidationError should usually be terminal. RateLimitError should honor a provider reset time when available. DuplicateActionError should mark the job complete or suppressed, not retry.

The most revealing unit test is not a happy path test. It is a clock-controlled failure test. Freeze time at T0. Arrange a pending job for prospect-7ca677ed25d3. Make the action handler raise TimeoutError. Run one worker tick. Assert that the job is not claimable at T0. Advance to T0 + delay - 1ms. Assert it is still not claimable. Advance to T0 + delay. Assert it is claimable with attempt_number = 2 and the same idempotency_key. That test turns retry-without-backoff from a suspicion into a binary property.

Idempotency is the guardrail that keeps retries from becoming duplicate actions

Backoff prevents hot-looping. Idempotency prevents side effects from multiplying. The two are related but not interchangeable. A job can wait five minutes and still send a duplicate message if the second attempt generates a new downstream key. A job can reuse an idempotency key and still waste capacity if it retries immediately. The fix needs both.

For prospect actions, the idempotency key should be derived from stable business intent, not from an execution attempt. A safe shape is idempotency_key = hash(prospect_id + action_key + campaign_id + intent_version). Unsafe shapes include hash(job_id + attempt_number), uuid() inside the handler, or a key generated after the side effect has already started. If the action is supposed to happen only once per prospect and campaign, every retry for that intent must use the same key.

The action boundary should make this hard to violate. Instead of allowing handlers to call external systems with ad hoc parameters, revenue_worker can pass an ActionExecutionContext containing prospect_id, action_key, attempt_number, idempotency_key, deadline_at, and correlation_id. The handler should not be responsible for inventing retry metadata. It should receive the metadata and attach it to every outbound request, database write, browser command, or internal event.

There is also a recovery detail that matters after timeouts. A timeout does not prove the remote side did nothing. It only proves the local worker stopped waiting. Before retrying an action with side effects, the worker should check an action ledger. The ledger can be a table keyed by idempotency_key with states such as started, committed, unknown, failed_retryable, and failed_terminal. If attempt one timed out after sending a request, the ledger state may be unknown. Attempt two should begin with reconciliation, not blind repetition. That reconciliation might query a provider, inspect a local outbox, or verify whether the prospect already advanced to the next state.

The timeout path should therefore record two facts separately: worker_result = timeout and side_effect_state = unknown. Combining them into failed hides the risk. A retry policy can safely retry failed_retryable. It should be more careful with unknown. In many revenue workflows, the next step after unknown is reconcile_before_retry, with a shorter handler that asks whether the previous side effect landed. That is how a timeout investigation becomes a correctness improvement rather than just a delay knob.

The five-day sprint that turns the symptom into a shipped fix

A focused sprint should be short enough to ship and strict enough to avoid speculative rewrites. The target is not to redesign all revenue automation. The target is to make identical timeout pairs explainable, prevent immediate retry loops, and preserve exactly-once business intent for prospect actions.

Day one is evidence capture. Build the failure fingerprint query and backfill enough recent attempts to find patterns around prospect-7ca677ed25d3. Identify whether identical failures share job_id, lease_id, and idempotency_key. Add missing attempt events if the current data cannot answer those questions. The deliverable is a timeline that distinguishes immediate retry, lease overlap, duplicate scheduling, and duplicate logging.

Day two is code-path mapping. Trace revenue_worker from job claim to handler execution to failure handling. Mark every place that can set status, run_after, attempt_number, lease_expires_at, and idempotency_key. The deliverable is a small map of state transitions, not a generic architecture diagram. Every transition should name the function that performs it and the invariant it must preserve.

Day three is the regression harness. Add clock-controlled tests for timeout retry delay, max-attempt terminal failure, lease expiration safety, and idempotency-key stability across attempts. The key test reproduces the pain point: same prospect, same action, two TimeoutError attempts. Before the fix, attempt two is claimable immediately or metadata is ambiguous. After the fix, attempt two is ineligible until run_after, and the retry event records the policy-selected delay.

Day four is the bounded implementation. Introduce a central retry policy if one does not exist. Replace direct requeue calls in timeout handlers with schedule_retry. Persist next_not_before or run_after atomically with the incremented attempt count. Preserve the original idempotency key. Add a reconciliation state for side effects that may have landed despite the timeout if the current ledger lacks that distinction. Avoid changing unrelated action handlers unless they bypass the central boundary.

Day five is rollout and operator visibility. Ship behind a narrow flag if the system supports flags. Log and store retry decisions with retry_policy, delay_ms, attempt_number, and eligible_at. Add a dashboard or query for same_action_same_error_within_backoff_window. The sprint is complete only when the original symptom can be classified automatically: legitimate delayed retry, duplicate dispatch, lease overlap, duplicate log, or terminal policy failure.

What good looks like after the fix ships

After the fix, the same scenario should no longer require debate. If prospect-7ca677ed25d3 times out on a revenue action, the attempt record should show a stable action identity, a stable idempotency key, and a retry policy decision. The next attempt should not be eligible until the stored delay has elapsed. If a second failure occurs, it should carry attempt_number = 2, a later started_at, and a clear link to the first attempt. Identical exception text is acceptable. Identical evidence is not.

The worker should also become easier to operate under partial outages. When a provider slows down, timeouts should spread out instead of forming a tight retry storm. When a lease expires early, metrics should show overlap instead of presenting two clean failures. When a handler creates a new idempotency key per attempt, tests should fail before production does. When a prospect lands in unknown side-effect state, the system should reconcile rather than blindly repeat the action.

The strongest sign of success is that the investigation path becomes boring. A query over the attempt table can answer whether revenue_worker retried without backoff. A unit test can prevent the behavior from returning. A ledger row can show whether a timeout left the business action committed, failed, or unknown. An operator-facing event can explain why the next attempt is scheduled for a specific time. No one has to infer policy from log spacing or stack traces.

There is still a residual risk to handle honestly. A local backoff policy cannot fix an external system that accepts a request and then loses its own idempotency state. A ledger cannot reconcile a side effect if there is no observable provider status. A deterministic retry delay cannot guarantee success if the timeout budget is lower than normal provider latency. Those are real limits. The improvement is that the system now names those limits and routes them into explicit states instead of hiding them behind repeated TimeoutError lines.

Ship the forensic loop, then ship the retry fix

The clean way to resolve this class of failure is a forensic loop: fingerprint the failure, map the state transitions, reproduce the duplicate timeout deterministically, patch the retry boundary, and verify that the original symptom becomes automatically classifiable. That loop is intentionally narrow. It does not expand autonomy, does not change revenue strategy, and does not require a broad rewrite of the worker. It turns one painful prospect incident into durable control over a failure mode.

For teams that need this shipped quickly, the right internal sprint is Agent Failure Forensics. It is the sprint that fits a case like prospect-7ca677ed25d3: same action, same TimeoutError, same uncertainty about whether revenue_worker retried without backoff. The output should be concrete: attempt evidence, regression tests, a central retry policy, idempotency preservation, and rollout visibility. The fix is not a louder alert. The fix is a worker that can explain and constrain its own retries.

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