An autonomous_loop failure where prospect_qualification_deepening is selected and then cooldown-blocked looks small in isolation. No state is corrupted. No bad outreach is sent. No account balance moves. The operator simply decides on the next useful action, checks the guardrail, discovers the action is still cooling down, and yields. That sounds safe. It is safe in the narrow sense. It is also expensive, because the loop has converted its decision engine into a repeatable no-op generator.
The cost compounds in three places. First, each cycle burns scheduler time, model budget, and log volume without advancing the revenue surface. Second, it erodes observability because every failed cycle emits plausible control-plane language: decision made, cooldown respected, dispatch skipped. The incident does not present as a crash; it presents as a disciplined system doing nothing. Third, it makes autonomous supervision less trustworthy. The system can appear active while the actual work queue remains stranded behind a gate that should have forced a deterministic fallback.
The broken pattern is precise: the loop scores prospect_qualification_deepening as the right next dispatch, the dispatcher checks the action cooldown, the cooldown blocks execution, and the next cycle recomputes the same decision because the scoring layer has not learned that the selected route is temporarily unavailable. The loop therefore stalls by design. The bug is not that cooldowns exist. The bug is that eligibility is checked too late and does not feed back into planning.
A competent fix does not remove the cooldown and does not loosen the safety rule. The correct fix makes availability part of action selection, records why the preferred action was excluded, and routes the loop toward the best eligible alternative. If there is no eligible alternative, it emits a bounded idle record with the next wake time and the exact blocker. That is the difference between a safe pause and a stranded control loop.
The repeat stall-cycle usually emerges when the loop is split into clean but incomplete layers. The planner chooses intent. The dispatcher enforces cooldown. The recorder persists the result. Each layer is reasonable on its own, but the composition is wrong because the planner is allowed to choose actions that the dispatcher already knows cannot run.
The anti-pattern looks like this in simplified form:
candidate = planner.rank(context).first()
if cooldown.blocked(candidate.name): return DispatchSkipped(candidate, reason='cooldown')
return dispatcher.run(candidate)
This code is deterministic, legible, and still defective. Once prospect_qualification_deepening wins ranking, every subsequent loop sees nearly identical context. The same prospect backlog exists. The same qualification gaps exist. The same strategic objective exists. The planner picks the same candidate again. The cooldown check blocks it again. Nothing in the context changes except another skip record, and if the scoring function does not penalize or exclude cooldown-blocked actions, the skip record is inert.
The defect becomes worse when the loop uses language-model reasoning upstream of the dispatcher. A model may repeatedly explain that deepening qualification is the best next move because, semantically, it is. The model is not wrong about value; the control plane is wrong about eligibility. The fix belongs in deterministic code, not in a longer prompt asking the model to be more careful. Prompts can describe the rule, but prompts are not the enforcement boundary.
A strong implementation moves from rank then block to filter, rank, dispatch. The loop should build an action set with explicit fields:
name: stable action identifier, such as prospect_qualification_deepening.value_score: estimated usefulness if the action could run now.eligibility: deterministic result from cooldown, dependency, budget, and risk checks.blocked_until: timestamp when a temporary block is expected to clear.block_reason: machine-readable reason such as cooldown_active.fallback_class: the class of actions allowed to substitute without changing risk posture.Then selection becomes explicit:
eligible = [a for a in actions if a.eligibility.allowed]
selected = rank(eligible).first() if eligible else idle_until(min_blocked_until(actions))
This small structural change prevents the control loop from pretending that a blocked action is the current dispatch target. The preferred action can still be visible in the trace, but it cannot become the only output unless it is eligible.
Cooldown is a policy primitive. It encodes pacing, duplication prevention, safety, partner constraints, or simple operational hygiene. If it lives only at the dispatcher boundary, the loop treats policy as an exception. That is backwards. Policy should shape the option set before deliberation spends effort choosing a route.
For this failure, the cooldown on prospect_qualification_deepening likely exists for a good reason. Qualification work can become repetitive. Rechecking the same prospect too often can create noisy notes, duplicate research, or low-value churn. The answer is not to weaken the cooldown. The answer is to let the loop see that the action is unavailable and then choose from adjacent useful work: enrich a different prospect segment, reconcile stale CRM fields, audit failed enrichment attempts, refresh account fit criteria, or prepare the next batch for qualification once the cooldown clears.
The availability model should be boring. It should not depend on a model deciding whether a cooldown feels important. A deterministic function should produce one of a few states:
allowed: the action may dispatch now.blocked_temporary: the action is unavailable until a known time or condition.blocked_dependency: the action requires missing data, credentials, approvals, or upstream outputs.blocked_risk: the action crosses a safety or authorization boundary.disabled: the action has been removed from the active routing set.The key detail is that only allowed actions are candidates for dispatch. Temporary blocks can contribute to scheduling; dependency blocks can create diagnostic tasks; risk blocks can escalate or remain inert according to policy. But none of them should be selected as if they are executable work.
This also improves incident language. A weak loop says, decided prospect_qualification_deepening; skipped due to cooldown. A strong loop says, excluded prospect_qualification_deepening because cooldown_active until 2026-05-05T09:30:00Z; selected crm_staleness_reconciliation as fallback. If no fallback exists, it says, no eligible actions; idle until 2026-05-05T09:30:00Z. Those records are operationally different. One hides a stall. The other explains a controlled wait.
The five-day sprint should ship a narrow change: add an eligibility snapshot before ranking, require the selector to choose only from allowed actions, and add fallback routing for the specific class that contains prospect qualification. This is small enough to verify and large enough to stop the stall-cycle.
The snapshot is the center of the fix. Each loop tick should materialize the action universe into a record that can be inspected after the fact:
ActionEligibility(name='prospect_qualification_deepening', allowed=False, reason='cooldown_active', blocked_until='...', fallback_class='prospect_ops')
This record should be persisted with the loop tick, not merely logged as text. Text logs are useful for reading. Structured records are useful for tests, dashboards, regression checks, and later forensic queries. The selector should receive only the snapshot, not raw cooldown internals, so the rule remains centralized.
A minimal selector contract is:
select_next(snapshot, context) -> DispatchDecision
The returned decision should distinguish four cases:
dispatch: an allowed action was selected and can run now.fallback_dispatch: the top value action was blocked, but an allowed fallback was selected.idle_until: no action is eligible, and the loop has a deterministic next wake time.escalate: all valuable work is blocked by dependency or risk states that require external intervention.The fix should also include a ranking invariant: a blocked action may be the top desired action, but it may not be the selected dispatch action. That invariant should appear in code comments, tests, and event schema. If the action is blocked, it belongs in preferred_but_blocked, not in selected.
Fallback routing should remain conservative. The system should not jump from blocked qualification work into unrelated high-risk behavior. It should use a bounded map:
prospect_qualification_deepening falls back to prospect_record_reconciliation.prospect_record_reconciliation falls back to qualification_gap_inventory.qualification_gap_inventory falls back to idle_until if all prospect operations are blocked.This map prevents thrash. It also prevents the loop from interpreting any available task as a valid substitute. A fallback is not merely an action that can run; it is an action that advances the same operating surface without violating the reason the original action was blocked.
The sprint is not complete when the code looks right. The sprint is complete when a regression test can force the old failure pattern and prove it no longer repeats. The canonical test should construct a context where prospect_qualification_deepening has the highest value score and an active cooldown. Then it should assert that the selected decision is not that action.
The first test is the exclusion invariant:
given top_action.cooldown_active == true, decision.selected.name != top_action.name
This catches the exact bug. It should fail against the old implementation and pass against the new one. The assertion should inspect structured decision fields, not a rendered log message.
The second test is fallback selection. Given an allowed fallback in the same class, the selector should choose it and record the blocked preferred action:
decision.type == 'fallback_dispatch'
decision.preferred_but_blocked.name == 'prospect_qualification_deepening'
decision.selected.name == 'prospect_record_reconciliation'
The third test is deterministic idle. If every action in the fallback class is blocked temporarily, the loop should not spin. It should emit idle_until with the earliest relevant unblock time:
decision.type == 'idle_until'
decision.wake_at == min(action.blocked_until for action in snapshot)
The fourth test is stall-cycle prevention across repeated ticks. Run three loop iterations with unchanged context and a still-active cooldown. The old behavior produces three skipped dispatches for the same selected action. The new behavior should produce either one fallback followed by changed context, or three bounded idle records with the same wake time and no repeated failed dispatch attempt. The assertion should be about absence of stranded dispatch, not about cosmetic log differences.
The fifth test is telemetry completeness. Every tick should include eligible_count, blocked_count, selected_action, preferred_but_blocked, block_reason, and next_wake_at where applicable. Without these fields, operators cannot distinguish healthy pacing from silent paralysis.
This fix fits a five-day sprint because the scope is narrow: one failure mode, one selector invariant, one fallback class, and one forensic trace. It should not become a general autonomy rewrite. The point is to stop a known stall-cycle and leave behind enough evidence to catch the next one faster.
Create a fixture that reproduces the stranded dispatch: prospect_qualification_deepening ranks first, cooldown is active, and the loop repeats the same skipped selection across multiple ticks. Capture the current event shape before changing code. This establishes the before-state and prevents the team from fixing a different problem.
Add the deterministic eligibility layer and persist the snapshot on each loop tick. Do not alter ranking logic yet except to pass through the new structure. The output of this day is visibility: the system can now explain which actions were allowed, which were blocked, why they were blocked, and when temporary blocks expire.
Change the selector so that blocked actions cannot be selected for dispatch. Add the invariant test. The preferred blocked action should remain visible in the decision record, but the selected action must come from the allowed set. If no allowed action exists, return idle_until or escalate, never dispatch_skipped for the same unavailable route.
Implement the fallback chain for the prospect operations class. Keep it explicit and small. This is not the day to invent a universal fallback engine. The acceptable output is a conservative route from blocked deep qualification into useful adjacent work, with no jump into unrelated risk surfaces.
Run the regression suite, inspect the generated decision records, and document the incident signature. The final artifact should include the old pattern, the new invariant, the fallback map, and the telemetry fields required to diagnose future stalls. If the system idles, it should say exactly why and until when. If it dispatches a fallback, it should say which preferred action was blocked and why the substitute is valid.
The wrong response to this incident is to lower the cooldown, add more prompt text, or manually kick the loop whenever it stalls. Those moves may clear one queue, but they leave the architecture intact: the planner can still select unavailable work, and the dispatcher can still convert the loop into repeated no-ops. That is not autonomy. It is a scheduler with a recurring blind spot.
The right response is forensic and mechanical. Make action eligibility a first-class input. Separate desired work from executable work. Preserve blocked preferences for auditability. Require fallback routing to stay inside a safe operating class. Emit idle decisions that carry a wake time instead of letting the loop spin. Then prove the old stall-cycle cannot reappear under the same conditions.
This is exactly the kind of failure that should be handled by the Agent Failure Forensics sprint. The sprint does not treat the symptom as a mystery and does not ask the operator to trust vibes. It reconstructs the failure path, pins the invariant that was missing, ships the smallest code change that closes the hole, and leaves behind regression evidence. For an autonomous_loop stranded on prospect_qualification_deepening because cooldown was enforced too late, that is the fix that matters.
Five business days, fixed price, full runbook on delivery. Sample deliverables on the sprint page show exactly what you get before you commit.
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