A failure labeled sprint_product_from_research no_eligible_research after market_research_deepening succeeded at 07:18 is not a cosmetic orchestration issue. It is a production cost leak. The upstream step spent time collecting, normalizing, and scoring research. The downstream step then treated the system as if no usable research existed. That means the pipeline paid the compute, latency, queue slot, and operator-attention cost of research without receiving the product work it was designed to unlock.
The concrete damage is easy to underestimate because both halves can look locally correct. The research action reports success. The product sprint action refuses to proceed because its eligibility query returns an empty set. If the only alert is the final no_eligible_research state, the incident looks like a data-quality miss. In practice, it is usually a contract miss: the research artifact exists, but the downstream reader cannot find it, cannot trust it, or filters it out because a required field does not match the expected shape.
The cost compounds across a five-day sprint. Day one loses its planning base. Day two re-runs research manually or triggers another deepening pass. Day three creates duplicate artifacts that disagree by timestamp or schema version. Day four adds defensive exceptions in the wrong layer. Day five ships a brittle patch that fixes the single observed row but leaves the state transition undefined. A better sprint treats the incident as an interface failure and ships a deterministic bridge between completed research and product-sprint eligibility.
The goal is not to make sprint_product_from_research more forgiving by guessing. The goal is to make the handoff auditable. A completed research run should produce a durable artifact with an explicit status, scope, schema version, readiness flag, and traceable run identifier. The sprint action should use one eligibility query, explain every exclusion reason, and fail closed only after it has proved that no artifact satisfies the contract.
This article frames the fix as a five-day technical sprint. It starts with the handoff contract, moves through deterministic discovery and filter instrumentation, adds regression coverage, and closes with rollout rules that keep live behavior bounded. The pattern is intentionally plain: define the artifact, index it, validate it, expose exclusion reasons, and test the exact 07:18 style case where upstream success did not become downstream eligibility.
The first day should not begin by changing the scheduler or weakening the downstream filters. It should begin by writing the exact contract between market_research_deepening and sprint_product_from_research. A contract turns a vague statement, research output not detected, into a finite set of checks. The output is either missing, stored in the wrong namespace, stored under the wrong run id, marked with the wrong status, missing readiness metadata, or filtered out by a deterministic rule.
A useful handoff contract has two surfaces. The first is the persisted research artifact. The second is the eligibility view consumed by the sprint action. The persisted artifact is the source of record; the eligibility view is the downstream interface. Mixing those two leads to hidden behavior because every consumer invents its own interpretation of readiness.
The persisted artifact should carry fields close to this shape:
research_id: stable identifier for the artifact, not the transient job id.source_run_id: the run that produced or last materially updated the artifact.action_name: expected to equal market_research_deepening for this path.completed_at: wall-clock completion time, including timezone or normalized UTC.scope_key: product area, market, segment, or other routing key used by the sprint action.schema_version: explicit version used by validators and migrations.readiness_state: one of draft, eligible, consumed, or rejected.quality_score: numeric value only if the downstream filter uses it.exclusion_reasons: populated when the artifact is not eligible.The eligibility view should be even smaller. It should not expose every research field. It should expose only what the sprint action needs: research_id, scope_key, completed_at, readiness_state, quality_score, and a validation summary. If the sprint action needs more, that need should be made explicit rather than smuggled through ad hoc JSON parsing.
The most important day-one decision is to reject implicit success. A job can succeed while producing no eligible artifact. That may be valid. A job can also succeed while producing an eligible artifact that is invisible downstream. That is the bug class. The contract must distinguish job_succeeded_no_artifact, artifact_created_not_ready, artifact_ready_not_indexed, and artifact_indexed_but_filtered. Without those states, the system collapses multiple failure modes into no_eligible_research, and every investigation starts from zero.
Once the contract exists, day two turns discovery into a deterministic function. The downstream action should not search several folders, tables, or state files in priority order unless that priority order is documented and tested. It should not infer the latest artifact from file modification time if a completed timestamp exists. It should not silently cross scopes because a query returned nothing in the requested scope. Determinism is what makes the 07:18 incident reproducible.
A clean discovery function has one input object and one return type. The input contains the action name, scope key, minimum completion time if relevant, and accepted schema versions. The return type contains candidates and exclusions, not just the winning candidate. In pseudocode, the control flow should read like this:
candidates = list_research(scope_key, producer="market_research_deepening", schema_versions={"2026-05"})
validated = [validate_candidate(c) for c in candidates]
eligible = [v for v in validated if v.readiness_state == "eligible" and v.quality_score >= threshold]
return EligibilityResult(selected=pick_latest(eligible), exclusions=validated)
The exact implementation language matters less than the separation of responsibilities. list_research is allowed to know storage layout. validate_candidate is allowed to know schema and readiness rules. pick_latest is allowed to rank eligible artifacts. The sprint action should not reimplement any of those details inline. Inline discovery logic is how a storage path change in the research module becomes a silent downstream miss.
Namespace safety is the common hidden cause. The research step may write to a run-scoped path such as runs/2026-05-04T071800/research.json, while the downstream step reads from a product-scoped index such as research/by_scope/<scope_key>.json. If the index update is not part of the success condition, the upstream action can honestly report success while the downstream action honestly reports no eligible research. Both are telling the truth about different surfaces.
The sprint should therefore add an explicit indexing phase. The phase can be small, but it must be observable. After the research artifact is written, the system validates it, writes or updates the eligibility index, and records an index event keyed by research_id. The upstream action is not complete for downstream purposes until that event exists. If the index write fails, the research run can still preserve the artifact, but the handoff status should be artifact_ready_not_indexed, not generic success.
Day three should remove the ambiguity from no_eligible_research. A downstream action that returns an empty set should also return the exact reasons every candidate was excluded. This is not verbose logging for curiosity. It is the minimum evidence needed to tell whether the fix belongs in research production, indexing, eligibility filtering, or sprint routing.
The implementation pattern is an eligibility evaluator that accumulates named checks. Each check has a stable key, a pass or fail result, and a short machine-readable detail. For example, schema_version_accepted, readiness_is_eligible, scope_matches_request, quality_above_threshold, not_already_consumed, and freshness_window_valid. The evaluator should not stop at the first failure unless the candidate cannot be parsed. Full exclusion data turns one failed run into a complete diagnosis.
A candidate report can be represented as a compact structure:
{"research_id":"r_123","eligible":false,"checks":[{"key":"scope_matches_request","ok":true},{"key":"readiness_is_eligible","ok":false,"detail":"readiness_state=draft"}]}
That structure is more useful than a paragraph log because it can be tested. The regression test can assert that an artifact with readiness_state=draft is excluded for exactly that reason. The incident dashboard can group failures by check key. The downstream action can include the top exclusion reason in its terminal state without leaking implementation details into the scheduler.
The evaluator also prevents accidental broadening. When teams patch this class of bug under time pressure, the tempting change is to treat completed, success, or missing readiness as eligible. That may get the next run moving, but it converts an observability problem into a product-quality problem. A named check makes the tradeoff visible. If the sprint chooses to accept readiness_state=completed as eligible, that is a deliberate contract migration, not a hidden conditional.
The 07:18 case should become a fixture. Store a small artifact that mirrors the failure shape: research run succeeded, artifact exists, downstream query returns no selected item. Then add one expected exclusion report for each possible reason. If the artifact was not indexed, the test should assert artifact_ready_not_indexed. If it was indexed under the wrong scope, assert scope_matches_request failed. If it used an old schema, assert schema_version_accepted failed. The test should fail if the system only says no_eligible_research.
By day four, the sprint has enough evidence to ship a compatibility shim safely. The shim is not a guesser. It is a bounded adapter that maps known old research artifacts into the current eligibility shape. Its job is to unblock valid artifacts produced during the transition while still rejecting artifacts that lack required meaning.
A safe shim has three rules. First, it is version-gated. It only handles known legacy schema versions or known missing fields from a defined time window. Second, it emits a migration note as part of the eligibility report. Third, it does not invent business-critical values. It may map status=success plus a present validated payload into readiness_state=eligible if the old contract documented that equivalence. It should not create a quality_score from nothing if the threshold depends on that score.
The adapter can be implemented as a pure function:
normalized = normalize_research_artifact(raw_artifact, accepted_versions, compatibility_window)
report = evaluate_eligibility(normalized, request)
The pure-function boundary matters. It allows unit tests to cover legacy and current artifacts without booting the scheduler. It also keeps writes out of the adapter. If the system wants to backfill indexes, that should be a separate repair command with a dry-run mode, not a side effect of reading eligibility.
For the specific incident class, the shim often needs to reconcile three names for the same concept: job success, artifact completion, and sprint readiness. They should not be collapsed permanently. A research job can finish successfully after deciding the market evidence is too weak. That should not start a product sprint. A research artifact can be complete but still await scoring. That should not start a product sprint either. Only the explicit readiness state should be consumed by the sprint action. The shim can bridge historical artifacts, but new writes should use the explicit state.
Day four should also add a repair command for indexes. The command scans persisted artifacts, runs the same normalization and eligibility evaluation used by the sprint action, and prints planned index changes before writing. The write mode should require a flag such as --apply. The output should summarize counts by outcome: indexed, already indexed, rejected, parse_failed, and skipped_by_scope. That command is the operational answer when another green research run appears invisible. It gives Milo a safe way to repair state instead of re-running research blindly.
Day five turns the fix from a patch into a protected behavior. The minimum regression suite should include unit tests for normalization, eligibility evaluation, index discovery, and the terminal action state. Each layer catches a different class of future breakage. If only the full action test exists, failures are slower and harder to diagnose. If only unit tests exist, the system can still wire the pieces together incorrectly.
The core tests should be table driven. Each row provides a raw artifact, a sprint request, and expected eligibility outcome. Good rows include a current eligible artifact, a current draft artifact, a legacy artifact accepted by the shim, a legacy artifact outside the compatibility window, an artifact with the wrong scope key, an artifact below quality threshold, and an artifact already marked consumed. For each row, the test should assert both eligible and the named check results. That prevents silent changes to exclusion semantics.
The integration test should reproduce the original sequence. First, write a research artifact as the deepening action would. Second, run the indexing phase. Third, invoke sprint_product_from_research with the matching scope. Fourth, assert that the selected research_id is the artifact produced by the upstream run. Then run the negative variant: skip indexing or corrupt the scope, invoke the sprint action, and assert that the terminal state includes the structured exclusion summary. The result should never be an unexplained no_eligible_research.
Rollout should be gated behind read-only observation before behavior changes. In observe mode, the new evaluator runs beside the current path and records what it would have selected. If the current path says no eligible research and the new path finds one, that is a candidate rescue. If the current path selects one and the new path rejects it, that is a compatibility risk. A small diff report over recent runs is more valuable than a confident guess.
After observation, enable the new path for a narrow scope or dry-run sprint class. Keep a rollback switch that restores the old eligibility reader while leaving the new logging in place. The rollback is important because eligibility touches product action creation. The sprint should improve detection and explainability first, then selection behavior. If selection behavior changes, it should be attributable to a specific contract rule, not to a broad rewrite.
The durable fix for sprint_product_from_research no_eligible_research after a successful 07:18 research deepening run is not another manual rerun and not a larger autonomous leap. It is a small contract sprint. Define the artifact. Index it deliberately. Evaluate it with named checks. Preserve exclusion reasons. Add a compatibility shim only where historical semantics are known. Protect the sequence with tests that reproduce the invisible-output failure.
This approach keeps the system honest. A future research run may still produce no eligible product opportunity, and that is acceptable. The difference is that the terminal state will say why. It will distinguish no artifact from not indexed, wrong scope from low quality, and draft from consumed. The downstream action will stop turning missing evidence into a mystery.
For Milo, the right recommendation is intentionally conservative: ship the handoff repair before expanding the product sprint surface. The internal sprint pointer for this article is None, because the work is not a new product bet. It is a reliability correction that makes existing research usable by the action that already depends on it. Once the contract is visible and tested, the next five-day sprint can spend its time building product instead of proving that yesterday's research existed.
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