A prospect research job that reports completion in 0.17s and claims to have added five prospects is not fast. It is a cost leak wearing the mask of throughput. Real prospect research has irreducible latency: queue claim, source lookup, page fetch, parsing, entity matching, evidence scoring, dedupe, persistence, and post-run quality checks. Even a hot-cache path should leave a trace: source identifiers, fetch counts, confidence distribution, rejection reasons, and timestamps that line up with the actual work. When the system produces a neat batch of five prospects in less time than a single network round trip, the right interpretation is not optimization. The right interpretation is that a circuit breaker, fallback, fixture, or stale replay path is being counted as production research.
The direct cost is bad data entering the revenue loop. Five phantom prospects can contaminate prioritization, waste follow-up cycles, and make downstream scoring look healthier than it is. The larger cost is worse: the system learns the wrong lesson. If the run ledger records prospect_research as successful, schedulers will keep feeding the lane. If dashboards aggregate the additions without evidence quality, the lane appears productive. If budget governors see cheap success, they may prefer the fake-fast path over slower but useful research. One bogus green check can create a loop where the operator does more of exactly the wrong thing.
The five-day fix is not a rewrite of the research stack. It is a deterministic sprint that turns the fake-out into an impossible state. A research completion must prove that research happened. A fallback must be labeled as a fallback. A circuit-breaker fast-fail must produce zero prospects unless it can attach current, source-backed evidence for every record. Any path that cannot satisfy that contract should end as blocked, degraded, or quality_failed, never as a clean completion with a suspiciously round batch of prospects.
The first day is a truth-surface pass, not a product brainstorming session. The sprint begins by collecting every place where prospect_research claims completion: run ledger entries, queue rows, worker summaries, research artifacts, prospect tables, and quality reports. The target is the exact transition where running becomes done and prospects_added becomes 5. That transition should have a single authoritative writer. If multiple writers can report success, the sprint records them by module, function, and output schema before changing behavior.
The key fields are simple. For every completed research job, the artifact must include started_at, finished_at, elapsed_seconds, sources_attempted, sources_succeeded, source_fetches, candidate_count, accepted_count, rejected_count, fallback_used, circuit_breaker_open, and evidence_paths. The implementation should not infer these after the fact from prose summaries. The worker should emit structured counters as it runs, and the completion handler should copy them into the final artifact. If the existing code stores only a natural-language summary, the sprint adds a structured sidecar rather than parsing text.
One failure mode is especially common: a function named like research_prospects() catches a timeout, calls fallback_prospects(), and returns a list of plausible records with no explicit degraded status. Another is a test fixture leaking into runtime because a path such as sample_prospects.json is used whenever an upstream provider returns empty. A third is a replay cache keyed too broadly, where an old successful batch is attached to a new work item. Observability has to distinguish all three. The artifact should make the origin visible with a field like result_origin whose valid values are narrow: live_source, cache_hit, fixture, fallback, manual_seed, or unknown. For production completion, fixture, fallback, and unknown are not acceptable origins.
The first-day deliverable is a short failing report, not a fix. It should reproduce one suspicious run and show the mismatch: elapsed_seconds=0.17, accepted_count=5, insufficient source_fetches, and missing or fallback evidence. That report becomes the regression target for the rest of the sprint.
The second day turns the diagnosis into a hard contract. The rule should be boring and mechanical: a job cannot report done with added prospects unless each accepted prospect has current evidence, a traceable source, and a validation decision. A prospect record is not just a name and a company. It needs at least one source_url or internal source identifier, a retrieved_at timestamp from the current run or an explicitly bounded cache window, extracted fields, a confidence score, and a reason it passed dedupe. If the system cannot provide that, it may still save candidates for later inspection, but it cannot count them as added prospects.
The completion handler should enforce this centrally. Do not scatter the rule across every fetcher and parser. The fetchers can be messy; the completion gate must be strict. A typical implementation adds a function such as validate_research_completion(result, run_context). It inspects the structured result before the queue item is finalized. If result.accepted_count > 0, then result.sources_succeeded > 0 must be true, result.evidence_paths must be non-empty, every accepted prospect must have evidence_ref, and result.fallback_used must be false. If result.circuit_breaker_open is true, the only valid terminal states are degraded_noop, blocked_upstream, or quality_failed.
The contract should also reject suspicious timing when it contradicts the claimed work. Timing alone is not proof of fakery, but it is a strong signal. A minimum duration rule by itself would be crude; a better rule is evidence-coupled. If elapsed_seconds < 1.0 and the result claims live additions, then the artifact must show a cache hit with valid per-prospect evidence from a recent, keyed cache. If the result claims live network research, then it must show real fetch counters. The goal is not to punish fast code. The goal is to prevent a fast-fail path from borrowing the success semantics of real research.
This is where the circuit breaker gets demoted to its proper role. A circuit breaker protects the system from cascading failure; it does not produce business facts. When it opens, it should return an explicit object: {status: "blocked_upstream", accepted: [], fallback_used: true}. It may include a recommended retry time, provider name, and breaker reason. It should not include five polished prospects. If a fallback generator is useful for demos, it belongs behind a test flag, a sandbox-only mode, or a separate synthetic_data lane. It does not belong in the production research success path.
The third day is the implementation cut. The order matters. Patch the write path first, because that is where false success becomes durable. The minimal safe change is to add a gate immediately before persistence and queue finalization. Pseudocode is enough to show the shape: validation = validate_research_completion(result, ctx); if invalid, write the artifact with terminal_state=quality_failed, set prospects_added=0, store the validation errors, and do not insert accepted prospects into the canonical table. This keeps bad data out while preserving evidence for debugging.
The persistence layer should support a distinction between candidates and accepted prospects. If the current schema has only one table, the sprint should add a low-risk sidecar artifact rather than perform a broad migration. Write rejected or uncertain records to a run-scoped artifact such as prospect_research_candidates.<run_id>.json. Only validated records enter the durable prospect store. This avoids the common trap where the system cannot debug bad records because it either accepts them or throws them away. The sprint needs the failed records visible, but not counted.
Next, patch the fallback origin. Search for code paths that create prospects without source evidence. The names are usually plain: fallback, mock, sample, seed, fixture, default_prospects, or safe_empty. The correct replacement is not to delete every fallback. Some fallbacks are operationally valuable. The patch is to change their return type and status semantics. A fallback may return diagnostics, retry hints, or synthetic candidates explicitly marked as synthetic. It may not return production additions. The decisive field is result_origin; once that field exists, the completion gate can refuse fake origins consistently.
Then patch the counters. The worker should increment sources_attempted before each source call, sources_succeeded only after a parseable response, and source_fetches only for actual retrievals. Cache reads need their own counter, such as cache_hits. The bug often hides because a fallback returns records and the summary says 5 prospects added without exposing that source_fetches=0. After the patch, that combination is impossible to miss. It either fails validation or reports as a cache-backed completion with the exact cache keys and evidence timestamps.
The fourth day is test coverage. The sprint should not begin with a broad integration test that depends on live web behavior. It should begin with deterministic unit and component tests that recreate the lie. The first test feeds the completion handler a result with elapsed_seconds=0.17, accepted_count=5, fallback_used=true, and no evidence. Expected result: quality_failed, zero durable inserts, validation error recorded. The test name should be direct, for example test_fallback_batch_cannot_complete_as_research_success.
The second test covers the circuit breaker. It sets circuit_breaker_open=true and provides a fallback list. Expected result: blocked or degraded status, no accepted prospects. This catches the exact class of bug where defensive infrastructure accidentally becomes a data producer. The third test covers a legitimate fast path: a recent cache hit with evidence references for every prospect. Expected result: completion allowed only if the cache key includes the research query, source identity, retrieval timestamp, and evidence payload. This prevents the sprint from creating a dumb duration threshold that blocks valid cached work.
The fourth test covers mixed results. If ten candidates are found and only three have evidence, only three may be accepted. The other seven should be written as rejected candidates with reasons such as missing_source, stale_cache, dedupe_collision, or low_confidence. The final summary must say accepted_count=3, not candidate_count=10. This matters because phantom prospect bugs often start as sloppy counting bugs. Candidate volume is not business value.
The fifth test covers replay protection. A previous run's evidence cannot be attached to a new completion unless the cache policy allows it and the artifact says so. The test should create an old artifact, run a new job, and verify that the old evidence either fails freshness or appears as a bounded cache_hit with explicit provenance. If a stale replay can still pass as live research, the system will eventually rediscover the same fake-out under a different timing signature.
The fifth day is operational hardening. The run can now be technically correct while still misleading at the reporting layer. If dashboards continue to rank lanes by jobs_done and prospects_added without quality state, the old incentive remains. The dashboard needs a small set of blunt metrics: accepted_with_evidence, fallback_completions, quality_failed_completions, zero_fetch_success_claims, and median_elapsed_seconds_by_origin. These are not vanity metrics. They expose whether the lane is producing research or just moving counters.
The most important alert is a contradiction detector. If a job reports accepted_count > 0 while sources_succeeded=0 and cache_hits=0, that is a hard failure. If a job reports fallback_used=true and accepted_count > 0, that is a hard failure. If a lane produces repeated completions below a subsecond threshold with identical accepted counts, that is at least a warning. A repeated 5 is especially suspicious because it often comes from page-size defaults, fixture length, or demo data. The dashboard should call that out as a pattern, not bury it in averages.
The reporting change should also alter scheduler input. A quality_failed research job should not be treated as productive just because it completed quickly. It should feed a retry policy or a repair queue, not the success ledger. If the upstream provider is down, the scheduler can wait, switch sources, or run a different lane. What it cannot do is reward the broken path. The sprint should make quality state part of lane scoring so that fake-fast completions reduce confidence rather than increase it.
The final verification run is straightforward. Execute one synthetic circuit-breaker scenario, one fallback scenario, one no-source scenario, and one valid evidence-backed scenario. Confirm that the first three produce no durable prospects and visible failure or degraded states. Confirm that the valid scenario persists records with evidence references. Then inspect the dashboard or generated report and verify that the bad runs are counted as bad runs. If the interface still makes them look green, the sprint is not done.
The fix for the prospect_research 0.17s fake-out is not more prompting, more enthusiasm, or a larger batch size. It is a stricter definition of done. Day one finds the exact phantom path and makes it observable. Day two defines the completion contract. Day three gates persistence and labels fallback origins. Day four locks the bug behind regression tests. Day five updates operations so fake-fast success is punished instead of rewarded. That sequence is deliberately conservative. It changes the truth contract before it changes the research strategy.
The expected result is not that every prospect research run becomes successful. The expected result is that failure becomes honest. A circuit breaker can still open. A source can still time out. A cache can still be stale. A parser can still reject half the candidates. The difference is that those outcomes no longer masquerade as five fresh prospects. The system either produces evidence-backed additions or it records why it did not. That is the minimum standard for an autonomous research lane that is supposed to compound rather than hallucinate progress.
The recommended internal sprint is None, because this repair should ship as a narrow reliability sprint instead of being hidden inside a larger initiative. The label is less important than the contract: no evidence, no accepted prospect; fallback used, no success claim; circuit breaker open, no fake batch. Once those rules are enforced, the research lane can be tuned for coverage and speed without poisoning its own scoreboard.
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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