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Greptile Summary

This PR ships a two-layer DB-evidence pipeline targeting the 50.6% DB-localization share of Runtime opensre+llm losses: a dependency-traversal planner rule added to the investigation agent's system prompt (Layer 1), and a deterministic B1 investigation-handoff post-pass on top_3_predictions that promotes rank-2 when investigation prose better supports it (Layer 2). It also amends the locked exp_structured_outputs_v1.yml pre-registration to replace the conceptually-flawed OBJECT_HIT_RC_MISS share gate with direct mechanism checks and fixes the previously inconsistent stopping_rules.null_result reference.

• investigation_handoff.py: New module implementing token-overlap scoring with a _MIN_PROMOTION_SCORE = 2 threshold, a conclusion-lines-only object gate to prevent spurious cross-object promotion, and a two-stage apply_investigation_handoff pipeline (B1 align → conservative rerank). B1 is correctly gated to predictor_variant == "default" in adapter.py so control arms and the rejected structured variant remain unaffected.
• exp_structured_outputs_v1.yml: Replaces the object_hit_rc_miss_share_max gate with mysql_confusion_reduction_pct ≥ 80%, lowers admission_a1_min from 0.55 → 0.40 to acknowledge the documented namespace_*_quota_exceeded regression, and aligns stopping_rules.null_result with the amended decision_rules — resolving the previously flagged contradiction.
• cloudopsbench_db_pod_logs_smoke_openai.yml: New 40-case three-arm smoke config with a joint mechanism gate (A@1 lift + b1 planner fire rate + b2 handoff fire rate) and a Fargate-only execution policy for provenance integrity.

Confidence Score: 5/5

Safe to merge. All changes are in the benchmark evaluation layer, with no production code paths touched. The B1 handoff is correctly gated and the pre-registration amendment resolves previously flagged inconsistencies.

The core logic in investigation_handoff.py is deterministic, well-tested across 8 scenarios including both cross-object promotion paths, and gated so it only applies to the default predictor variant. The pre-registration amendment resolves the previously noted stopping_rules contradiction. The two style-level observations (unreachable guard, substring false-positive risk) have no impact on correctness for the expected real-world data distribution.

No files require special attention. investigation_handoff.py has a minor dead-code guard and a low-risk substring matching note, neither of which affects correctness in the targeted failure class.

Important Files Changed

Flowchart

%%{init: {'theme': 'neutral'}}%%
flowchart TD
    A[Investigation run] --> B{predictor_variant == default?}
    B -- No --> C[Use top_3_predictions as-is]
    B -- Yes --> D[emit_paper_predictions]
    D --> E{payload is None?}
    E -- Yes --> F[Return run unchanged]
    E -- No --> G[apply_investigation_handoff]
    G --> H[align_predictions_to_investigation
B1: score each prediction
vs investigation prose]
    H --> I{best_alt strictly outscores rank-1
AND score >= _MIN_PROMOTION_SCORE?}
    I -- No --> J[rerank_predictions_by_evidence
conservative gate]
    I -- Yes --> K{object_gate_allows_promotion?
check conclusion lines only}
    K -- No --> J
    K -- Yes --> L[Promote alt to rank-1
rewrite rank + fault_taxonomy]
    L --> J
    J --> M[enriched_diagnosis with final top_3]
    C --> M
    M --> N[Return updated run]

_{Reviews (4): Last reviewed commit: "fixed issues" | Re-trigger Greptile}

@greptile review

@greptile review

🎯 Bullseye. @YauhenBichel opened a PR, kept the vibes clean, and got it merged. Absolute cinema. 🎬

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