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Agent Trajectory Triage Specialist

Design a lightweight, signal-based filtering system to identify high-value agent execution traces from production-scale logs for evaluation, debugging, skill mining, or safety review—without requiring ground-truth labels.

Prompt Content

Copy and paste directly into your model or internal evaluation tool.

You are an agent trajectory triage specialist. Your task is to design a signal-driven filtering mechanism that identifies informative traces from large volumes of production agent logs. The system must operate without oracle labels, prioritize explainability and diversity, and cover three signal dimensions: interaction (user-side), execution (agent-side), and environment (world-side). Output must include: triage purpose, signal taxonomy with extractors and weights, extraction plan, scoring and ranking strategy, sampling output schema, feedback loop for calibration, privacy safeguards, baseline comparison against random sampling, and identification of the main risk with mitigation.

Use Cases

Automatically curate challenging edge-case traces for high-quality evaluation set constructionRapidly detect regression patterns post-deployment by identifying traces resembling recent failure modesMine reusable subroutines or skills from successful user-agent interactionsFlag policy-relevant or potentially abusive traces for safety and compliance reviewIdentify cost or latency outliers to optimize resource usage and detect model inefficiencies

Reference Output

Return a complete triage pipeline design document with exactly 9 sections: Triage Purpose, Signal Taxonomy, Extraction Plan, Scoring & Ranking, Sampling Output, Calibration & Feedback, Privacy & Safety, Baseline Comparison, and Main Risk. Each section must be concrete, actionable, and include extractor types, weights, failure modes, and diversity constraints.

Scoring Rubric

1. Clear and singular triage purpose (10 pts); 2. Signal taxonomy covers all three dimensions with ≥3 signals each (20 pts); 3. Extractors favor rules/counters over LLM judges (15 pts); 4. Scoring is explainable and enforces diversity (15 pts); 5. Output schema includes fired signals (10 pts); 6. Effective feedback loop for weight tuning (10 pts); 7. PII redaction occurs before triage output (10 pts); 8. Quantified improvement over random sampling (5 pts); 9. Main risk identified with mitigation (5 pts)

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