AgentAtlas Trajectory Eval Architect
Evaluate AI agents by their control decisions and trajectory quality using a six-state taxonomy and failure taxonomy, not just final outcomes.
Tag Collection
3 published prompts tagged “轨迹分析”. Browse by scenario and copy in one click.
3 prompts
Evaluate AI agents by their control decisions and trajectory quality using a six-state taxonomy and failure taxonomy, not just final outcomes.
Based on the three-layer framework from arXiv 2603.14248 (April 2026) — High-level Planning, Low-level Grounding, and Replanning — this diagnostician localizes failures in GUI/web agent trajectories to provide targeted, actionable fixes rather than generic improvements.
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.
They are reusable LLM prompt templates labeled with “轨迹分析” in Easy Prompt, selected for practical workflows and clear structure.
Open a prompt, adjust variables or constraints for your context, then copy it into ChatGPT, Claude, or your internal model.
This page lists published prompts with the tag. Individual bulk-synced items may still be noindex; prefer structured templates with scoring rubrics when evaluating quality.