arXiv:2605.31308v1 Announce Type: new Abstract: Agent benchmarks increasingly record rich interaction trajectories, yet evaluation often reduces each rollout to a pass rate or reward score. We introduce TraceGraph, a graph-based framework that turns released multi-model agent trajectories into shared decision landscapes. For each task, TraceGraph builds a graph over observable action-observation states from pooled rollouts before model identity is introduced. It then overlays outcome-informed productive cores and trap regions, and summarizes each rollout with three events: Access, Trap exposur
The proliferation of AI agents and the increasing complexity of their interactions necessitate more robust evaluation and diagnostic tools.
Improving the diagnosability and interpretability of AI agent behavior is crucial for their reliable development and deployment across various applications.
TraceGraph introduces a standardized, graph-based method for analyzing agent trajectories, moving beyond simple pass/fail metrics to understand decision-making landscapes.
- · AI model developers
- · AI agent researchers
- · AI system evaluators
- · Enterprises deploying AI agents
- · Developers relying solely on black-box evaluation
- · Inefficient AI agent development cycles
More sophisticated and reliable AI agents can be developed and integrated into workflows.
Reduced errors and improved performance lead to faster adoption of AI agents in critical applications.
The ability to diagnose AI agent failures more effectively could accelerate progress towards Artificial General Intelligence.
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Read at arXiv cs.AI