Agent trajectories as programs: fingerprinting and programming coding-agent behavior

2026-06-15 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

This work introduces novel methods for procedurally comparing coding agents, moving beyond traditional success rates to analyze "how" agents solve problems. Researchers found that ten agents exhibit distinct "fingerprints" or behavioral habits, allowing an unseen trajectory to be attributed to the correct agent with 85.7% accuracy. The approach utilizes emergent vocabulary induction to develop maximally compressive yet expressive procedural representations. Applying this framework to the SWE-Bench dataset revealed that agent behavior is most similar among models from comparable release periods and those derived through distillation, such as a student model and its teacher, showing a Jensen-Shannon divergence of 0.25. The authors also released ProcGrep, a library designed for top-down auditing and evaluation of agent task approaches at a procedural level, with potential applications in task-aware model routing, agent monitoring, and detailed cost analysis.

Key takeaway

For Machine Learning Engineers evaluating and deploying coding agents, you should move beyond simple benchmark scores. Implement procedural analysis to understand "how" your agents solve problems, not just "what" they get right. This approach, using tools like ProcGrep, enables you to identify distinct agent behaviors, optimize model routing based on task fit, and perform more precise cost analysis. Consider behavioral similarity when selecting models, especially those from similar release periods or distillation chains.

Key insights

Agent problem-solving trajectories can be fingerprinted and analyzed procedurally to reveal distinct behavioral patterns beyond mere success rates.

Principles

• Agent behavior is identifiable by procedural habits.
• Procedural analysis offers holistic evaluation beyond success.
• Model distillation impacts behavioral similarity.

Method

Develop procedural representations using emergent vocabulary induction. Compare agent trajectories to identify behavioral "fingerprints". Apply framework to evaluate structural distinctness.

In practice

• Use ProcGrep for auditing agent task approaches.
• Implement task-aware model routing.
• Conduct finer-grained agent cost analysis.

Topics

• Agent Behavior Analysis
• Procedural Representations
• Coding Agents
• SWE-Bench
• ProcGrep Library
• Model Fingerprinting

Best for: MLOps Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.