arXiv:2605.11946v2 Announce Type: replace Abstract: Large Language Model agents are increasingly augmented with agent skills. Current evaluation methods for skills remain limited. Most deployed benchmarks report only pass rate before and after a skill is attached, treating the skill as a black box change to agent behavior. We introduce Counterfactual Trace Auditing (CTA), a framework for measuring how a skill changes agent behavior. CTA pairs each with skill agent trace with a without skill counterpart on the same task, segments both traces into goal directed phases, aligns the phases, and emi
The rapid advancement and deployment of LLM agents necessitate more robust and transparent evaluation methods to ensure their reliability and safety.
Improved auditing of LLM agent skills is crucial for validating their effectiveness, fostering widespread adoption, and addressing potential biases or unintended behaviors.
This new framework moves beyond black-box evaluation, allowing for a detailed, counterfactual analysis of how skills alter agent behavior, providing deeper insights into their impact.
- · AI Agent Developers
- · Enterprises deploying LLM Agents
- · AI Safety Researchers
- · Audit & Compliance Software Vendors
- · Companies relying on opaque AI agent evaluation
- · Badly designed LLM agent skills
More reliable and transparent LLM agents will accelerate their integration into complex workflows.
The demand for specialized tools and services to implement counterfactual trace auditing will increase.
Standardization of agent skill auditing methods could emerge, influencing regulatory frameworks for AI systems.
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Read at arXiv cs.AI