arXiv:2606.20659v2 Announce Type: replace Abstract: Agent skills encode reusable procedural knowledge for large language model (LLM) agents, and existing benchmarks show that such skills can improve task-level performance. However, a task outcome does not reveal which parts of a reusable skill were exercised, nor whether the agent followed the relevant skill instructions when those parts were exercised. This gap makes it unclear whether a skill has been adequately tested, or whether observed task failures provide actionable evidence for improving agent skill effectiveness. To fill this gap, we
The rapid advancement and deployment of LLM agents necessitate robust methods for evaluating their performance and reliability, moving beyond simple task outcome metrics to more granular skill assessment.
Developing effective testing methodologies for AI agents is crucial for their commercial viability, safety, and integration into complex workflows, allowing for targeted improvements and verification of their capabilities.
The introduction of 'Skill Coverage' as a test adequacy metric provides a more nuanced way to assess and improve AI agent skills, moving beyond binary success/failure to understand *how* a skill was utilized.
- · AI Agent developers
- · LLM providers
- · Software testing companies
- · Industries adopting AI agents
- · Developers relying on ad-hoc testing
- · Agent architectures lacking modular skill design
Increased reliability and effectiveness of AI agents as testing becomes more rigorous and targeted.
Faster iteration and deployment cycles for AI agents due to clearer diagnostic feedback on skill performance.
Higher trust in autonomous agentic systems, accelerating their adoption across critical applications and collapsing more white-collar workflows.
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Read at arXiv cs.AI