arXiv:2606.11435v1 Announce Type: new Abstract: The growth of agent skills has transformed how agentic systems are built, evaluated, and deployed. As skill libraries continue to scale, rigorous evaluation becomes critical to ensuring their utility, quality, and safety in real-world applications. Consequently, the field is undergoing an emerging paradigm shift from isolated skill creation to automated, evaluation-driven skill evolution. In this survey, we systematically examine the landscape of skill evolution and evaluation beyond foundational skill creation. We categorize evolution into four
The rapid development and deployment of agentic systems necessitates robust evaluation frameworks to ensure their utility, quality, and safety as skill libraries grow exponentially.
Rigorous evaluation and evolution of AI agent skills are critical for their safe and effective integration into real-world applications, directly influencing productivity and trust.
The focus in AI agent development is shifting from isolated skill creation to automated, evaluation-driven skill evolution, implying more scalable and reliable agent systems.
- · AI agent developers
- · Businesses adopting AI agents
- · AI safety researchers
- · Software quality assurance
- · Unverified AI agent providers
- · Manual workflow processes
- · Inefficient AI development cycles
Improved reliability and broader adoption of AI agentic systems in various industries.
Accelerated automation of white-collar tasks, potentially leading to significant shifts in knowledge work employment.
The development of highly adaptive and self-improving AI systems capable of complex, unsupervised operation across domains.
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