arXiv:2605.31264v1 Announce Type: cross Abstract: LLM agents are increasingly expected not only to complete isolated tasks, but also to carry bounded representations of human expertise, judgment, and interaction style. Building such person-grounded agents remains difficult because actionable knowledge associated with a person or role is usually embedded in heterogeneous traces rather than written as clean instructions. Existing memory and persona systems capture fragments of this evidence, while skill frameworks provide portable packaging formats; however, there is no end-to-end workflow for d
The proliferation of LLMs and the increasing demand for sophisticated, adaptable AI agents necessitate breakthroughs in automated skill generation to move beyond isolated tasks.
This work directly addresses a critical bottleneck in the scaling and personalization of AI agents, moving them closer to embodying human-like expertise and judgment.
The development of automated AI skill generation shifts the paradigm from manual, instruction-based agent development to expert knowledge distillation, accelerating agent capabilities.
- · AI platform developers
- · Enterprises adopting AI agents
- · Professionals seeking AI augmentation
- · Manual AI agent development services
- · Companies with undifferentiated AI agent offerings
More sophisticated and context-aware AI agents become widely deployable, handling complex tasks.
This leads to significant productivity gains in white-collar sectors as agents can autonomously learn and adapt to nuanced roles.
The increased autonomy and human-like expertise of agents could redefine job functions, augmenting or displacing roles that involve structured expert knowledge.
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Read at arXiv cs.LG