arXiv:2606.06893v1 Announce Type: new Abstract: Large language model agents increasingly rely on Skills to encode procedural knowledge, yet high-quality Skills remain costly to hand-write. This paper studies automatic Skill construction from heterogeneous interaction evidence, including demonstrations, agent trajectories, tool traces, and execution logs. We argue that trace-to-skill construction is not simple summarization tasks, because traces are fragmented, redundant, and may miss rare but safety-critical behaviors. To address this, we introduce RWSA, a workflow-oriented intermediate repres
The increasing reliance on large language model agents highlights the urgent need for scalable and automated skill creation to overcome the limitations of manual programming.
Automating the creation of high-quality AI skills from diverse data could significantly accelerate the development and deployment of autonomous AI agents, making them more robust and capable.
The method of building agentic capabilities shifts from manual, costly engineering to potentially automated synthesis from existing traces and interactions, reducing development bottlenecks.
- · AI agent developers
- · Companies adopting AI agents
- · AI research institutions
- · Manual AI skill programmers (long-term)
- · Companies slow to adopt advanced AI agent development
Efficient skill creation enables more complex and reliable AI agents for various tasks.
The proliferation of highly capable AI agents could disrupt traditional white-collar workflows and industries.
Accelerated AI agent development might intensify the strategic competition for AI leadership among nations and corporations.
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Read at arXiv cs.AI