MIT和哈佛研究人员提出智能体技能的完整生命周期架构，解决LLM智能体在复杂任务中的可靠性瓶颈。
该论文提出了智能体技能(agentic skills)的统一系统基础和参考架构。研究将技能定义为外部化程序知识，连接高层认知规划与确定性执行环境。论文详细阐述了技能的九阶段生命周期：自主发现、创作与表示格式、内存存储、动态检索与路由、组合与编排、执行与修复、终身适应、实证评估和安全治理。研究还考察了市场动态、公共注册表和新兴对抗威胁向量。
Towards a Systems Foundation for Agentic Skills: Architecture, Lifecycle, and Security
Autonomous large language model (LLM) agents increasingly face reliability, context consumption, and execution stability bottlenecks when deployed on complex, long-horizon tasks. While monolithic prompt engineering and stateless tool-calling paradigms struggle to scale, the field is rapidly converging toward \emph{agentic skills}: modular procedural abstractions that externalize execution knowledge into reusable, executable, and portable artifacts. This paper establishes a unified systems foundation and reference architecture for the agentic skills ecosystem. We formalize skills as externalized procedural knowledge bridging high-level cognitive planning with deterministic execution environments, and systematically delineate the architecture across a nine-stage lifecycle: autonomous discovery, authoring and representation formats, memory storage, dynamic retrieval and routing, composition and orchestration, execution and repair, lifelong adaptation, empirical evaluation, and security governance. We further examine marketplace dynamics, public registries, and emerging adversarial threat vectors, alongside runtime verification and defense mechanisms. Finally, we categorize system implementations across software engineering, operating system navigation, embodied robotics, and scientific discovery, while highlighting critical open challenges in continual learning and benchmark realism. This work establishes agentic skills as a foundational paradigm for building scalable, robust, and verifiable autonomous language agents.