Anthropic is challenging the prevailing trend in AI agent development with its bold stance: don’t build full agents—build skills instead. This philosophy marks a strategic shift in how AI capabilities are designed and deployed, positioning Anthropic at the forefront of a new architectural paradigm in agentic systems. While 2025 saw the rise of AI agents across multiple fronts, three distinct architectures have emerged, each with unique strengths and trade-offs. The first is the monolithic single-agent model, where one large language model acts as the central intelligence, invoking tools as needed. While effective for rapid prototyping, it struggles under complexity and tool overload, leading to reliability issues in real-world scenarios. The second approach is agentic workflows—multi-agent orchestration—where specialized agents collaborate in a workflow graph. This enables parallel execution, better cost control, and predictable outcomes, making it ideal for enterprise applications, especially when powered by frameworks like LangGraph. The third and most compelling architecture, championed by Anthropic, is the LLM Skills model—a modular, extensible framework where core models like Claude are enhanced with reusable, domain-specific capabilities. These skills are not standalone agents but dynamic, pre-built extensions—think of them as plug-and-play functions for tasks such as document processing, computer interaction, or data retrieval. Anthropic’s Skills architecture focuses on empowering models with practical, real-world abilities without the overhead of managing complex agent ecosystems. For example, a skill can enable an AI to navigate a GUI, click buttons, type text, or interact with applications—capabilities trained on general computer behaviors rather than specific tools. This reduces dependency on brittle tool-calling mechanisms and lowers the barrier for deploying AI in everyday workflows. The practical benefits are clear. Developers can compose skills on-demand, integrating them seamlessly into models like Claude 3.5 Sonnet to create autonomous workflows. This modular approach enhances composability, improves efficiency, and avoids the pitfalls of monolithic designs or sprawling agent networks. To demonstrate this in action, a hands-on example shows how to build a simple weather checker skill using the Anthropic SDK on a MacBook. By setting up a skill directory with metadata and a Python script that calls the OpenWeatherMap API, the system dynamically exposes the skill to Claude. When prompted, Claude detects the need for weather data, attempts to use the skill, adapts after an initial failure (due to a typo in location input), retries successfully, and delivers a detailed, natural-language response. This real-world test illustrates the core value of the skills model: adaptability, resilience, and intelligent error recovery—all within a lightweight, scalable framework. Anthropic’s strategic pivot reflects a broader trend: moving up the AI stack by focusing on reusable, high-quality components rather than reinventing agent architectures from scratch. By treating skills as foundational building blocks, Anthropic enables faster innovation, reduces risks tied to bespoke agent development, and supports greater model sovereignty. In essence, the shift from building agents to building skills isn’t just a technical change—it’s a paradigm shift toward smarter, more sustainable AI development. As AI continues to evolve, Anthropic’s modular approach offers a pragmatic path forward, blending flexibility, reliability, and real-world usability in one cohesive framework.