How to build AI Agents according to Anthropic ?

In the rapidly evolving world of AI, effective LLM agents don’t require complex frameworks—they thrive on simplicity and composability. At Anthropic, after working with dozens of teams across industries, one insight stands out: the most impactful agents are built using straightforward patterns and direct LLM API use. This article distills our experience into practical guidance for developers looking to create robust, reliable, and scalable LLM-based systems.

We explore the distinction between workflows and agents, showing when and how each can be applied for maximum benefit. From basic prompt chaining and routing to advanced orchestrator-worker models and evaluator-optimizer loops, we break down the building blocks that power real-world agentic systems. Special focus is given to the concept of the augmented LLM—models enhanced with tools, memory, and retrieval—which serve as the foundation for all agentic designs.

Whether you’re optimizing customer service flows or building autonomous coding assistants, the key takeaway is clear: start simple, test iteratively, and only scale complexity when it’s truly needed. This post provides a strategic blueprint for anyone designing intelligent systems that interact with and adapt to dynamic environments. Dive in to learn how to harness the full potential of LLM agents—without unnecessary complication.

Explore our full guide to learn how to build practical, efficient LLM agents—and start implementing smarter, more scalable systems today.