AI Agents: The Multi-Agent Design Pattern - Part 8
This blog post, Part 8 in a series on AI agents, explores the Multi-Agent Design Pattern, outlining the benefits and key components of building systems with multiple interacting agents. It details the scenarios where multi-agent systems excel (large workloads, complex tasks, diverse expertise), highlights their advantages over single-agent approaches (specialization, scalability, fault tolerance), and discusses the fundamental building blocks for implementation, including agent communication, coordination mechanisms, and architectural considerations. The post introduces common multi-agent patterns (group chat, hand-off, collaborative filtering) and illustrates these concepts with a refund process example. Finally, it includes a practical assignment and provides links to further resources and previous posts in the series.AI Agents: Planning and Orchestration with the Planning Design Pattern - Part 7
This blog post, Part 7 in a series on AI agents, focuses on the Planning Design Pattern for effective task orchestration. It explains how to define clear goals, decompose complex tasks into manageable subtasks, and leverage structured output (e.g., JSON) for seamless communication between agents. The post includes code snippets demonstrating how to create a planning agent, orchestrate multi-agent workflows, and implement iterative planning for dynamic adaptation. It also links to a practical example notebook (07-autogen.ipynb) and further resources like AutoGen Magnetic One, encouraging readers to explore advanced planning concepts. Links to the previous posts in the series are provided for easy access to foundational AI agent concepts.
