Designing Multi-Agent Systems: Collaborative AI Workflows

Artificial intelligence is evolving from single-prompt assistants to networks of collaborative agents that can plan, execute, and solve complex problems. Understanding how to orchestrate these multi-agent systems is becoming an essential skill for modern developers and tech innovators. This text-only course guides you from the fundamental principles of agentic AI to designing structured workflows where multiple agents communicate, share tasks, and collaborate effectively.\n\nWhat you'll learn:\n- Understand the core architecture of autonomous agents, including memory, tools, and planning capabilities.\n- Design structured collaboration patterns such as hierarchical, sequential, and bidirectional workflows.\n- Configure agent communication protocols to ensure reliable data sharing and task handoffs.\n- Integrate modern retrieval-augmented generation (RAG) patterns to ground agent decisions in external knowledge bases.\n- Apply specialized prompt engineering techniques tailored for individual agent roles and personas.\n- Practice debugging and testing multi-agent interactions through structured written scenarios and execution blueprints.\n\nYou will begin with foundational definitions and key terminology before exploring practical design patterns, agent communication strategies, and workflow management techniques. This course is designed for software developers, product managers, and AI enthusiasts who want to build cooperative AI systems, with no prior multi-agent experience required. Start reading today to design your first collaborative AI workforce.