Designing Multi-Agent Pipelines for LLM Workflows

Large language models are powerful on their own, but orchestrating multiple specialized agents to work together unlocks a new level of automation and complex problem-solving. This text-based course guides you through the foundational concepts of multi-agent systems, showing you how to design, coordinate, and execute structured AI pipelines. You will transition from writing simple prompts to structuring collaborative agent architectures that plan, execute, and refine their own workflows. What you'll learn: - Understand the core principles of multi-agent orchestration and task decomposition - Implement planning and execution patterns to guide agent collaboration - Manage state, memory, and context sharing across multiple interacting agents - Apply robust error-handling and fallback strategies for resilient agent communication - Evaluate and monitor agent interactions using modern AI observability concepts - Write structured Python code to build clean, maintainable multi-agent workflows Starting with fundamental definitions of agents and pipeline structures, the course walks you through coordination strategies, state management, and real-world execution patterns, ending with best practices for testing and debugging. This course is designed for software developers, technical product managers, and AI enthusiasts who are new to multi-agent architectures and want a structured, step-by-step introduction. No advanced machine learning background is required. Start reading today to master the next generation of collaborative AI workflow design.