Multi-Agent Research Systems with the Orchestrator-Worker Pattern

Building effective AI systems requires moving beyond single-prompt interactions toward structured multi-agent architectures. This text-only course introduces you to the core principles of the Orchestrator-Worker pattern, a highly efficient agentic design pattern used to manage and delegate complex research workflows. You will learn how to structure a central orchestrator that analyzes tasks, distributes subtasks to specialized worker agents, and synthesizes the final outputs. By completing this course, you will understand how to coordinate multiple LLMs to collaborate on deep research, data analysis, and document generation without losing track of state or context. You will also explore modern practices such as managing agent memory, implementing type-safe structured outputs, and designing robust error-handling mechanisms. What you'll learn: - Understand the foundational concepts of agentic design patterns and multi-agent coordination - Design an Orchestrator-Worker architecture to break down complex research queries into manageable subtasks - Implement structured prompts and type hints to ensure reliable communication between agents - Manage state, context, and memory across multiple parallel worker executions - Apply evaluation patterns to verify and synthesize worker outputs into a cohesive final report - Address common failure modes, rate limits, and error recovery in multi-agent workflows This course begins with essential definitions and core architectural concepts before guiding you through the step-by-step logic of agent communication, state management, and output synthesis. Through structured code walkthroughs and conceptual explanations, you will gain a practical framework for building reliable AI systems. This course is designed for software developers, data professionals, and AI enthusiasts who want to move beyond basic API calls and build sophisticated, automated research workflows. No advanced machine learning background is required, though basic familiarity with Python is recommended. Start reading today to master the design patterns that power modern multi-agent AI systems.