Project Scoping for AI Research Assistant Workflows

Artificial intelligence is transforming how we gather, analyze, and synthesize information, but building a reliable AI assistant requires disciplined planning and architecture. Without proper scoping, multi-agent systems often suffer from unpredictable loops, lost context, and poor scalability. This course teaches you how to systematically design a structured research assistant workflow from the ground up. You will learn to map out complex information-gathering tasks into predictable, state-driven architectures before writing any production code. By understanding how to define clear boundaries and data flows, you will be able to build AI systems that are both reliable and easy to maintain. What you'll learn: Understand core orchestration concepts and state-driven design patterns; Define robust state schemas to manage context across complex workflow steps; Map routing logic and conditional transitions to guide agent decisions; Design modular nodes to separate concerns and ensure system scalability; Apply best practices for handling asynchronous tasks and rate limits in agentic workflows. This text-based course begins with essential terminology and foundational concepts of state machines and agentic design patterns. You will then progress through step-by-step written walkthroughs that demonstrate how to translate a research problem into a production-ready system architecture. This course is designed for software developers, data professionals, and AI enthusiasts who want to move beyond basic prompts and build structured, multi-step AI applications. No prior experience with complex orchestration frameworks is required. Start reading to master the fundamentals of scoping and designing reliable multi-agent systems today.