Multi-Agent Systems with LangGraph

This program introduces Building Stateful & Multi-Agent Systems with LangGraph for developers and AI engineers who want to move beyond single-prompt agents and build reliable, production-ready workflows. You’ll begin by learning how LangGraph executes agent workflows and why state management is critical for correctness, debuggability, and long-running tasks. Next, you’ll work with state reducers, typed state objects, and checkpointing mechanisms that allow agents to persist progress, recover from failures, and resume complex multi-step executions. Through hands-on demonstrations, you’ll implement conditional routing, parallel execution paths, and modular subgraphs to enable dynamic, decision-driven workflows. As you progress, you’ll design human-in-the-loop systems with approvals and interrupts, apply debugging and time-travel analysis using execution logs and snapshots, and build multi-agent systems using supervisor–worker and consensus-based reasoning models for scalable, collaborative agent workflows. By the end of the program, you will be able to: - Explain how LangGraph executes workflows and manages state across agent nodes. - Design stateful agent pipelines using typed state objects and reducer patterns. - Implement checkpointing and recovery mechanisms for long-running agent workflows. - Control execution flow using conditional routing, parallel execution, and subgraphs. - Build human-in-the-loop workflows with approvals, interrupts, and state inspection. - Debug agent systems using execution logs, snapshots, and time-travel analysis. - Design multi-step planner–executor workflows for complex task execution. - Orchestrate multi-agent systems using supervisor–worker and consensus-based models. This program is ideal for AI engineers, backend developers, and system architects who want to build agent systems that are not only intelligent, but also predictable, auditable, and production-ready. Prior experience with Python, LLM fundamentals, and basic agent co