Multi-Agent Systems & Orchestration
Design and build networks of specialized AI agents that collaborate, delegate, and self-correct to solve complex tasks. Multi-agent systems push AI automation to the next level.
Level: Intermediate · Category: Agentic AI · Estimated time: 7 hours
Prerequisites
- AI Agents: Foundations & Patterns
Lessons
- Multi-Agent Design Patterns — Supervisor-worker, peer-to-peer, hierarchical, and parallel agent topologies — when to use each.
- Agent Communication & State Management — Message passing, shared state, handoffs, structured inter-agent protocols, and avoiding race conditions.
- LangGraph: Stateful Agent Workflows — Nodes, edges, conditional routing, cycles, checkpointing, and human-in-the-loop with LangGraph.
- AutoGen: Conversation-Driven Multi-Agents — Microsoft AutoGen's GroupChat, AssistantAgent, UserProxyAgent, and code execution agents.
- CrewAI: Role-Based Agent Teams — Defining Crew, Agents, Tasks, and Processes — building a research-to-report pipeline with role-based delegation.
- Error Recovery & Self-Correction — Retry logic, the Reflexion pattern, self-critique loops, guardrails, and graceful degradation.
- Production Multi-Agent Systems — Observability, cost management, latency optimization, security, and deployment patterns for real-world agentic apps.
Topics covered
multi-agent, langgraph, autogen, crewai, orchestration, agent-communication, agentic-ai, workflows