Multi-Agent Swarms & Consensus Hierarchies

Orchestrate teams of specialized AI agents: Supervisor-Worker topologies, debate consensus, and LangGraph state machines.

1. Multi-Agent System Topologies

Hierarchical Supervisor Pattern: A central Planner/Orchestrator decomposes goals into subtasks, delegates them to specialized workers (e.g. Coder, Data Analyst, Web Researcher), and synthesizes final outputs.
Debate & Multi-Agent Consensus: Multiple agent instances critique each other's outputs in iterative rounds to eliminate hallucinations and achieve verified consensus.

2. Graph-Based State Machines (LangGraph Architecture)

Modern agent frameworks model workflows as directed cyclical graphs \(G = (V, E)\) with global typed state channels, conditional edge branching, and checkpointed human-in-the-loop validation.

🎯 Module Mastery Certification Quiz

+100 XP

What is the primary architectural advantage of a Hierarchical Supervisor Multi-Agent pattern over a single massive prompt?

Specialization of agent system prompts, isolated tool scopes, and reduced context window bloat.

It guarantees 0ms latency responses.

It requires zero API keys.

It runs without a network connection.