The Complete Guide to AI Agent Architectures: From MoE to Multi-Agent Orchestration

Every AI system that takes actions in the real world is built on an agent architecture . That architecture determines how the system reasons, which tools it invokes, how it coordinates work across agents, and how it performs under production load. The problem is that "AI agent" now covers everything from a single ReAct loop to a fleet of 800 specialized agents running in parallel. If you are building production AI systems, you need a clear taxonomy of architectures, their tradeoffs, and the decision criteria for choosing between them. This guide is the hub for that taxonomy. It covers the full spectrum — from model-level architectures like Mixture of Experts to system-level patterns like orchestrator-worker and swarm — and links to dedicated deep-dives on each topic. Whether you are evaluating whether to move from a single agent to a multi-agent system, or choosing between coordination patterns for an existing deployment, start here. What Are AI Agent Architectures An AI agent architec