Multi-Agent Architectures

Multi-agent architectures coordinate multiple specialized agents to complete complex tasks that exceed what a single agent can handle, distributing work across agents that each have focused capabilities and smaller, more manageable context windows. Common patterns include supervisor architectures (one agent manages others), swarm patterns (agents dynamically hand off between each other), and parallel execution (multiple agents work simultaneously on subtasks). The primary advantage is specialization — each agent can have a targeted system prompt, limited tool set, and focused context — but the primary cost is coordination overhead: inter-agent communication, shared state management, and debugging distributed reasoning chains. The most important design principle is to exhaust single-agent solutions first, because multi-agent complexity is rarely justified by the problem and usually reflects insufficient tool design or context engineering. This concept connects to single agent patterns for the simpler alternative, orchestrator-worker for the most common multi-agent pattern, and the A2A protocol for standardizing inter-agent communication.