Overview
Multi-agent systems distribute complex tasks across networks of specialized AI agents that collaborate through structured communication protocols. Rather than overloading a single model with all knowledge and responsibilities, the system assigns each agent a narrow role — planner, executor, critic, researcher — and coordinates their outputs toward a shared goal. This mirrors human organizational patterns: teams outperform individuals on tasks requiring breadth, parallel execution, or adversarial verification. The key design challenges are coordination overhead, context passing between agents, and preventing failures from cascading across the network.
Key Concepts
- Orchestrator-worker pattern — A central orchestrator agent decomposes the task, delegates subtasks to worker agents, and synthesizes their outputs. The orchestrator maintains global state; workers operate in isolated contexts.
- Agent specialization — Each agent is given a focused system prompt and tool set. Specialization improves reliability by reducing the decision space each agent must navigate.
- Communication topology — Agents can communicate sequentially (pipeline), in parallel (fan-out/fan-in), or peer-to-peer (debate/ensemble). Topology determines latency, reliability, and the kinds of errors that can occur.
- Shared memory — A persistent store (file, database, or structured document) that agents read from and write to, enabling coordination without direct inter-agent messaging.
- Adversarial verification — A critic or evaluator agent reviews the outputs of other agents, catching errors and hallucinations that a single model would miss.
- Context isolation — Each subagent gets only the context relevant to its task, preventing context pollution from degrading decision quality.
Trade-offs
- Parallelism reduces wall-clock time but increases total token cost
- More agents means more coordination logic and harder debugging
- Isolated context prevents pollution but can cause agents to make locally rational but globally wrong decisions
- Adversarial critique improves quality but adds at least one additional round-trip
Related
- AI & Agents — the broader taxonomy this pattern sits within
- Agent Patterns — foundational patterns including plan-and-execute which underpin most multi-agent designs
- Evaluation Frameworks — essential for measuring multi-agent system quality across the full pipeline
- Multi-Agent Workflow — the Aloysius Productions implementation of this pattern using Claude Code and Copilot CLI