## Definition A **multi-agent system** decomposes a task across multiple specialised agents that coordinate via structured handoffs. Each agent has its own role, prompt, tool surface, and often its own model — see [[Specialized Agent]]. ## Why More Than One Agent - **Specialisation.** Different roles tune differently for cost and quality — see [[Model Selection Strategy]]. - **Independence.** A verifier reading the artifact cold catches issues the builder won't see — see [[Verifier Independence]]. - **Parallelism.** Independent subtasks can run in parallel — see [[Parallel Agents via Worktrees]]. - **Context isolation.** Each agent has its own context window; noisy intermediate work doesn't pollute the main thread. ## Coordination Patterns Anthropic's *Building Effective AI Agents* names the canonical shapes — see [[Building Effective AI Agents]]: | Pattern | Shape | When to use | | -------------------- | ----------------------------------------------- | ------------------------------------ | | Prompt chaining | A → B → C, fixed pipeline | Tasks with stable phases | | Routing | Router → one of N specialists | Heterogeneous inputs | | Parallelisation | N agents on independent subtasks, then aggregate | Embarrassingly parallel work | | Orchestrator-workers | Orchestrator dynamically spawns workers | Unpredictable complexity | | Evaluator-optimizer | Generator + critic loop | Quality-sensitive output | ## Failure Modes - **Telephone game.** Each agent paraphrases the previous one's output; intent drifts. - **Conflicting personalities.** Two agents argue indefinitely. Need a tie-breaker or a budget. - **Hidden coupling.** Two agents "agree" because they share the same base model's bias. - **Coordination overhead exceeding the work.** Three agents for a one-file change. The trifecta runs but no benefit accrues. ## When NOT to Reach For Multi-Agent - Single-file changes. - Tasks small enough to fit in one focused prompt. - Cases where you can't articulate what each agent *uniquely* adds. If you can't, you don't need it. ## In Practice Most production multi-agent systems are **shallow** — 2–4 roles, fixed pipeline. Deep orchestrator-worker hierarchies sound impressive in papers and are brittle in deployment. Start shallow; add depth only when the data demands it. ## Related - [[AI Agent]] - [[Specialized Agent]] - [[Sequential Pipeline]] - [[Builder-Critic Pattern]] - [[Building Effective AI Agents]]