Multi-Agent Systems

Multiple agents with distinct roles can be arranged as supervisor, pipeline, hierarchy or swarm; a role buys separate context, tools and evals, at the price of coordination.

One orchestrator agent decomposes work, delegates to specialist agents, and merges results — simple to reason about, but the orchestrator is the bottleneck and the single point of failure.

Agents arranged as fixed stages with typed hand-offs (Research → Analyze → Write → Review) trade flexibility for per-stage evaluation, replaceability and predictable cost.

Manager → team leads → workers scales a supervisor to many parallel sub-tasks, but each level compresses information and multiplies latency and cost.

Peer agents hand a conversation and its context to whichever agent has the right capability, with no central controller — flexible for routing-heavy dialogues, risky without hop limits.

Multi-agent systems stack latency, compound errors, cost coordination tokens and are harder to evaluate; the first question is always whether one agent or deterministic code can do the job reliably.

Agents coordinate through discovery, capability descriptions, task delegation and hand-off, choosing between shared state and message passing; A2A-style protocols cover agent↔agent the way MCP covers agent↔tool.