Multi-Agent Orchestration
Coordinate a network of specialised AI agents under an orchestrator, where each agent owns a distinct capability or domain and agents communicate through structured messages.
errorWhat Problem Does the Multi-Agent Orchestration Pattern Solve?
Complex tasks span multiple domains — research, coding, data analysis, communication — and a single general-purpose agent must context-switch between all of them, diluting focus and overflowing context windows. Monolithic agents also lack isolation: a failure in one capability can corrupt the entire task context.
check_circleHow the Multi-Agent Orchestration Pattern Works
Decompose the system into specialised sub-agents (e.g., a ResearchAgent, a CodeAgent, a CritiqueAgent) each with a focused prompt, tool set, and memory scope. An Orchestrator agent receives the top-level goal, delegates sub-tasks to the appropriate specialist via structured messages, aggregates their outputs, and synthesises the final result.
Multi-Agent Orchestration Pattern Architecture
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Implementation by Language
Multi-Agent Orchestration Pattern in the Real World
“A film director (Orchestrator) does not personally operate the camera, compose the score, or design costumes. Instead they delegate to specialist department heads — cinematographer, composer, costume designer — each expert in their domain. The director collects their work, gives feedback, and integrates it into a coherent film.”
Frequently Asked Questions
helpWhat is the difference between Multi-Agent and a single agent with multiple tools?
A single agent with tools makes all decisions itself. Multi-Agent distributes decision-making across specialized agents, each with their own system prompts, tools, and context. This enables separation of concerns — a coding agent doesn't need marketing context and vice versa.
helpHow do multiple agents communicate with each other?
Common patterns include message passing (agents send structured messages), shared state (agents read/write to a common store), and orchestration (a supervisor agent delegates tasks and collects results). The choice depends on whether coordination is centralized or peer-to-peer.
helpWhat are the cost implications of Multi-Agent systems?
Each agent call consumes tokens independently, so costs multiply with the number of agents. Minimize cost by using smaller models for simple sub-tasks (like validation) and reserving expensive models for complex reasoning. Also limit the context passed between agents.