What is multi-agent orchestration?
A single agent already runs a loop — reason, act, observe, repeat — using one model and one set of tools. Multi-agent orchestration is what you add once more than one agent works on the same overall task: a layer that decides which agent runs next, what each one sees of the shared task state, and how their separate outputs get combined into one result. Microsoft's Azure Architecture Center frames it as addressing "the unique challenges of coordinating autonomous components in AI-driven workloads" — distinct from classic distributed-systems patterns because the components reason and can produce nondeterministic outputs (Microsoft — AI Agent Orchestration Patterns).
If you haven't read the basics of a single agent's loop yet, start with our AI agents explained guide — orchestration builds on top of that loop, it doesn't replace it. For how agents remember things across steps and sessions, see our guide to AI agent memory.
The five orchestration patterns
Microsoft's Agent Framework documentation and Azure Architecture Center both converge on the same core set, and the research literature on event-driven multi-agent systems describes the same handful of coordination topologies under slightly different names (Microsoft Learn — Workflow Orchestrations in Agent Framework, arXiv — Autonomous Event-Driven Multi-Agent Orchestration for Enterprise AI):
| Pattern | How it works | Best for |
|---|---|---|
| Sequential | Agents run one after another in a fixed order; each consumes the previous agent's output. | Pipelines with a known, linear order of steps (draft → review → format) |
| Concurrent | The same task fans out to several agents in parallel, then results are merged (fan-out/fan-in). | Independent sub-tasks or getting several perspectives on one input |
| Handoff | One agent transfers control to another mid-conversation based on context, without a fixed script. | Support/triage flows where the right specialist isn't known up front |
| Group chat | Several agents share one conversation and decide amongst themselves who responds next. | Debate, brainstorming, or peer-review style collaboration |
| Hierarchical (orchestrator-worker) | A manager agent plans and dispatches tasks to specialized workers, then assembles their results. | Complex tasks that decompose into distinct sub-tasks of unknown shape ahead of time |
The sequential pattern is also called a pipeline or prompt-chaining; concurrent is also called fan-out/fan-in, parallel, or scatter-gather; and hierarchical is the same shape Anthropic calls the orchestrator-workers workflow in Building Effective Agents — a central agent dynamically breaks down a task and delegates pieces to worker agents it calls in parallel.
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Which pattern should you pick?
Both Microsoft's guidance and Anthropic's engineering writeup land on the same advice from different angles: start with the simplest pattern that solves the problem, and only add parallelism or hierarchy once a single agent or a fixed pipeline demonstrably can't handle the task. A few concrete rules of thumb:
- • Known, linear steps (draft → fact-check → format) → sequential. No coordination overhead, easy to debug.
- • Independent sub-tasks or you want several takes (research five competitors, or get three model opinions) → concurrent fan-out/fan-in.
- • You don't know which specialist should handle a request until you see it (support triage: billing vs. technical vs. sales) → handoff.
- • You want agents to critique or build on each other's output before converging → group chat.
- • The task decomposes into sub-tasks whose shape isn't known ahead of time, and sub-tasks need different tools or context → hierarchical (orchestrator-worker). Anthropic's own multi-agent research system uses exactly this shape: a lead agent plans and spins up subagents to search in parallel, then synthesizes what they found.
How the major frameworks implement orchestration
You rarely hand-roll orchestration logic from scratch — the mainstream frameworks bake these patterns in:
| Framework | Orchestration model | Notes |
|---|---|---|
| LangGraph | Graph-based state machine | Explicit nodes/edges give the most control over routing and retries; steepest learning curve. |
| Microsoft Agent Framework | Built-in sequential, concurrent, handoff, group chat, Magentic orchestrations | Successor to AutoGen + Semantic Kernel; deep Microsoft 365/Copilot Studio integration. |
| CrewAI | Role-based crews | Fastest to a working prototype — a handful of lines for a basic multi-agent workflow. |
| Google ADK | Hierarchical agent trees | Google's agent development kit; integrates with Gemini tooling and A2A for cross-vendor agents. |
See our rundown of open-source AI agent frameworks for a broader comparison beyond orchestration specifically. When the agents you're coordinating come from different vendors rather than one framework, that's the problem Google's A2A protocol targets — a transport standard for cross-vendor agent-to-agent calls, which pairs naturally with ADK's hierarchical orchestration model. For a concrete worked example of two specific tools handing off work to each other, see our piece on Hermes and Claude Code orchestration.
What goes wrong in multi-agent systems
Adding agents adds failure surface, not just capability. The recurring problems, per Microsoft's own framework guidance and the enterprise orchestration research:
- • Context loss at handoffs. The next agent doesn't see what actually mattered from the previous step unless the orchestration layer explicitly carries it forward.
- • Runaway cost and latency. Agents calling agents multiplies model calls fast; concurrent fan-out especially needs a hard cap on breadth.
- • Non-deterministic routing. Handoff and group-chat patterns let the system itself decide who acts next, which makes reproducing a bug harder than in a fixed pipeline.
- • Over-engineering. Reaching for hierarchy or group chat when a sequential pipeline would do — the single most common mistake per Microsoft's own guidance, which explicitly recommends starting simple.
Most of the production reliability work — durable state across steps, retries, sandboxed tool execution, observability — lives one layer below orchestration, in what each agent runs on. Our guide to AI agent runtimes covers that infrastructure layer in depth.
Frequently Asked Questions
What is multi-agent orchestration?
Multi-agent orchestration is the layer that coordinates two or more AI agents working on the same overall task — deciding which agent runs when, what each one sees, and how their outputs combine. It sits above a single agent's own reasoning loop: each agent still plans, uses tools, and observes results, but the orchestration layer controls the handoffs, parallelism, and aggregation between agents.
What are the main multi-agent orchestration patterns?
Microsoft's Agent Framework documentation names five built-in orchestrations: sequential (agents run in a fixed pipeline order), concurrent (the same task fans out to multiple agents and results fan back in), handoff (control transfers between agents based on context), group chat (agents share one conversation and take turns), and Magentic/hierarchical (a manager agent plans and dispatches to workers). Most production systems combine two or more of these rather than picking just one.
What's the difference between orchestration and a single agent's tool loop?
A single agent's loop (reason, act, observe, repeat) happens inside one agent working with one set of tools. Orchestration is the layer above that: it decides which of several agents runs next, routes context between them, and merges their separate outputs. You can have a sophisticated single-agent loop with no orchestration at all, or simple agents coordinated by rich orchestration — they're independent concerns.
Which framework should I use for multi-agent orchestration?
Pick by how much control you need versus how fast you want to ship. LangGraph gives the most explicit control via a graph of nodes and edges, at the cost of more setup. CrewAI gets a role-based multi-agent workflow running in the least code. Microsoft Agent Framework (the successor to AutoGen and Semantic Kernel) ships sequential, concurrent, handoff, group-chat, and Magentic orchestrations out of the box. Google's Agent Development Kit (ADK) favors hierarchical agent trees and pairs with the A2A protocol for cross-vendor agent calls.
When should I use hierarchical orchestration instead of a single bigger agent?
Use hierarchical orchestration — an orchestrator planning and dispatching to specialized workers — when a task decomposes into sub-tasks whose shape you don't know ahead of time, or when different sub-tasks need different tools, context, or models. Anthropic's own multi-agent research system uses an orchestrator-worker pattern for exactly this: a lead agent plans and spins up subagents to search in parallel, then synthesizes their findings. For tasks with a known, fixed sequence of steps, a plain sequential pipeline is simpler and cheaper to run.
What goes wrong in multi-agent systems?
The common failure modes are context loss at handoffs (the next agent doesn't see what mattered from the previous step), runaway cost and latency from agents calling each other in loops, and non-deterministic routing that makes debugging hard. Microsoft's own guidance recommends starting with the simplest pattern (sequential) that solves the problem and adding concurrency, handoff, or hierarchy only once a single agent or a fixed pipeline demonstrably can't handle the task.
Go deeper on multi-agent orchestration with AI
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