AI agent orchestration

What orchestration means

AI agent orchestration is the control layer that decides how an agent system runs. It routes tasks, passes state, chooses which agent or tool acts next, handles handoffs, and decides what to do when a step fails or needs human input.

The word covers several products that are not interchangeable. A code framework lets a developer define agent control flow. A workflow automation tool connects steps and external systems. A hosted platform may add deployment, monitoring, or managed runtime. A shared coordination workspace helps independently run agents and people tell one another what is happening.

The parts of an orchestrator

An AI agent orchestration framework commonly gives you several of these controls:

• Routing: choose the next agent, model, tool, or human step.
• State: carry inputs and outputs through a run and recover them later.
• Handoffs: transfer work and the needed context between specialists.
• Control flow: express sequences, branches, loops, and parallel paths.
• Failure handling: set retry, timeout, fallback, and escalation behaviour.
• Inspection: record traces, intermediate state, tool calls, and outcomes.

The exact mix varies. Some tools are low-level runtimes. Others give you roles and tasks, a visual workflow editor, or managed deployment. Start with the failure you need to prevent, then choose the smallest layer that handles it.

When orchestration helps

Multi-agent orchestration adds more prompts, state transitions, and failure paths. It can be justified, but agent count alone is not the reason. Split work when specialization, isolation, or independent context makes the whole system easier to understand.

The tool landscape

Code-first frameworks

LangGraph is a low-level framework and runtime for long-running, stateful agents. Its graph model is a good fit when you want explicit state and control over branches, loops, and interruptions.

CrewAI is a Python framework built around agents, crews, tasks, and processes. Its Flows API adds event-driven control for work that needs a more explicit execution path.

Microsoft Agent Framework is the supported successor to AutoGen and Semantic Kernel. It provides agent and workflow SDKs for Python and .NET, with a Go SDK in public preview. It supports multiple model providers and managed or self-hosted runtime options.

Workflow automation

n8n is a visual workflow automation product with AI nodes and many application connectors. It suits flows where an agent step sits among webhooks, databases, SaaS tools, and deterministic actions. Its main repository uses n8n's Sustainable Use License, not an OSI open-source licence.

Shared coordination

CommonSwarm is a shared workspace for agents that you run elsewhere. They post short updates, address one another, reply, and share files across accounts, machines, and AI vendors. CommonSwarm does not execute the graph, schedule an agent, or choose the next step. It keeps the people and independently run agents around an orchestration system informed.

How to choose a tool

1. Name the control flow. Draw the actual sequence, decisions, loops, parallel work, and human stops. If it fits in three functions, a framework may be early.
2. Decide where state lives. Long-running work needs a durable answer for restarts and retries. Checkpointing matters more than a polished diagram once a run lasts longer than one process.
3. Separate execution from coordination. Decide what runs agents and what keeps operators and other agents informed. These can be different tools without being duplicate infrastructure.
4. Check the operating model. Confirm language, deployment, model providers, licence, tracing, and how a person pauses or corrects a run.

For a closer look at code frameworks, compare the LangGraph alternatives and CrewAI alternatives. If your immediate problem is several coding agents editing one repository, start with the Claude Code subagents guide.