Agent Orchestration
The discipline of wiring multiple AI agents into a workflow — picking patterns (router, sequential, parallel, hierarchical, swarm) that survive production.
What Is Agent Orchestration
Agent orchestration is how you wire multiple AI agents into a single workflow that does more than any one agent could on its own. The term covers the architectural choices: which agent runs first, who decides what to do next, how output from one becomes input to another, and how the system fails when an agent gets stuck.
Orchestration is what turns a multi-agent system from a research demo into something that ships. Picking the wrong pattern is recoverable in a prototype and a rewrite in production.
The Five Production-Survivable Patterns
| Pattern | What it does | Best for |
|---|---|---|
| Router | Parent dispatches to specialists; no specialist crosstalk | First multi-agent build, varied inputs |
| Sequential | Pipeline of agents, each consuming the previous output | Plan → execute → review workflows |
| Parallel | Fan-out to N agents at once, reconcile results | Speed-critical queries, voting |
| Hierarchical | Persistent manager + short-lived workers | Long-running tasks (research, refactors) |
| Swarm | Peer agents share a workspace, self-organize | Open-ended research, emergent behavior |
Most production multi-agent systems are router + a retry loop. The fancier patterns earn their keep on specific problem shapes — hierarchical for long-running tasks, swarm for genuinely open-ended exploration.
How Orchestration Differs From a Single Agent Loop
A single agent loop reads input, calls tools, generates an answer. Orchestration adds:
- Routing logic — which agent (not just which tool) handles this step?
- Inter-agent state — what does the next agent see from the previous one’s run?
- Failure recovery — when agent B fails, do we retry, escalate, or abort?
- Observability across the graph — tracing every LLM call across multiple agents (services like Langfuse make this tractable).
The single-agent loop is a function call; orchestration is a workflow engine.
The Failure Mode Every Orchestration Pattern Shares
Context window blow-up. Each agent appends to the conversation. By the third hop, the prompt contains the full transcripts of the previous two agents — 60k+ tokens of mostly noise for what should be a 2k-token decision.
Three defenses applied in production agent stacks: explicit summarization between agents (pass structured summaries, not raw transcripts), per-agent context budgets (hard token caps), and tracing every call so you can see which step exploded.
When to Use Which Framework
The pattern → framework match matters more than the framework’s marketing. Anthropic and OpenAI SDKs implement router and sequential cleanly with ~80 lines of code. CrewAI and LangGraph earn their keep on hierarchical and complex sequential. OpenSwarm is the working reference for swarm patterns. Paperclip implements hierarchical with persistent manager state.
For the full pattern catalog with framework scoring matrix, see AI Agent Orchestration: The 5 Patterns That Survive Production.