AI Agent Orchestration: Patterns for Coordinating Multiple Agents

Agent orchestration is coordinating several AI agents on one job — deciding who does what, in what order, and how results get combined.

Before the patterns, the honest part: most tasks that look like they need multiple agents need one agent with better tools. Splitting work across agents adds coordination overhead, multiplies cost, and introduces a failure mode single agents don't have — agents disagreeing, or confidently handing each other wrong information.

Multiple agents earn their keep in three situations, and only three:

• Genuinely parallel work — ten documents that don't depend on each other
• Real specialization — different tools, different context, different models
• Deliberate independence — you want separate opinions, not one chain of reasoning

If none apply, add a tool, not an agent.

The patterns

Sequential is the most common and the one to be most careful with. Each stage inherits the previous stage's mistakes, and a wrong assumption at step one gets elaborated rather than caught. Put a validation step between stages that can be checked cheaply.

Parallel fan-out is where multi-agent genuinely shines, because the items are independent — so nothing compounds. The interesting work is in the merge: naive concatenation of ten summaries produces something worse than any one of them. Dedupe and synthesize deliberately.

Supervisor reads well on a diagram and is where most projects overspend. The manager agent burns tokens deciding what to do rather than doing it, and its routing decisions are themselves a source of error. Use a plain router — a classifier or even a rule — before you use a manager agent.

Debate is real and expensive. Three agents answering independently and a fourth judging catches errors a single pass misses. Worth it when being wrong is costly; wasteful otherwise. Note the trap: agents agreeing doesn't mean they're right, especially when they share a model and a prompt style.

What actually breaks

Four failure modes, in order of how often they bite:

1. Context loss at handoffs. Agent A knows something it doesn't pass to B, because it didn't realize it mattered. This is the most common multi-agent bug and it's usually invisible — B produces a plausible answer built on missing information. Pass structured state between agents, not prose summaries.

2. Cost multiplication. Every agent re-reads shared context. Five agents on a long document is five times the input tokens, before any of them produce output. Budget as roughly the square of what a single agent would cost on a multi-turn task.

3. Nobody owns the outcome. With one agent, the transcript tells you what happened. With five, you're reconstructing a conversation across logs. Log a single correlated trace across all agents or debugging becomes archaeology.

4. Termination. Two agents can pass work back and forth indefinitely, each waiting for the other to finish. Every orchestration needs a global step limit and a global spend cap — not per-agent ones, which is a mistake people make once.

When one agent is the right answer

A single agent with ten well-described tools beats five agents with two tools each, most of the time. The reasons are unglamorous:

• No handoff means no context loss.
• One transcript means real debugging.
• One budget means predictable cost.
• Tools are cheaper than agents — a tool call costs a function execution; an agent costs a full model round trip with the whole context.

The honest test before splitting: can I name the specific information each agent has that the others must not, or cannot, see? If the answer is "nothing, they'd just be doing different steps," those are tool calls, not agents. Our guide to building agents covers the single-agent loop that handles most of this.

Implementing it

Once you genuinely need coordination, the choice is about how much control you keep over the sequence:

• LangGraph models the whole thing as an explicit graph with shared state — the common production answer because you can see where execution is and resume from a node.
• CrewAI gives you role-based agents that collaborate, which is the fastest way to prototype and the abstraction most likely to need replacing when you want precise control.
• Temporal is worth knowing when orchestration must survive restarts and run for days — durable execution rather than agent-specific features.
• Plain code remains a serious option. A for loop over documents calling one agent is orchestration, and it's the easiest thing in this list to debug.

Expose shared tools over the Model Context Protocol where you can, so integrations survive a change of framework. Agentic AI frameworks compares the options in more depth.

If the blocker is running agent setups rather than designing them, Taku mirrors a working AI setup into your own desktop workspace and runs it there, without reproducing someone's environment first. The free app library shows what's available to mirror. Taku is in Beta, and the Mac app is available now.

FAQ

What is AI agent orchestration?

Coordinating multiple AI agents on a single job — assigning work, sequencing it, passing state between agents, and combining results. It sits above individual agents in the stack.

When do I need multiple agents?

Only for genuinely parallel work, real specialization across tools or models, or when you want independent opinions. Otherwise one agent with more tools is cheaper and easier to debug.

What are the main multi-agent orchestration patterns?

Sequential pipelines, parallel fan-out with a merge, a supervisor delegating to workers, debate panels with a judge, and handoff to a specialist. Each fails differently.

Why is multi-agent so expensive?

Every agent re-reads shared context, so token cost multiplies before any output is produced. Budget roughly the square of a single-agent multi-turn task.

What's the most common multi-agent bug?

Context loss at handoffs — one agent omits something the next needed, and the next produces a plausible answer built on the gap. Pass structured state rather than prose summaries.

Key points

• Most tasks that look multi-agent need one agent with better tools.
• Parallel fan-out is where multi-agent genuinely wins; sequential compounds errors.
• Supervisor patterns look good on diagrams and are where projects overspend.
• Context loss at handoffs is the most common and most invisible failure.
• Set global step and spend limits, not per-agent ones.