Multi-agent orchestration has become one of the most consequential architectural decisions in agentic AI, so it's worth understanding the actual pattern options, not just whether to go multi-agent at all. We've written about when multi-agent is actually worth the complexity; this is about what to build once you've decided it is.
The supervisor pattern
One orchestrator agent decomposes an incoming task, routes sub-tasks to the right specialist agents, and synthesizes their outputs into a final response. This is the most common pattern for good reason: it's the easiest to reason about, debug, and evaluate, because task decomposition and final synthesis happen in one clear place.
Where it works well: tasks with a natural decomposition into distinct, non-overlapping specialist domains, where the orchestrator's job is genuinely just routing and synthesis, not deep reasoning itself.
Where it breaks down: tasks where the right decomposition isn't obvious upfront, or where specialists need to iterate with each other, not just report back to a central coordinator once each.
The handoff pattern
Rather than a central orchestrator, control passes directly from one agent to the next, each handling its part of the task before handing off to whichever agent is appropriate next. This tends to fit workflows that are genuinely sequential, where each stage's output is a natural precondition for the next, rather than tasks that need central coordination or parallel work.
Where it works well: linear, staged processes: intake, then processing, then review, then finalization, where the next step is always clear once the current one completes.
Where it breaks down: anything requiring the equivalent of a project manager keeping track of multiple parallel threads. A pure handoff chain has no natural place for that coordination to live.
The swarm / peer pattern
Multiple agents work more independently, without a strict central coordinator, communicating directly with each other as needed and converging on a result collectively. This is the least mature pattern in practice and the hardest to evaluate and debug, because there's no single place where the overall reasoning is legible.
Where it works well: genuinely exploratory or parallel-search problems, where multiple independent attempts and cross-checking add real value and a rigid hierarchy would actually slow things down.
Where it breaks down: anything where predictability and auditability matter more than exploratory breadth, which describes most business-process automation.
How to actually choose
- Start with the supervisor pattern unless you have a specific reason not to. It's the most debuggable and the easiest to evaluate, which matters enormously once something goes wrong and you need to understand why.
- Move to a handoff pattern only if the task is genuinely sequential and a central coordinator would just be relaying messages with no real decision-making of its own.
- Reserve swarm patterns for genuinely exploratory problems where you're intentionally trading predictability for breadth, and where you have the evaluation maturity to actually validate the results that come out of a less legible architecture.
Coordination cost applies regardless of pattern
Every multi-agent pattern adds real coordination overhead: more latency from inter-agent calls, more surface area for something to go subtly wrong, and a genuinely harder evaluation problem than a single agent. Choosing the right pattern reduces this cost; it doesn't eliminate it. Budget for it explicitly rather than treating multi-agent complexity as free once you've decided it's justified.
How we approach this
We default to the supervisor pattern for its debuggability, and only move to a different pattern when the task's actual shape (not architectural preference) calls for it. Either way, we plan explicitly for the coordination and evaluation cost.