Multi-Agent Orchestration Patterns: When One Agent Isn't Enough

Adding a second agent to a system isn't automatically an improvement, coordination between multiple agents introduces real complexity, more places for context to get lost, more places for a failure to hide. It's worth being deliberate about when splitting a task across multiple agents actually earns that added complexity.

When a single agent genuinely struggles

A single agent handling a task that requires meaningfully different kinds of expertise or context, deep research followed by code implementation followed by testing, for example, can end up with an unwieldy system prompt trying to cover every mode at once, and a context window increasingly cluttered with information relevant to one sub-task but not another. Splitting distinct phases into separate, focused agents can keep each one's context and instructions cleaner and more targeted.

Sequential handoff

The simplest multi-agent pattern: one agent completes its portion of a task and passes its output to the next agent in a defined sequence, a research agent handing findings to a writing agent, for instance. This works well for tasks with a genuinely linear structure, and its main failure mode is information loss at the handoff, if the first agent's output doesn't capture everything the next agent actually needs, quality degrades at exactly the seam between the two.

Supervisor-worker

A coordinating agent breaks a task into subtasks and delegates them to specialized worker agents, then integrates their results. This handles more complex, less strictly linear tasks than a simple sequential handoff, at the cost of real added complexity in the supervisor's own logic for delegating well and integrating results coherently, a supervisor making poor delegation decisions can produce a worse outcome than a single capable agent would have.

Parallel fan-out

Multiple agents work on independent sub-parts of a task simultaneously, then results get combined, useful when subtasks genuinely don't depend on each other's output and speed matters. The real risk here is inconsistency between parallel outputs, without some coordination or a final reconciliation step, results from independent agents can conflict or duplicate work in ways a single sequential process wouldn't.

The coordination tax that's easy to underestimate

Every additional agent in a system adds a place where context can be lost or misinterpreted in the handoff, adds latency from the additional round trips, and adds a debugging burden, when something goes wrong, figuring out which agent in the chain caused it is genuinely harder than debugging a single agent's behavior. This tax is real and worth weighing honestly against the benefit multi-agent design is expected to provide for a specific task.

A practical rule of thumb

Start with a single agent and only split into multiple agents once there's a concrete, observed problem a single agent is genuinely struggling with, an unwieldy context, a clear mixing of unrelated concerns, rather than adopting a multi-agent architecture upfront because it sounds more sophisticated. Multi-agent systems solve a real class of problems, but they solve it at a real cost, and that cost is only worth paying once a single well-designed agent has actually proven insufficient.