Workflows & orchestration
What is AI agent orchestration, and when do you need more than one agent?
AI agent orchestration is the coordination that lets several specialised agents tackle one goal together. A coordinator splits the work, routes each piece to the right agent, manages the context that flows between them, and merges the answers. You only need it once a single agent can no longer do the job reliably.
Orchestration is what you add when one agent is no longer enough. A single agent can take you a long way, but as the work spreads across several domains, or the tool list grows past what one prompt can hold, the job starts to exceed what any one agent does reliably. At that point the answer is usually more agents, each kept small and specialised, with something to coordinate them. Microsoft describes this as multiple specialised agents that coordinate to solve a problem, where an orchestrator or a peer protocol manages work distribution, context sharing, and result aggregation.[1]
The coordinator is the heart of it. It takes the overall goal, breaks it into subtasks, sends each one to the agent best suited to it, keeps track of what information passes between them, and stitches the separate answers back into a single result. Anthropic’s orchestrator-workers pattern is one clean version of this: a central model dynamically breaks down a task, hands the pieces to worker models, and then synthesises what comes back.[1][2] The route through the work is not fixed in advance. The coordinator decides the order, and decides what to do when a step fails.
It helps to separate orchestration from a plain workflow. In a workflow, the path is wired up ahead of time in code, so the model fills in steps along a track someone already laid. Orchestration leaves more of that routing to a coordinating model, which is more flexible and also harder to predict.[2] Neither is automatically better. A fixed workflow is easier to test and cheaper to run, and most production systems are workflows for exactly that reason.
So the honest rule is to reach for orchestration last, not first. Use the simplest setup that does the job, and only add a coordination layer once a single agent genuinely cannot keep up.[1] When you do cross that line, the patterns for wiring agents together are fairly well settled.
Each rung up that ladder is a real cost, not just a capability gained. A single agent has one prompt to maintain and one failure mode to reason about. A fixed workflow adds the code that wires its steps together, which is now something that can itself have bugs. Orchestration adds a coordinator, the context-passing between agents, and the failure modes of coordination itself — an agent that never gets its turn, or two agents that each assume the other handled something. None of that is a reason to avoid orchestration when the job genuinely needs it; it is a reason not to reach for it by default.
References
- AI Agent Orchestration Patterns — Microsoft Azure
- Building Effective Agents — Anthropic
Common questions
- Do I always need multiple agents?
- No. Most jobs are handled fine by one agent or a plain workflow. Microsoft's guidance is to use the lowest level of complexity that reliably meets your requirements, because every extra agent adds coordination overhead, latency, and new ways to fail.
- What does the orchestrator actually do?
- It breaks the goal into subtasks, decides which agent handles each one, passes the right context between them, and assembles the final result. In Anthropic's orchestrator-workers pattern, a central model dynamically breaks down tasks, delegates them to worker models, and synthesises their output.