An orchestrator is the component that coordinates the steps of an AI workflow. It decides which task to run, which agent or tool to call, what data to pass along, and when to finish or request human intervention. In a simple system, this role can be implemented with plain code, without a dedicated AI model.
How it works
The orchestrator receives a goal and applies a sequence of rules. First, it interprets the request or the current state of the work. Then, it chooses an action, such as querying a knowledge base, calling a service, or delegating part of the work to another agent. Finally, it receives the result and decides if the task is done, needs another step, or should be handed to a person.
This cycle can be fixed or adaptive. In a fixed flow, the steps follow a predefined order, such as validating data, generating text, and requesting approval. In an adaptive flow, the system chooses the next step based on the previous result. This flexibility can help with varied tasks, but it makes behavior harder to predict and test.
The orchestrator is also responsible for operational aspects: maintaining task state, controlling permissions, logging actions, and handling errors or time limits. It can route work to specialized AI agents and coordinate agent orchestration, but it is not necessarily the agent that executes each activity.
Practical example
Imagine a flow that prepares a commercial proposal. The orchestrator receives the client data, asks a research agent to gather authorized information, sends the context to another agent to draft a first version, and requests a consistency check.
If the check finds a required field missing, the flow can return to the collection step. If it is complete, the orchestrator presents the draft to a person for approval, instead of sending it automatically. Each decision should have an observable reason and clear rules, especially when it involves personal data or external actions.
What it's for
An orchestrator serves to turn isolated calls to models and tools into a coordinated process. It helps split the work, route information, track progress, and apply controls between steps.
It is useful when a task requires more than one action, such as researching, writing, and reviewing, or when different agents and services have distinct roles. For a simple question that can be answered in a single call, a complex orchestrator may add work without benefit.
When designing a flow, start with the minimal sequence that delivers the result. Define what data each step can access, how failures are handled, and which actions need approval. Then test alternative paths, not just the ideal case. That way, the orchestrator improves coordination without hiding important decisions inside a chain that is hard to understand.