An orchestrator is an agent responsible for the planning and coordination of a larger whole. It receives an overarching assignment, assesses which steps are needed, determines which subagent executes which step and processes the returning results.

The orchestrator itself generally does not carry out the substantive tasks. It does not write texts, look up data or make calculations. That is the work of the subagents. The orchestrator decides, delegates and integrates.

This is comparable to a project manager in a team. The project manager knows the end destination, divides the work and monitors progress. The specialists execute. The distinction between these roles makes the system more manageable.

There are two common communication patterns. With synchronous orchestration the orchestrator waits for the result from each subagent before giving the next instruction. This is simpler to implement and debug but slower when tasks can be executed in parallel.

With asynchronous orchestration the orchestrator starts multiple subagents simultaneously and processes results as they come in. This is more efficient but more complex, because the orchestrator must track which tasks are done and which results it has already received.

The choice depends on the nature of the tasks. If step B depends on the output of step A, synchronous execution is needed. If step B and step C are independent, they can run in parallel.

A subagent works best when it receives exactly the context it needs for its specific task. The orchestrator is responsible for assembling that context: which information is relevant for this particular subagent, and what does it not need to know?

This is an underestimated aspect of orchestration design. If a subagent receives too much irrelevant information, its context fills up and the quality of its output decreases. If it receives too little information, it cannot perform its task properly. The orchestrator filters and structures.

A robust orchestrator has an error handling strategy. What does it do if a subagent returns an error? Options include retrying, engaging an alternative subagent, requesting human intervention, or stopping the workflow with a clear error message.

The right choice depends on the nature of the error. Temporary errors (network timeout, overloaded API) call for a retry strategy. Structural errors (the subagent does not understand the assignment) call for redesigning the prompt instructions. Errors with major consequences call for human intervention.

At Mach8 we build orchestrators with explicit error handling logic as a standard part of every design.

An orchestrator adds value when:

• The system contains more than two or three specialized agents
• The task distribution is dynamic and depends on the content of the assignment
• Parallel execution is desirable to reduce turnaround time
• There is a need for central logging and progress monitoring

For simple workflows with one or two agents, an orchestrator layer is unnecessary and only adds complexity. Skip it in those cases.

A content pipeline at Mach8 for generating product pages works as follows. An orchestrator receives a list of products. For each product it directs a research agent that retrieves product specifications, a writing agent that generates text based on the specs and tone-of-voice guidelines, and a quality agent that checks the output against a checklist. If the quality check passes, the orchestrator sends the content to the CMS. If it fails, it redirects the writing agent with feedback.

An AI orchestrator makes complex multi-agent systems manageable by creating a clear separation between planning, execution and integration. The pattern requires careful design but pays back in modularity and maintainability.

Want to build a structured multi-agent architecture for your business process? Get in touch with Mach8 or see our AI agents services.