orchestrator–worker minta

in English: orchestrator-worker pattern

advancedspreading

A way of organizing work in which an orchestrating AI agent splits the job into subtasks, assigns them to executing agents, and then merges their results into a single answer.

In plain terms

Imagine a general contractor for a home renovation. They do not lay tiles or install wiring themselves; instead, they assess the work, call in specialists, instruct everyone on their tasks, and finally verify that everything fits together. In the orchestrator–worker pattern, the controlling agent (orchestrator) is the general contractor, and the executing agents (workers) are the specialists.

The pattern is useful if the task is too large or too scattered for a single agent. This is the case, for example, if twenty sources need to be reviewed, or many files of a program need to be modified at once. The workers can work in parallel, and each only focuses on its own part, so they do not get lost in the details.

It differs from a pre-written, rigid process in that subtasks are not predefined by a human. The controlling agent decides in real time what is needed, how many workers to launch, and what to request from them.

How it works

The orchestrating agent receives the task and plans which parts it is worth breaking it into.
For each subtask, it launches a worker agent and gives it a short, precise briefing: what the goal is, what to return, and the scope of the task.
Workers operate independently, often in parallel, using their own tools (such as search or file reading), and return only the essentials.
The coordinator compares incoming results and, if it detects gaps or contradictions, launches additional workers.
Finally, the coordinator assembles a single, coherent response and passes it to you.

An everyday example

You ask an AI assistant with a research function to compare electric car subsidies in five countries. In the background, a controlling agent launches a separate worker for each country; they search and read simultaneously, and a few minutes later you receive a single summary table.

Who is affected

developerCoding agents often split large tasks this way, so you need to understand when the extra cost is worth it and how to write good instructions. → Code

entrepreneur, executiveIf you automate complex processes with AI agents, this is one of the most common architectures, and it directly affects cost, speed, and reliability.

employeeDeep research and analytical AI tools that you use in your work often operate this way in the background. This partly explains why they are slower and more thorough than a simple chat. → Work & HR

A common misconception

MythThe more agents working on a task, the better the result will be.

In realityMultiple agents mean higher costs and more opportunities for misunderstanding. For simple, step-by-step tasks, a single agent is often faster, cheaper, and more accurate.

Limits and risks

It is much more expensive than a single agent because every worker consumes computing capacity separately, and costs multiply quickly.
A worker only knows what the controller specified in the instructions, so imprecise task assignment leads to erroneous or overlapping partial results.
The supervisor sees only a summary of the workers' output, so a confidently phrased error can easily enter the final answer.
It is not suitable for tasks where steps build tightly on each other, because independently working workers do not see each other's decisions.

Similar, but not the same

prompt chaining – There, the developer predefines the sequence of steps; here, the controlling agent decides on the fly what needs to be done and by whom.
parallelization – In simple parallelization, subtasks are predefined; in the orchestrator–worker pattern, the controller itself generates them based on the specific task.
agentraj – In a swarm, typically equal agents coordinate or pass work to each other without a permanent central leader, but here the hierarchy is clear: one directs, the others execute. The term is used loosely in practice, sometimes for a directed group of agents.

Related concepts

Broader

multi-agent system, agentes munkafolyamat, AI-agent

Narrower

alagent, orchestrator agent

On the edge

parallelization, prompt chaining, task decomposition, context window, agentraj, deep research

A brief history

The structure traces back to the old computer science 'master–worker' distributed processing pattern. For agents built on language models, Anthropic's December 2024 publication on building effective agents popularized it under the name 'orchestrator-workers'.

Where things stand

As of late 2026, it is an established approach in deep-research features and coding agents, and the major agent-building frameworks also offer it as a ready-made component. In everyday corporate processes, however, it is still being introduced cautiously because of cost and the difficulty of verification.

Status: 2026 November

Also known as

orchestrator-worker pattern · orchestrator-workers · orchestrator–worker pattern · controller–executor pattern · conductor–worker pattern · lead agent – subagent · supervisor pattern · main agent–sub-agent architecture

Sources

This entry was machine-edited and has undergone machine expert review; the “In the news” section updates automatically from Agent Age stories.