Orchestrator Agent Pattern: Coordinate Multi-Agent Workflows

Pattern essence

Orchestrator Agent is a pattern where one agent coordinates work across multiple executors: splits the task, delegates subtasks, tracks status, and combines results.

When to use it: when one large task must be distributed across executors and assembled into one result.

Instead of one agent doing everything alone, Orchestrator:

• analyzes the overall goal
• breaks it into subtasks
• sends subtasks to the right agents
• combines their responses into a single result

Problem

Imagine you need to prepare a go-to-market launch.

This is not one action, but several parallel streams:

• content and positioning
• analytics and forecasting
• legal review
• final release plan

When one agent runs everything sequentially, the system becomes "narrow at one point".

Without orchestration, a multi-step task loses speed and becomes fragile to local failures.

As a result:

• the process slows down because of serial execution
• cost increases because of extra load on the execution runtime
• one step failure blocks the whole task
• it is hard to keep timeouts, retries, and deadlines

That is the problem: without coordination across multiple executors, a complex task becomes slow, expensive, and weakly controlled.

Solution

Orchestrator adds a coordination layer to manage multiple executors.

Analogy: this is like a project manager. They do not perform every task themselves, but coordinate the team, order, and dependencies. That reduces delays and conflicts between steps.

Key principle: first coordinate subtasks and dependencies, then execute.

Separate agents execute subtasks, while orchestrator-policy defines the rules:

• what to run in parallel
• what to wait for sequentially
• how to handle timeout/retry/fallback
• when to consider the result complete

Controlled process:

1. Planning (Plan): break the goal into subtasks and dependencies
2. Dispatch: assign executors and limits
3. Parallel Execute: run independent steps simultaneously
4. Aggregate: collect results from executors
5. Validate/Done: validate the final output and finish

Monitoring (retries, timeouts, statuses) runs throughout execution.

This gives:

• lower total execution time
• control of partial failures
• centralized limits and rules
• a consistent final result

Works well if:

• the plan has explicit dependencies
• timeout/budget limits are set for executors
• there is policy for retry/fallback/partial result
• aggregation validates completeness and consistency

The model may "want" to run everything at once or everything in sequence, but orchestrator-policy is what fixes the right order and boundaries.

How it works

Orchestrator does not execute subtasks itself.

It controls the process:

• who should receive each subtask
• what time or budget limits to apply
• when to restart a failed step
• when to finish the full process

In code it looks like this

plan = plan_tasks(goal)

jobs = [dispatch_async(worker, task) for worker, task in plan]  # start parallel subtasks
results = await gather_with_limits(jobs, timeout_sec=30)  # collect with timeout/limit policies
results = retry_timeouts_once(results)  # simple example: one retry for timeout

final = aggregate(results)
return final

Orchestrator starts subtasks in parallel and waits for results with timeouts and limits.

How it looks during execution

Goal: prepare a go-to-market plan

Plan:
- Agent A: market and competitors
- Agent B: financial model
- Agent C: risks and compliance

Dispatch: all three subtasks start in parallel

Observe:
- Agent A: done in 6 s
- Agent B: done in 9 s
- Agent C: timeout, restart
- Agent C: done in 5 s after retry
- Between stages: results after retry return into the shared set for aggregate

Aggregate:
- A + B + C collected (C after retry)
- error policy applied
- single document produced

When it fits - and when it does not

Fits

Does not fit

Because Orchestrator adds extra coordination: task queues, waiting for results, and response aggregation.

How it differs from Routing

Routing answers: "who should receive the task".

Orchestrator answers: "how to synchronize multiple tasks and assemble the result".

When to use Orchestrator among other patterns

Use Orchestrator when there are multiple dependent steps and the correct execution order matters.

Quick test:

• if you need to "run a task through a clear chain of steps" -> Orchestrator
• if you need to "only choose the executor for a request" -> Routing Agent

How to combine with other patterns

• Orchestrator + Routing — Routing chooses the executor for a subtask, and Orchestrator tracks the overall workflow.
• Orchestrator + ReAct — each agent executes its part step by step, while Orchestrator assembles everything into one plan.
• Orchestrator + Supervisor — before critical actions, policies, risks, and execution budget are checked.

In short

Pros and Cons

FAQ

Q: Does Orchestrator always run subtasks in parallel?
A: No. Some steps can run sequentially if there are dependencies between them.

Q: What to do if one agent did not return a result?
A: Define an error policy: retry, timeout, fallback, or partial result. Orchestrator applies this policy before final aggregation.

Q: Is Orchestrator needed if Routing already exists?
A: Routing chooses one executor. Orchestrator coordinates many executors, often using Routing inside each subtask.

What next

Orchestrator coordinates work of multiple agents at once.

But who ensures they do not violate policies, budget, and security rules?