Orchestrator and Subagent Systems

An orchestrator-subagent architecture uses one central controller to coordinate multiple specialized agents. The orchestrator receives the user’s goal, decomposes it into subtasks, assigns work to subagents, collects results, resolves conflicts, and produces the final output.

This pattern is useful when a task requires different skills or perspectives. Instead of asking one agent to do everything, you create specialized roles.

Example roles:

• Research agent
• Coding agent
• Test agent
• Security reviewer
• Documentation writer
• Data analyst
• Project planner

The orchestrator is responsible for coordination.

Basic architecture

A simple flow looks like this:

User goal
  ↓
[Orchestrator]
  ├── [Research subagent]
  ├── [Analysis subagent]
  ├── [Writing subagent]
  └── [Reviewer subagent]
  ↓
Synthesize final answer

The subagents may be separate model calls with different prompts, tools, memories, or permissions. They do not need to be separate deployed services. The architectural idea is specialization plus coordination.

Why use subagents?

Subagents can improve reliability by narrowing each agent’s responsibility. A research agent can focus on source gathering. A reviewer can focus on quality. A coding agent can focus on implementation. A test agent can focus on validation.

Specialization helps because prompts become simpler. Instead of one enormous system prompt that says “research, code, test, review, write, and manage risk,” each subagent gets a focused instruction.

For example:

Research subagent: Find relevant sources and extract facts. Do not write final recommendations.

Reviewer subagent: Check the draft for unsupported claims, missing caveats, and policy violations. Do not add new claims.

Clear separation reduces role confusion.

Orchestrator responsibilities

The orchestrator usually handles:

• Understanding the user goal
• Breaking the goal into subtasks
• Selecting subagents
• Passing relevant context
• Tracking progress
• Merging outputs
• Resolving disagreement
• Enforcing stopping conditions
• Deciding when to ask the user

A simple orchestrator pseudocode:

def run_orchestrated_task(goal):
    plan = orchestrator.create_task_plan(goal)
    results = {}

    for task in plan.tasks:
        agent = select_subagent(task)
        results[task.id] = agent.run(task, context=results)

    review = reviewer.run(goal, results)
    final = orchestrator.synthesize(goal, results, review)
    return final

More advanced systems allow parallel subagents and iterative review cycles.

Crew-style systems

Frameworks such as CrewAI popularized the idea of defining agents with roles, goals, tools, and tasks. A “crew” can represent a team of agents working together. This maps naturally to business workflows where different roles contribute to a deliverable.

A simplified crew definition might be:

researcher = Agent(
    role="Technical Researcher",
    goal="Find accurate sources and extract relevant facts",
    tools=[web_search, fetch_url]
)

writer = Agent(
    role="Technical Writer",
    goal="Turn research notes into clear educational content",
    tools=[]
)

reviewer = Agent(
    role="Quality Reviewer",
    goal="Check for accuracy, completeness, and unsupported claims",
    tools=[]
)

The value is not that agents have personas. The value is that responsibilities are separated.

Conflict and disagreement

Subagents may disagree. A research agent may find conflicting sources. A security reviewer may reject a coding agent’s proposed implementation. A writer may simplify details that a domain expert considers essential.

The orchestrator needs a conflict policy:

If sources conflict, prefer primary sources.
If reviewer flags a safety issue, revise before final output.
If implementation and tests disagree, trust test results.
If confidence is low, report uncertainty rather than hiding it.

Without a conflict policy, the orchestrator may simply blend incompatible outputs.

Avoiding unnecessary multi-agent complexity

Multi-agent systems sound impressive, but they add cost, latency, and coordination complexity. Many tasks do not need multiple agents.

Use orchestrator-subagent systems when:

• The task has distinct specialized subtasks.
• Parallel work can save time.
• Review quality matters.
• Different tools or permissions are needed for different roles.
• The workflow benefits from separation of concerns.

Avoid them when:

• One agent can handle the task simply.
• The overhead exceeds the value.
• Subagents would mostly duplicate each other.
• You do not have evaluation for coordination quality.

Example: software change workflow

A multi-agent coding workflow might look like:

[Orchestrator]
  → asks Codebase Analyst to locate relevant files
  → asks Implementer to propose patch
  → asks Test Runner to run tests
  → asks Security Reviewer to inspect risk
  → synthesizes final report

This is more controlled than a single agent editing code and declaring success.

Practical takeaway

Orchestrator-subagent systems are useful when complexity demands specialization. The orchestrator provides coordination; subagents provide focused capability.

The risk is coordination overhead. Use this architecture deliberately. Define roles clearly, limit each subagent’s authority, create conflict-resolution rules, and evaluate both individual outputs and final synthesis.