Multi-Agent Systems: How to Orchestrate Teams of AI Agents

Multi-agent systems decompose complex tasks across multiple specialized agents, each optimized for a sub-problem. This mirrors how human organizations work: different specialists, coordinated by a manager, each doing what they're best at. The result is higher quality, greater speed through parallelism, and lower cost through model specialization.

Core multi-agent patterns

1. Orchestrator–Worker

A central orchestrator agent breaks down the task, delegates sub-tasks to specialized worker agents, and synthesizes their outputs into a final result. Workers don't know about each other — only the orchestrator does coordination.

Best for: research pipelines, report generation, software feature implementation

2. Sequential Pipeline

Agents are arranged in a chain: each agent consumes the output of the previous one and passes its result forward. Optimized for workflows with clear stage dependencies: extract → transform → validate → format.

Best for: document processing, ETL workflows, content generation with editing passes

3. Parallel Fan-Out/Fan-In

An orchestrator fans tasks out to multiple agents running in parallel, then fan-in collects and merges their results. Dramatically reduces wall-clock time for tasks that can be parallelized.

Best for: competitive analysis, multi-source research, batch processing

4. Debate / Critic-Actor

One agent generates a response (actor); another critiques it (critic). The actor refines based on critique, iterating until the critic approves or a max round limit is hit. Improves output quality significantly on high-stakes tasks.

Best for: contract review, code security audit, medical documentation

5. Hierarchical Agent Tree

Nested orchestration with multiple levels. A top-level orchestrator manages sub-orchestrators, each managing their own worker teams. Scales to very large, complex tasks with many concurrent sub-problems.

Best for: full product development, large-scale data analysis, complex research projects

Implementation example: Orchestrator–Worker in MoltBot

# Define specialized agents
research_agent = Agent(
    name="researcher",
    model="claude-opus-4",
    tools=["web_search", "read_url"]
)

writer_agent = Agent(
    name="writer",
    model="claude-sonnet-4",  # cheaper for generation
    tools=["code_exec"]
)

reviewer_agent = Agent(
    name="reviewer",
    model="gpt-5",
    tools=[]  # review only, no tools needed
)

# Orchestrator coordinates the team
orchestrator = Orchestrator(
    agents=[research_agent, writer_agent, reviewer_agent],
    strategy="sequential"
)

result = orchestrator.run(task="Write a technical blog post about feature flags")
      

Key design decisions

Model selection per agent role

Not every agent needs the most expensive model. Orchestrators benefit from Claude Opus 4 or GPT-5 for their reasoning quality. Workers doing retrieval, formatting, or simple extraction can use Gemini Flash or Claude Haiku at a fraction of the cost. This model-tier matching is one of the most impactful cost optimizations in multi-agent systems.

Context passing between agents

How you pass context between agents significantly affects quality. Three approaches:

Full transcript pass: Each agent sees the complete output of previous agents. High quality, high token cost.
Summary pass: A compression agent summarizes previous outputs before passing forward. Good balance of cost and quality.
Structured output pass: Agents produce JSON/typed outputs that downstream agents consume structurally. Best for pipelines with clear schemas.

When to stop iterating

Critic-actor loops need stopping conditions to avoid infinite refinement. Common strategies: max rounds (typically 3–5), quality score threshold from a judge agent, or consensus between multiple critic agents. Build in hard limits — unbounded loops are expensive.

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