Multi-Agent Systems: Design Patterns for Collaboration

A single AI agent is like a talented intern: smart, capable, but easily overwhelmed by complex, multi-faceted tasks. Multi-Agent Systems (MAS) are like building a Company. You have specialists (Researchers, Coders, Critics) and Managers who coordinate them.

In 2025, the frontier of AI isn't building a better model; it's building a better Team.

1. Why Multi-Agent?

Why not just use one giant prompt with GPT-5?

1. Context Window Limits: A single prompt gets confused with too many instructions.
2. Role Specialization: An agent prompted to be a "Creative Writer" is bad at "Python Code Review."
3. Parallelism: Three agents can work on three different files simultaneously.

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2. Core Design Patterns

Pattern A: Reviewer-Executor (The Quality Gate)

The most common pattern for coding and content generation.

• Generator Agent: Produces the initial work (Code, Article).
• Critic Agent: Reviews the output against specific rules (Security, Style).
• Loop: If the Critic rejects it, the Generator tries again with feedback.

sequenceDiagram
    participant User
    participant Coder
    participant Reviewer
    
    User->>Coder: "Write a Python script to scrape data"
    Coder->>Reviewer: "Here is the code v1"
    Reviewer->>Coder: "Error: No error handling. Fix it."
    Coder->>Reviewer: "Here is code v2"
    Reviewer->>User: "Approved. Final Code."

Pattern B: Hierarchical Teams (The Boss)

Used for complex goals like "Build a Website."

• Manager Agent (The Brain): Breaks the goal into sub-tasks ("Write HTML", "Write CSS", "Write JS").
• Worker Agents: Execute the specific sub-tasks.
• Manager Agent: Aggregates the results and presents the final product.

Pattern C: Shared Memory (The Blackboard)

Agents don't talk to each other directly; they write to a shared "Blackboard" or Database.

• Agent A writes findings to the DB.
• Agent B reads the DB, adds its own analysis, and writes it back.
• This decouples the agents and allows for asynchronous collaboration.

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3. Implementing a "Virtual Software House"

Let's imagine a MAS designed to ship software features. We will use a conceptual framework similar to LangGraph or CrewAI.

The Team Roster

1. Product Manager: Defines requirements from user vague requests.
2. Tech Lead: Plans the architecture.
3. Developer: Writes the code.
4. QA Engineer: Writes the tests.

# Conceptual Multi-Agent Flow

workflow = StateGraph()

# Define Nodes (Agents)
workflow.add_node("product_manager", define_requirements_agent)
workflow.add_node("developer", write_code_agent)
workflow.add_node("qa", write_test_agent)

# Define Edges (Routing Logic)
workflow.add_edge("product_manager", "developer")
workflow.add_edge("developer", "qa")

def qa_router(state):
    if state["tests_passed"]:
        return "end"
    else:
        return "developer" # Go back and fix the code!

workflow.add_conditional_edges("qa", qa_router)

app = workflow.compile()

The "Silo" Effect

A risk in MAS is that agents become silos. The Developer might write code that the QA agent doesn't understand. Solution: System Instructions for all agents must include a "Common Language" or "Standard Operation Procedure (SOP)" to ensure consistency.

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4. Conflict Resolution

What happens when the Security Agent says "Block this port" but the Performance Agent says "Open this port for speed"?

In simple systems, you hardcode a hierarchy (Security > Performance). In advanced "Agentic" systems, you introduce a Mediator Agent.

• The Mediator takes both arguments.
• It reasons about the "Global Goal" (e.g., "Company Policy correlates strongly with Security").
• It makes a final decision.

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5. Conclusion

Multi-Agent Systems simulate human organizations. They introduce overhead (coordination) but unlock capability (specialization). As we move into 2026, we will stop asking "Which model did you use?" and start asking "What is your Agent Org Chart?"

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Next Up: Keeping these teams in check. Read our guide on Agentic Governance and Safety.