Effective Multi-Agent Collaboration: 4 Practical Structures Beyond Just Scaling Agents

In the realm of AI agents, merely increasing the number of agents doesn't equate to effective collaboration. True productivity lies in structured coordination. This article dives into four practical multi-agent structures, complete with actionable code examples, to help you leverage agent collaboration for real-world tasks.

Why "More Agents" Isn't Synonymous with "Collaboration"

Opening multiple agent sessions side by side doesn't mean they're working together. For instance, in a development scenario, you might have one agent fixing bugs, another running tests, and others handling frontend and backend tasks. Without shared context, the bug-fixing agent might be unaware that the testing agent has already encountered the same bug, leading to redundant work.

To achieve true collaboration, three core elements must be defined:

• Division of Labor: Who is responsible for what task?
• Handover Protocol: How do agents exchange outputs?
• Decision Authority: Who has the final say in disputes or next steps?

4 Practical Multi-Agent Structures for Real-World Use

1. Orchestrator-Workers: Dynamic Task Allocation

This structure is ideal when a single agent can't handle a large task, and the sub-tasks aren't known in advance.

• How It Works: A central orchestrator agent analyzes the main task and dynamically splits it into sub-tasks, assigning each to a specialized worker agent. Once all workers complete their tasks, the orchestrator consolidates the results.

# Main orchestrator logic
def orchestrate_large_task(task):
    sub_tasks = analyze_and_split_task(task)  # Split into, e.g., "debug_error", "scan_code", "run_tests"
    results = []
    for sub_task in sub_tasks:
        # Launch a SubAgent with independent context for each sub-task
        sub_agent = SubAgent(
            api_key="your_api_key",
            context=sub_task,
            model="M3"
        )
        result = sub_agent.run()
        results.append(result)
    return consolidate_results(results)

• Key Insight: Sub-tasks are allocated on the fly, and workers only return summarized conclusions to the orchestrator, keeping context manageable.

2. Pipeline: Sequential Task Handover

Use this when a task can be broken into fixed, sequential steps (e.g., "write code → review code → test code").

• How It Works: The output of one agent becomes the input of the next, creating a linear workflow.

# Agent 1: Write code
def write_code(task):
    code = generate_code(task)
    push_to_git(code, "feature-branch")
    return "Code pushed to feature-branch"

# Agent 2: Review code
def review_code(git_link):
    code = pull_from_git(git_link)
    feedback = analyze_code_quality(code)
    push_feedback(feedback, "feedback-branch")
    return "Feedback pushed to feedback-branch"

# Agent 3: Test code
def test_code(feedback_link):
    feedback = pull_from_git(feedback_link)
    test_results = run_tests(feedback)
    return test_results

• Best Practice: Define a fixed output format for each step (e.g., JSON with task_completed, output, and notes) to ensure smooth handover.

3. Parallelization: Simultaneous, Independent Work

This structure shines when a task can be split into independent sub-tasks that can run concurrently (e.g., developing frontend and backend modules separately).

• How It Works: Split the main task into non-overlapping sub-tasks and assign each to a separate agent. Once all agents finish, merge their outputs.

from concurrent.futures import ThreadPoolExecutor

def parallel_development(task):
    sub_tasks = split_into_independent_tasks(task)  # e.g., "frontend", "backend", "tests"
    results = []
    with ThreadPoolExecutor(max_workers=3) as executor:
        futures = [executor.submit(run_agent, sub_task) for sub_task in sub_tasks]
        for future in futures:
            results.append(future.result())
    return merge_results(results)

def run_agent(sub_task):
    agent = Agent(api_key="your_api_key", task=sub_task, model="M3")
    return agent.execute()

• Critical Check: Ensure sub-tasks are truly independent. If two agents modify the same logic, merging will fail. Use Git worktrees or environment isolation to prevent conflicts.

4. Evaluator-Optimizer: Iterative Refinement

Use this when you need to verify and improve an agent's output (e.g., "generate a report → critique it → revise it").

• How It Works: One agent produces an initial output, a separate evaluator agent critiques it, and the original agent (or a new one) revises based on the feedback. This loops until the output meets standards.

def iterative_improvement(task):
    # Agent 1: Generate initial output
    generator = Agent(api_key="your_api_key", role="generator", task=task)
    output = generator.execute()

    # Agent 2: Evaluate output
    evaluator = Agent(
        api_key="your_api_key",
        role="evaluator",
        task=f"Critique this output: {output}"
    )
    feedback = evaluator.execute()

    # Agent 1: Revise based on feedback
    revised_output = generator.execute(f"Revise based on feedback: {feedback}")
    return revised_output

• Must-Do: Keep the evaluator agent's context independent from the generator's. Sharing context reduces the evaluator's objectivity.

When to Use Multi-Agent vs. Single-Agent

• Read-Heavy Tasks (e.g., research, data scraping, code scanning): Multi-agent parallelization is efficient.
• Write-Heavy Tasks (e.g., code generation, creative writing): A single agent with well-managed context is often more stable, as multi-agent handovers can introduce inconsistencies.

Actionable Workflow for Your Tasks

1. Define Collaboration Basics: Clarify who does what, how outputs are handed over, and who makes final decisions.
2. Assess Task Divisibility: Determine if the task can be split into independent or sequential steps.
3. Choose the Right Structure: Pick from Orchestrator-Workers, Pipeline, Parallelization, or Evaluator-Optimizer (or stick with a single agent if it suffices).

By focusing on structure rather than just quantity, you can turn a crowd of agents into a cohesive, productive team.

常见问题

Which structure should I start with for a typical development project?

Start with Pipeline for code generation tasks (write → review → test). It's the simplest to implement and debug because the handover points are fixed. Once you're comfortable, add Parallelization for independent modules (frontend + backend simultaneously). Save Orchestrator-Workers for complex, unpredictable tasks where you genuinely don't know the sub-task breakdown upfront.

How do I prevent agents from overwriting each other's work in Parallelization mode?

Three techniques: (1) Use Git worktrees so each agent has its own isolated filesystem, (2) assign each agent a non-overlapping file/module scope in the prompt, (3) have a dedicated merge agent that resolves conflicts after all parallel agents complete. The Git worktree approach is the most robust — each agent works in .claude/worktrees/agent-N/ and only the merge agent touches the main branch.

When is single-agent better than multi-agent?

Single-agent wins when: (1) the task requires deep context that's expensive to replicate across agents, (2) the output needs stylistic consistency (creative writing, UI design), (3) the task is small enough that coordination overhead exceeds execution time. A good rule of thumb: if explaining the task to multiple agents takes longer than doing it with one, stick with single-agent.