AI Agent Collaboration Tutorial: How to Make Multiple Agents Work Together

A single AI agent can write an email. But a squad of collaborating agents -- one researching, one writing, one reviewing -- produces work that's researched, well-written, and quality-checked. The challenge isn't creating individual agents. It's making them work together.

This tutorial covers the patterns and implementations for AI agent collaboration: how agents communicate, share context, hand off tasks, and produce unified output.

In this tutorial:

• Why agents need to collaborate
• Collaboration patterns
• Building a shared context system
• Implementing agent handoffs
• Building a 3-agent content squad
• Error handling in multi-agent systems
• No-code agent collaboration

Related tutorials: Multi-Agent System Tutorial · AI Agent Team Roles · Agent Communication Guide

Why Agents Need to Collaborate

No single AI model excels at everything. Collaboration lets you:

• Specialize -- each agent focuses on what it does best
• Cross-check -- agents review each other's work
• Pipeline -- break complex tasks into stages
• Scale -- run agents in parallel for faster results

Here's what collaboration looks like in practice:

[Research Agent] ──finds data──▶ [Writer Agent] ──drafts──▶ [Reviewer Agent]
       ▲                                                       │
       └───────────────feedback loop──────────────────────────┘

For the theory behind multi-agent systems, see our Multi-Agent AI Teams Guide.

Collaboration Patterns

There are four fundamental patterns for agent collaboration:

Pattern 1: Sequential Pipeline

Agents pass work in a line. Each agent receives the previous agent's output.

class SequentialPipeline:
    def __init__(self, agents: list):
        self.agents = agents

    def run(self, initial_input: str) -> str:
        current = initial_input
        for agent in self.agents:
            current = agent.process(current)
        return current

Best for: Content creation, data processing, report generation.

Pattern 2: Parallel Execution

Multiple agents work simultaneously on different aspects.

import asyncio

class ParallelExecution:
    def __init__(self, agents: list):
        self.agents = agents

    async def run(self, input_data: str) -> list[str]:
        tasks = [agent.process(input_data) for agent in self.agents]
        return await asyncio.gather(*tasks)

Best for: Research (multiple sources), brainstorming, A/B content.

Pattern 3: Supervisor-Worker

A supervisor agent delegates tasks to specialized workers.

class SupervisorWorker:
    def __init__(self, supervisor, workers: dict):
        self.supervisor = supervisor
        self.workers = workers

    def run(self, goal: str) -> str:
        plan = self.supervisor.plan(goal)
        results = {}
        for task in plan["tasks"]:
            worker = self.workers[task["role"]]
            results[task["id"]] = worker.process(task["instruction"])
        return self.supervisor.synthesize(results)

Best for: Complex projects, research reports, multi-format content.

Pattern 4: Round-Robin Critique

Agents take turns producing and reviewing.

class RoundRobinCritique:
    def __init__(self, agents: list, rounds: int = 3):
        self.agents = agents
        self.rounds = rounds

    def run(self, prompt: str) -> str:
        current = prompt
        for round_num in range(self.rounds):
            for i, agent in enumerate(self.agents):
                role = "produce" if i == 0 else "critique and improve"
                current = agent.process(f"{role} this: {current}")
        return current

Best for: Quality-sensitive output, creative writing, strategic documents.

Building a Shared Context System

Agents need to share state. Here's a simple shared context:

from dataclasses import dataclass, field
from datetime import datetime

@dataclass
class SharedContext:
    goal: str
    messages: list = field(default_factory=list)
    artifacts: dict = field(default_factory=dict)
    metadata: dict = field(default_factory=dict)

    def add_message(self, agent_name: str, message: str):
        self.messages.append({
            "agent": agent_name,
            "message": message,
            "timestamp": datetime.now().isoformat()
        })

    def add_artifact(self, name: str, content: str):
        self.artifacts[name] = content

    def get_artifact(self, name: str) -> str:
        return self.artifacts.get(name, "")

    def get_recent_context(self, n: int = 5) -> list:
        return self.messages[-n:]

Using Shared Context

context = SharedContext(goal="Write a blog post about AI agent collaboration")

# Research agent adds findings
context.add_message("researcher", "Found 5 key collaboration patterns in multi-agent systems")
context.add_artifact("research_notes", "1. Sequential pipeline... 2. Parallel execution...")

# Writer agent reads research and creates draft
research = context.get_artifact("research_notes")
context.add_message("writer", f"Drafting blog post based on research: {research[:100]}...")
context.add_artifact("draft", "# AI Agent Collaboration...\n\n...")

# Reviewer agent reads draft and provides feedback
draft = context.get_artifact("draft")
context.add_message("reviewer", "The draft covers patterns well but needs code examples")
context.add_artifact("feedback", "Add Python code for each pattern. Include error handling.")

Implementing Agent Handoffs

Agent handoffs are the transitions where one agent passes control to another. There are three approaches:

Approach 1: Fixed Handoff Chain

class FixedHandoffChain:
    def __init__(self, agent_sequence: list):
        self.agents = agent_sequence
        self.context = SharedContext(goal="")

    def run(self, goal: str) -> str:
        self.context.goal = goal
        output = goal

        for agent in self.agents:
            output = agent.process(output, self.context)
            self.context.add_message(agent.name, f"Completed: {output[:100]}...")

        return output

Approach 2: Dynamic Routing

class DynamicRouter:
    def __init__(self, router_agent, specialist_agents: dict):
        self.router = router_agent
        self.specialists = specialist_agents

    def run(self, task: str) -> str:
        decision = self.router.route(task)
        
        if decision["type"] == "single":
            return self.specialists[decision["agent"]].process(task)
        
        if decision["type"] == "sequential":
            result = task
            for agent_name in decision["agents"]:
                result = self.specialists[agent_name].process(result)
            return result
        
        if decision["type"] == "parallel":
            import asyncio
            results = asyncio.gather(*[
                self.specialists[name].process(task)
                for name in decision["agents"]
            ])
            return self.router.merge(list(results))

Approach 3: Event-Driven Handoffs

class EventBus:
    def __init__(self):
        self.subscribers = {}

    def subscribe(self, event_type: str, agent):
        self.subscribers.setdefault(event_type, []).append(agent)

    def publish(self, event_type: str, data: dict):
        for agent in self.subscribers.get(event_type, []):
            agent.handle_event(event_type, data)

bus = EventBus()
bus.subscribe("research_complete", writer_agent)
bus.subscribe("draft_complete", reviewer_agent)
bus.subscribe("revision_needed", writer_agent)

Building a 3-Agent Content Squad

Let's put it all together with a real example: a content creation squad with Researcher, Writer, and Reviewer agents.

from openai import OpenAI

client = OpenAI()

class Agent:
    def __init__(self, name: str, system_prompt: str):
        self.name = name
        self.system_prompt = system_prompt

    def process(self, input_text: str, context: SharedContext) -> str:
        recent = context.get_recent_context(3)
        context_str = "\n".join([f"{m['agent']}: {m['message']}" for m in recent])
        
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content": self.system_prompt},
                {"role": "user", "content": f"Context:\n{context_str}\n\nCurrent task: {input_text}"}
            ]
        )
        return response.choices[0].message.content

researcher = Agent(
    name="researcher",
    system_prompt="You research topics thoroughly. Find key facts, statistics, and expert opinions. Structure your findings clearly."
)

writer = Agent(
    name="writer",
    system_prompt="You write clear, engaging content. Use the research provided to create well-structured articles with headings, examples, and actionable advice."
)

reviewer = Agent(
    name="reviewer",
    system_prompt="You review content for accuracy, clarity, and completeness. Provide specific feedback and a quality score from 1-10."
)

# Run the squad
context = SharedContext(goal="Write about multi-agent AI collaboration patterns")

research = researcher.process("Research AI agent collaboration patterns", context)
context.add_artifact("research", research)

draft = writer.process(f"Write an article based on this research:\n{research}", context)
context.add_artifact("draft", draft)

review = reviewer.process(f"Review this article:\n{draft}", context)
context.add_artifact("review", review)

print("=== Final Article ===")
print(draft)
print("\n=== Review ===")
print(review)

Error Handling in Multi-Agent Systems

When one agent fails, the whole pipeline can break. Here's how to handle failures:

class ResilientPipeline:
    def __init__(self, agents: list, max_retries: int = 2):
        self.agents = agents
        self.max_retries = max_retries

    def run(self, goal: str) -> str:
        context = SharedContext(goal=goal)
        current = goal

        for agent in self.agents:
            for attempt in range(self.max_retries + 1):
                try:
                    current = agent.process(current, context)
                    context.add_message(agent.name, "Success")
                    break
                except Exception as e:
                    context.add_message(agent.name, f"Error (attempt {attempt + 1}): {e}")
                    if attempt == self.max_retries:
                        context.add_message(agent.name, "Skipping due to repeated failures")
                        continue

        return current

For more on debugging multi-agent workflows, see our guide on monitoring and debugging AI workflows.

No-Code Agent Collaboration

Building agent collaboration from scratch is educational but time-consuming. Ivern AI provides built-in agent squad collaboration:

• Pre-configured agent roles -- Researcher, Writer, Coder, Reviewer ready to go
• Automatic handoffs -- agents pass context and results to each other
• Shared task board -- see every agent's status and output in real-time
• Cross-provider squads -- mix Claude, GPT-4, and other models in one squad
• BYOK pricing -- use your own API keys, no markup

Build your first collaborating squad: ivern.ai/signup (free tier included)

Key Takeaways

1. Start with sequential pipelines -- they're simplest to build and debug
2. Use shared context -- agents need a common knowledge base
3. Plan your handoff strategy -- fixed chains for simple tasks, dynamic routing for complex ones
4. Build in error handling -- agent failures are inevitable
5. Measure quality -- add evaluation agents to catch problems early

Next tutorials: AI Agent Team Roles · AI Agent Orchestration · Multi-Agent Workflow Examples