Building a Multi-Agent System: Coordinating AI Agents for Complex Workflows

Single-agent systems hit limits quickly. One LLM making every decision for a complex workflow leads to token waste, context confusion, and poor specialization. Multi-agent systems solve this by dividing work among specialized agents that communicate and coordinate. Here's how to design multi-agent architectures that actually work.

Why Multi-Agent Architecture?

A single agent handling a complex task like "research a market, write a report, and create a presentation" will:

• Blow through token budgets on irrelevant details
• Lose track of context across drastically different subtasks
• Apply one reasoning style to problems that need diverse approaches

A multi-agent system assigns each subtask to a specialized agent with its own system prompt, tools, and memory:

Orchestrator Agent
    ├── Research Agent (tools: web search, document retriever, database)
    ├── Analysis Agent (tools: Python REPL, statistical models, data viz)
    ├── Writing Agent (tools: knowledge base, brand voice guide, style checker)
    └── Review Agent (tools: rubric evaluator, plagiarism checker, fact verifier)

Communication Patterns

Agents need to communicate. There are three primary patterns:

Pattern 1: Orchestrator-Worker

A central orchestrator delegates tasks to worker agents and synthesizes their outputs:

class Orchestrator:
    def __init__(self):
        self.agents = {
            "research": ResearchAgent(),
            "analysis": AnalysisAgent(),
            "writer": WritingAgent(),
            "reviewer": ReviewAgent()
        }
    
    async def execute(self, task):
        # Phase 1: Research
        research_results = await self.agents["research"].run(task)
        
        # Phase 2: Analyze
        analysis = await self.agents["analysis"].run(research_results)
        
        # Phase 3: Write
        draft = await self.agents["writer"].run(task, research_results, analysis)
        
        # Phase 4: Review
        final = await self.agents["reviewer"].run(draft)
        
        return final

This pattern works well when the workflow is sequential and predictable. The orchestrator is a simple controller—it doesn't need to be an LLM.

Pattern 2: Debate and Consensus

Multiple agents independently analyze the same problem and compare results:

def debate_resolution(problem, models=["gpt-4o", "claude-3-5-sonnet", "gemini-2-pro"]):
    """Run parallel analysis and synthesize the best answer."""
    responses = {}
    
    for model in models:
        responses[model] = query_model(model, problem)
    
    # Synthesizer agent reconciles differences
    synthesis = query_model(
        "gpt-4o",
        f"Reconcile these three analyses into a single answer. Note disagreements:\n"
        f"gpt-4o: {responses['gpt-4o']}\n"
        f"claude: {responses['claude-3-5-sonnet']}\n"
        f"gemini: {responses['gemini-2-pro']}\n"
        f"Identify areas of agreement and explain remaining disagreements."
    )
    
    return synthesis

This pattern is expensive (multiple API calls per query) but produces more robust results for high-stakes decisions like code review, security analysis, or financial assessment.

Pattern 3: Supervisor with Reflection

A supervisor agent monitors worker agents and provides feedback:

class Supervisor:
    def __init__(self):
        self.worker = CodeGenerationAgent()
        self.quality_threshold = 0.85
    
    async def supervise_task(self, coding_task):
        max_attempts = 3
        
        for attempt in range(max_attempts):
            code = await self.worker.generate(coding_task)
            
            # Review the output
            review = await self.review_code(code, coding_task)
            
            if review.score >= self.quality_threshold:
                return code, review
            
            # Provide feedback for improvement
            self.worker.receive_feedback(review.feedback)
        
        return None, {"error": "Max attempts reached", "last_review": review}
    
    async def review_code(self, code, task):
        return await query_model("gpt-4o", f"""
        Review this code for:
        1. Correctness: Does it solve the problem?
        2. Security: Any vulnerabilities?
        3. Performance: Efficient algorithm?
        4. Style: Follows best practices?
        
        Task: {task}
        Code: {code}
        
        Score 0-1 and provide specific feedback.
        """)

Task Delegation Strategies

The orchestrator needs a reliable way to select which agent handles which task:

def select_agent(task_description):
    """Classify the task and route to the appropriate agent."""
    
    task_type = classifier_llm.invoke(f"""
    Classify this task into one category:
    - RESEARCH: Finding information, gathering data
    - ANALYSIS: Processing data, running calculations
    - CREATION: Writing, designing, generating content
    - REVIEW: Evaluating, testing, checking quality
    
    Task: {task_description}
    Category:
    """)
    
    agent_map = {
        "RESEARCH": "research_agent",
        "ANALYSIS": "analysis_agent",
        "CREATION": "writing_agent",
        "REVIEW": "review_agent"
    }
    
    return agent_map.get(task_type.strip(), "fallback_agent")

The classifier itself can be a small, fast model (GPT-4o-mini or Claude Haiku), keeping costs low while the specialized agents use more capable models.

Shared Memory and State

Multi-agent systems need shared state to avoid redundant work:

import redis.asyncio as redis

class SharedMemory:
    def __init__(self):
        self.redis = redis.Redis(host='localhost', port=6379, db=0)
    
    async def store_artifact(self, task_id, agent_id, artifact):
        key = f"workflow:{task_id}:artifacts"
        await self.redis.hset(key, agent_id, json.dumps(artifact))
        await self.redis.expire(key, 3600)
    
    async def get_artifacts(self, task_id):
        key = f"workflow:{task_id}:artifacts"
        artifacts = await self.redis.hgetall(key)
        return {k.decode(): json.loads(v) for k, v in artifacts.items()}
    
    async def store_decision(self, task_id, decision):
        key = f"workflow:{task_id}:decisions"
        await self.redis.rpush(key, json.dumps(decision))

Error Handling and Recovery

When one agent fails, the system must recover gracefully:

async def run_with_fallback(task, primary_agent, fallback_agent):
    try:
        return await primary_agent.run(task)
    except AgentFailure as e:
        logger.warning(f"Primary agent failed: {e}. Switching to fallback.")
        return await fallback_agent.run(task)
    except MaxRetriesExceeded:
        return {"status": "needs_human", "task": task, "error": "Agent loop exhausted"}

Design every multi-agent system with the assumption that agents will fail. Graceful degradation—falling back to simpler agents or escalating to humans—is the mark of a production-ready system.

At SoniNow, we design and deploy multi-agent systems that coordinate specialized AI agents for complex business workflows. Our AI automation services cover architecture, implementation, and monitoring.

Multiple agents working together can tackle problems no single LLM can handle reliably. Contact us to design a multi-agent system for your complex workflow.