What is a Multi-Agent System?

The rise of agentic AI, where language models can interact with tools, possess memory, and reason through complex problems, is fundamentally changing how we build software. As these agents become more capable, a new paradigm is emerging: multi-agent systems. Instead of relying on a single, monolithic AI, developers are creating teams of specialized agents that collaborate to achieve goals far beyond the reach of any individual agent.

This article explores what multi-agent systems are, why they represent a powerful evolution in AI development, and how you can build them.

What is a Multi-Agent System?

A multi-agent system is a collection of autonomous AI agents that interact with each other and their environment to solve a common problem.

Think of it like a well-organized team of human experts. You wouldn't ask a single person to write a marketing report, translate it into three languages, and design the accompanying graphics. Instead, you'd assemble a team: a writer, a translator, and a graphic designer, all coordinated by a project manager.

In a multi-agent AI system, this structure is mirrored:

• Worker Agents are specialists. Each is given a narrow, well-defined task, a specific set of tools, and clear instructions. For example, one agent might specialize in searching a database, while another excels at writing user-friendly summaries.
• Supervisor (or Router) Agent acts as the project manager. It analyzes an incoming request, breaks it down into sub-tasks, and delegates each sub-task to the appropriate worker agent. It then synthesizes the results into a final, coherent response.

This "divide and conquer" strategy is the key to building more robust, capable, and reliable AI applications.

Why Use a Multi-Agent System?

If a single large language model (LLM) is powerful, why add the complexity of managing multiple agents? The need arises from the limitations observed when a single agent is given too many responsibilities.

The Problem of Tool Overload

When a single agent is equipped with a large number of tools (e.g., APIs for booking flights, checking weather, searching documents, etc.), its performance can degrade. With too many options, the agent may struggle to choose the correct tool for a given task. The LangGraph team, for example, has anecdotally observed that 5–10 tools is the sweet spot for a single agent. Beyond that, accuracy tends to drop. By creating specialized agents with only one or two tools each, you simplify the decision-making process and increase reliability.

The Challenge of Context Complexity

A single agent can also be confused by conflicting instructions. For instance, an agent might be told to "be brief and concise" but also to "be warm, empathetic, and professional." These goals can be at odds. A multi-agent approach resolves this by assigning different facets of a complex task to different agents, each with a simple, unambiguous set of instructions. A "Triage Agent" can be concise, while an "Escalation Agent" can be more empathetic and detailed.

By breaking down a complex workload into smaller, specialized tasks, multi-agent systems become a best practice for building sophisticated and scalable AI.

Types of Multi-Agent Architectures

Network Architecture

In this setup, any agent can hand off to any other agent. While this offers maximum flexibility, it often lacks control- agents may loop or branch unpredictably, leading to inefficiency, longer runtimes, and higher costs due to excessive LLM calls.

Supervisor Architecture

Here, a dedicated supervisor (or "router") agent orchestrates the workflow by deciding which agent to call and in what order. This allows sub-agents to focus on their specializations without needing to manage routing themselves.

Sub-agents as tools Architecture

A simplified version of the supervisor model: sub-agents are wrapped as "tools" within the supervisor.

• Pro: Easier to implement- commonly used in TypeScript frameworks like Mastra, VoltAgent, and the Vercel AI SDK.
• Con: Communication is limited. Agents only exchange tool call parameters, rather than sharing a richer state or context.

Custom Approaches

Many real-world implementations combine elements of these models. For example, a hybrid might use a supervisor structure for high-level routing but allow peer-to-peer agent handoffs in specific cases.

How to Build a Multi-Agent System

Building a multi-agent system typically follows the supervisor pattern, so let's start there. Let's walk through building a customer service system that handles general questions, refund requests, and technical support issues.

The Declarative, High-Level Approach

Modern AI frameworks make building multi-agent systems incredibly straightforward. They allow you to define a team of agents and a supervisor declaratively, letting the framework handle the complex routing logic behind the scenes.

First, you create your specialized "worker" agents. Each agent gets a name, a set of instructions defining its role, and an appropriate LLM.

typescript
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// Assume 'Agent' is provided by a high-level TypeScript framework
import { Agent, VercelAIProvider, openai } from 'some-ai-framework';

// Create specialized agents for different query types
const generalAgent = new Agent({
  name: 'General Support',
  instructions: 'You are an expert customer service agent handling general inquiries.',
  llm: new VercelAIProvider(),
  model: openai('gpt-4o-mini'),
});

const refundAgent = new Agent({
  name: 'Refund Specialist',
  instructions: 'You are a customer service agent specializing in refund requests. Follow company policy and collect necessary information.',
  llm: new VercelAIProvider(),
  model: openai('gpt-4o-mini'),
});

const technicalAgent = new Agent({
  name: 'Technical Support',
  instructions: 'You are a technical support specialist with deep product knowledge. Focus on clear step-by-step troubleshooting.',
  llm: new VercelAIProvider(),
  model: openai('gpt-4o'),
});

Next, you create the supervisor. Its instructions tell it how to delegate tasks to its sub-agents. The framework automatically makes the supervisor aware of the specialists and handles the delegation.

typescript
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// Create a supervisor agent that automatically routes to the appropriate specialist
const customerServiceAgent = new Agent({
  name: 'Customer Service Supervisor',
  instructions: `You are a customer service supervisor. Analyze customer queries and delegate to the appropriate specialist:
  - General queries → delegate to General Support
  - Refund requests → delegate to Refund Specialist
  - Technical issues → delegate to Technical Support`,
  llm: new VercelAIProvider(),
  model: openai('gpt-4o'),
  subAgents: [generalAgent, refundAgent, technicalAgent], // Simply list the workers
});

// A single function call handles everything
async function handleCustomerQuery(query: string) {
  const response = await customerServiceAgent.generateText(query);
  return response.text;
}

This approach is powerful because it leverages the LLM's natural language understanding to handle routing. You don't need to write explicit if/else logic; you simply describe the delegation rules in the supervisor's prompt.

The Manual, Low-Level Approach

Alternatively, you can build the entire system from the ground up using a low-level toolkit like the Vercel AI SDK. This approach gives you complete control but requires you to program the routing logic manually.

Instead of a supervisor agent, you would first create a classification step to determine the nature of the query.

typescript
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import { openai } from '@ai-sdk/openai';
import { generateObject, generateText } from 'ai';
import { z } from 'zod';

async function handleCustomerQuery(query: string) {
  const model = openai('gpt-4o');

  // Step 1: Explicitly classify the query type
  const { object: classification } = await generateObject({
    model,
    schema: z.object({
      type: z.enum(['general', 'refund', 'technical']),
      complexity: z.enum(['simple', 'complex']),
    }),
    prompt: `Classify this customer query: ${query}`,
  });

  // Step 2: Manually route based on the classification
  const { text: response } = await generateText({
    model:
      classification.complexity === 'simple'
        ? openai('gpt-4o-mini')
        : openai('gpt-4o'),
    system: { // Manually select the system prompt
      general:
        'You are an expert customer service agent handling general inquiries.',
      refund:
        'You are a customer service agent specializing in refund requests.',
      technical:
        'You are a technical support specialist.',
    }[classification.type],
    prompt: query,
  });

  return { response, classification };
}

While more verbose, this method offers maximum flexibility. You have fine-grained control over every step of the process, from classification to model selection and prompt engineering.

Conclusion

Multi-agent systems represent a significant leap forward, enabling us to build more sophisticated, reliable, and specialized AI applications. By dividing complex problems among a team of collaborating agents, we can overcome the limitations of a single LLM and unlock new possibilities.

When building your own system, the key decision is choosing the right level of abstraction:

• High-Level Frameworks are best for rapid development. They offer declarative APIs that let you define what you want your agent team to do, while the framework handles how it gets done.
• Low-Level SDKs are ideal for custom implementations. They provide the fundamental building blocks, giving you full control to design bespoke orchestration logic from scratch.

Whichever path you choose, the principle remains the same: a team of specialists will almost always outperform a single generalist. As AI continues to evolve, this collaborative, multi-agent approach will become the standard for tackling the world's most challenging problems.

Build Multi-Agent Systems with Inkeep

Ready to build your own multi-agent system? Inkeep's multi-agent framework makes it easy to orchestrate specialized AI agents for customer support, developer tools, and enterprise workflows.

Whether you're building a customer service team, a developer copilot, or an enterprise automation system, Inkeep gives you the tools to deploy sophisticated multi-agent systems in days, not months.

Get started with Inkeep →