
When large language models (LLMs) like GPT-3 first appeared, they made a big impact. They could generate text, answer questions, and perform various tasks with impressive accuracy. But as the tasks we face in AI get more complex, relying on a single model for everything has its limits.
Think about trying to solve a problem that requires expertise across different fields or steps. A single LLM might handle part of it, but it’s not designed to break down a complex task into manageable pieces. What happens when a task needs several different skills to complete?
This is where multi-agent systems come in. Instead of one model trying to handle everything, these systems use multiple agents, each designed for a specific part of the task. Agents can work together, focusing on their areas of strength, to tackle the problem more effectively.
Why does this matter? As tasks grow more complicated, breaking them down and having specialized agents handle each part creates more flexibility. It allows us to solve problems that a single LLM couldn’t tackle alone.
In this article, you will learn how multi-agent systems operate, how agents collaborate, and why this approach is gaining popularity in AI. We’ll also look at real-world applications and the frameworks behind these systems.
Understanding Multi-Agent systems
As AI continues to evolve, solving increasingly complex problems requires more than just powerful models; it demands systems that can divide and conquer tasks efficiently. Multi-agent systems provide a solution by distributing the workload across several specialized agents, each designed to handle specific aspects of the problem. These systems enable better scalability and flexibility, allowing for more effective problem-solving when a single model struggles to manage the entire process. By coordinating the efforts of multiple agents, these architectures can address challenges beyond one LLM’s capacity, offering a more robust approach to complex AI tasks.
A Large Language Model (LLM) agent is a framework that manages application control flow. As these systems evolve, they can become increasingly complex, which may lead to challenges in management and scalability. Some potential issues include:
- Tool overload – when agents have excessive tool access, leading to suboptimal tool selection decisions
- Context overextension – when the scope becomes too extensive for single-agent handling
- Role complexity – when the system requires various specialized functions
One solution to these issues is to decompose your application into multiple smaller, autonomous agents working together in a multi-agent system. These individual agents can be as basic as a simple prompt with an LLM response, or as sophisticated as ReAct agents.
The primary benefits of adopting a multi-agent architecture include:
- Modular Design: Separate agents enable simpler development, testing, and system maintenance
- Expert Focus: You can create specialized agents for specific domains, improving system efficiency
- Enhanced Oversight: Explicit management of agent interactions becomes possible, moving beyond tool calls
To illustrate the practical value of LLM agents, consider this basic query:
“What’s the average PM2.5 level in Warsaw for 2024?”
This question could be answered by an LLM with built-in knowledge, or through a basic RAG system where the LLM can access environmental data and air quality reports.
Now let’s give the system a more complex question like the following:
How has the trend in air quality (particularly PM2.5 and PM10 levels) changed over the last decade in major Polish cities, and what impact might this have on respiratory health conditions? Additionally, can you provide a graphical representation of the correlation between smog levels and respiratory hospital admissions during winter months?
Such a complex query exceeds the capabilities of both a standalone LLM and a basic RAG system. This scenario requires an LLM to decompose the task into manageable components, utilizing various tools and executing a sequence of operations to generate a comprehensive response. A viable approach would be to develop an LLM agent with access to:
- Polish air quality monitoring APIs
- Public health statistics
- Weather data archives
- Environmental research publications focusing on Central European air quality
Furthermore, the LLM would require access to a “code interpreter” tool to process the collected data and generate visualizations showing relationships between pollution levels and health impacts across Polish regions.
Note: This section is based on insights from LLM Agents | Prompt Engineering Guide (paraphrased)
How Do AI Agents Work?
AI agents work by following a structured process known as control flow. Control flow refers to the sequence of steps an agent takes to complete a task, ensuring the right actions are taken in the correct order and enabling coordination with other agents when needed. The control flow helps manage the decision-making process, tool usage, and collaboration among multiple agents. Here’s a breakdown of how it works:
Reflection: The LLM analyzes its output and performance to identify areas for improvement. This self-assessment helps refine the agent’s approach and outcomes over time.
Tool Use: The LLM has access to a variety of external services, such as internet search, code execution environments, and other utilities. These tools allow the agent to collect information, perform actions, and analyze data to make more informed decisions.
Planning: The LLM creates step-by-step strategies to achieve its goals. For example, it may first organize its thoughts, then gather relevant data online, and finally produce a document or report based on the gathered insights.
Multi-agent Collaboration: When multiple AI agents collaborate, they divide tasks and share insights with each other. This teamwork ensures a more comprehensive solution, as agents can focus on specific areas while working together to reach a better outcome than a single agent could alone.
Want to know more about LLM Agents? In our previous article, we talked about “Applications of LLM Agents in Various Industries.” If you want to dive deeper into the real-world use cases of LLM agents, click here: Link
Multi-agent system design patterns
A multi-agent framework comprises several interconnected agents collaborating to tackle challenges that would typically overwhelm a standalone agent. These systems leverage the strengths of individual agents through collaboration, coordination, task handoff and sometimes competition.
There are several ways to connect agents in a multi-agent system:
- Network Architecture: Each agent can call any other agent. All agents can communicate with each other directly, allowing for flexible decision-making and coordination.
- Supervisor Architecture: Each agent communicates with a single supervisor agent that makes decisions about which agent should execute a task next.
- Hierarchical Architecture: This is actually a generalisation of a supervisor architecture. In this model, there is a tree-like structure where agents are organised into nested levels. A supervisor agent oversees other agents, which can themselves supervise lower-level agents. This architecture allows for complex control flows and is beneficial for large systems.
- Custom Architecture: Each agent interacts with only a specific subset of agents. Certain aspects of the flow are deterministic, and only a select few agents have the authority to determine to which other agents route next.
- Human-in-the-loop Architecture: AI Agents can interact with other systems with tool calls. Some interactions could be dangerous if the agent makes a mistake. Human-in-the-loop architecture ensures that for those sensitive actions (e.g. writing to database, deleting records in database, booking a ticket for a flight or hotel, payments etc.) flow is being passed to the human for review and accept or deny. This can be either user directly or some company worker/expert.

Source: Multi-agent Systems / Source: Build a Customer Support Bot
Customer service as a use case of Multi-Agent Systems
One of the most popular use cases of Multi-agent systems is customer support. In contrast to conventional chatbots bound by predetermined scripts and processes, LLM-driven AI agents possess the capability to comprehend subtle variations in customer inquiries and engage in flexible dialogue with natural intelligence. These agents can discern the underlying meaning regardless of question formulation, generating responsive, context-aware answers that create a more individualized and fluid interaction experience.
Why AI agents for customer service?
- Availability: AI agents are available 24/7 and are ready to assist customers, without the need for breaks or human intervention (except human-in-the-loop workflow).
- Multilinguality: AI agents can talk to customers in the language they prefer, making it perfect for global businesses.
- Scalability: AI agents handle more queries, allowing businesses to extend support even during the highest volumes.
- Context-awareness: In addition, AI agents can use external data to ground it’s answers in facts
- Personalisation: AI agents can also use specific customer data—such as purchase history, preferences, and behaviour—to deliver personalised responses and recommendations.
- Domain expertise: Handling different domain queries by routing to specialised AI agents
- Integration with external services: AI agents can communicate with external services like web search engines, calendars, emails or databases via API calls, which gives the system ability to autonomously write or read data to specified services.
Efficient task management in multi-agent systems through supervisor-agent architectures

A common pattern in multi-agent systems (MAS) for customer service is the supervisor or hierarchical architecture. Companies often operate across various domains, such as finance, IT, and retail, each requiring specialised handling of customer inquiries and issues.
In a supervisor architecture, there is typically a supervisory agent that oversees and plans the interactions among multiple subordinate specialised agents. This structure allows for efficient management of tasks by handling simpler queries by supervisor agent while escalating more complex issues to specialised agents. For example:
Supervisor Agent:
- Handle simple customer inquiries,
- Route to specialised agents if needed
- Asks follow up questions to understand user issue before taking any action
- Can have access to helper tools like for example calendar integration to efficiently handle date operations, web search for looking up additional information or RAG-as-a-tool for additional data like for example FAQ.
Specialised Agents:
- Address specific domains or complex issues, such as checking balance in finance or technical support in IT.
- Each specialised agent has its own set of tools. It can be for example RAG-as-a-tool (Retrieval Augmented Generation), database query, hotel reservation, flight ticket buying, arbitrary API calls etc.
- Additionally, human-in-the-loop workflow can be used to minimise the risk of agents taking any sensitive actions by mistake (paying, booking flight ticket or hotel reservation). Actions that look up the data from different sources do not need user consent.
This tiered approach ensures that customers receive timely responses while allowing specialised agents to focus on intricate problems that require deeper expertise.
Popular Frameworks for Building Multi-Agent Systems
Various frameworks have been developed to support the creation of these systems, each offering distinct features tailored to different needs. Below is an overview of some of the most popular frameworks for building multi-agent systems:
1. LangGraph
LangGraph is a specialized framework designed for creating stateful, multi-agent applications that utilize large language models (LLMs). It provides detailed control over workflows and agent states, facilitating the development of sophisticated AI systems that incorporate features such as cycles, persistence, and human-in-the-loop interactions.
2. Microsoft AutoGen
Microsoft AutoGen is an open-source framework for multi-agent conversations aimed at streamlining the creation of next-generation LLM applications. It offers a high-level abstraction for developing workflows where multiple agents can collaborate, learn, and adapt over time.
3. Semantic Kernel
Semantic Kernel is a lightweight, open-source toolkit that allows developers to incorporate AI models into their existing applications built with C#, Python, or Java. Serving as middleware, Semantic Kernel automates business processes and enables developers to create AI agents that interact effectively with their codebase.
4. CrewAI
CrewAI is a multi-agent platform that enhances workflows across various industries by utilizing AI agents for automation. It enables users to design and implement automated workflows using any large language model (LLM) and cloud service.
Source: AI Agent Frameworks-Components & Top 5 Open Source Solutions
How do you tackle complex problems with Multi-Agent Systems?
As tasks become more complex, relying on a single agent can limit what you can achieve. Multi-agent systems break down these tasks, using multiple agents that specialize in different areas. This approach allows agents to work together and solve problems more effectively.
Our team can help you build and implement Multi-Agent systems for managing and coordinating multiple AI models, optimizing task distribution, and enabling seamless collaboration between different models in complex applications.
Let’s talk about how multi-agent systems can help you solve your toughest challenges. We’re here to help you create the solution that works best for you. Contact us to schedule a call.
Have More Questions? Let’s Connect!
If you have additional questions or are ready to explore how Multi-Agent Systems can transform your project, feel free to reach out. Our team is here to provide the insights and solutions you need to leverage the power of interconnected agents for enhanced efficiency, collaboration, and decision-making.