What is a multi-agent system?

Multi-agent systems take single AI agents to the next level, combining them in a cooperative environment to intelligently carry out repetitive workflows and tasks. Those developing multi-agent systems need to understand the environment, the communication methods among agents, and available frameworks to build an efficient, goal-oriented system.

What is a multi-agent system?

A multi-agent system, or MAS, is made up of multiple artificial intelligence (AI) agents working together to perform tasks. These systems can help automate complex systems and solve complex problems. Within a multi-agent system, each AI agent has been trained on a specialized function and is able to communicate with the other agents within the system. A multi-agent system can contain up to thousands of separate agents.

If a single AI agent can automate a single task or workflow for a human worker, then multi-agent systems can serve as a programmable workforce that automates larger processes or repetitive tasks, such as running the robots in a warehouse, self-driving a car, or running an IT service desk ticketing system. Agents act as orchestrators for LLMs, providing the planning, memory, and tool usage that moves an LLM from an individual task to goal-oriented decision making. Agents in a multi-agent system then also depend on APIs to understand what’s happening, then make a decision and choose an action. Working together, agents can communicate and take individual action toward solving a larger problem.

How a multi-agent system works

Multi-agent systems solve complex problems by setting up agents to communicate with each other and ensuring that problem solving is distributed among agents. Agent interactions are essentially multi-agent reinforcement learning, where sensors or actions provide information immediately to an agent. Or, an agent can share information it has learned through experience, such as ingesting a new policy or observing an action. It can share information in real time to make sure other agents aren’t learning identical information and muddying its purpose.

Setting up multi-agent systems requires an understanding of how AI agents work and a desired outcome or problem to address. Broadly, there are a few areas to understand about multi-agent systems. 

The AI agents

Within a multi-agent system, agents will collect data based on their surroundings, such as identifying direct signals or noticing changes. Agents have some autonomy to make choices based on the information they access. The reasoning behind these agents is powered by a large language model (LLM) that is capable of first understanding intent, then choosing which action to take to meet the stated goal.

Agents can be drones, sensors, robots, bots, software programs, and more. Each agent plays a specific role in the multi-agent system. As with any AI agent, access to high-quality, up-to-date data is absolutely essential for success. 

The framework

There are several framework options available to developers creating and managing a multi-agent system, including:

• JADE (Java Agent Development Framework)
• Mesa (Python)
• Ray (Python)
• AutoGen (Microsoft)
• CrewAI
• LangGraph
• Langchain
• LlamaIndex

The choice depends on the development experience of the team, how the multi-agent system will need to integrate into the bigger company infrastructure, and the complexity and scope of the tasks being executed by the AI agents.

Challenges of Scale: Cost & Logic

As systems grow to include dozens or even hundreds of agents, two primary challenges emerge: token cost and infinite loops.

Token Optimization

Because agents “chatter” back and forth, they can consume a high volume of tokens. Developers manage this through “summarization handoffs”—where agents pass concise summaries instead of raw data—and “model tiering,” using smaller, faster models for routing tasks. Research from AWS and recent studies on agentic workflows show that these techniques, including semantic caching, can reduce API costs by 40% to 60%.

Preventing Logic Loops

Without strict boundaries, agents can get stuck in a “clarification cycle.” To prevent this, developers implement Max-Turn Guardrails (a hard limit on interactions) and State Machine logic. This ensures that if a problem isn’t solved within a set number of steps, the system automatically triggers a “break-glass” procedure to involve a human technician.

Communication in a multi-agent system

The crux of a multi-agent system is how agents communicate with each other within their environment — the shared space where they work and interact. Developers have to understand distributed AI and options for communication rules between agents when creating a multi-agent system. Agents can pass messages to each other, share information, and modify the environment, which other agents observe and learn from. Beyond simply sharing the information, agents can coordinate, negotiate, and even compete, opening up a lot of potential for what these systems can accomplish. 

Orchestration is a key concept in multi-agent systems: this principle refers to a complex task being broken down into an agentic workflow. It might look like a flow chart or plan with assigned roles and responsibilities. An orchestrator ensures that agents work in sequence and that information flows between them to meet the goal or desired output. Agents might also work in parallel, such as for AI IT ticketing systems. 

Developers creating a multi-agent system will choose the communication protocols by which agents exchange information. That includes message formatting, like JSON or XML, and how the messages are sent, like HTTP or MQTT. Agent communication languages, like FIPA ACL and KQML, standardize agent interactions. MAS developers also put coordination mechanisms into place to address disagreements between agents, make sure agents align on goals and generally work effectively together. These mechanisms might use bidding techniques for tasks, like the contract net protocol (CNP), or a voting system for decision making.

Why to use a multi-agent system

As with individual AI agents, multi-agent systems can remove many of the burdens of manual or repetitive work that’s done by human workforces now. Multi-agent systems work well even in dynamic environments that change constantly, because they’re distributed systems with autonomous agents. 

Modern developers and IT teams choosing to build or buy multi-agent systems are typically looking for these common benefits:

Reliability

Multi-agent systems can withstand a failure of one agent. Other agents take over, so the system remains dependable even in high-pressure or business-critical situations. And for situations with continually changing variables, like smart buildings or in transportation, agents can use machine learning to predict demand and allocate resources accordingly.

Speed and scale

A team of AI agents working together can often solve problems very quickly, as agents work on different parts of the problem concurrently. Team learning among the agents means they share and gather information and improve methods as they go, so AI systems continually improve. And because multi-agent systems can use new information or challenges to change how they work, they don’t require constant human assistance.

Performance

Multi-agent systems can synthesize information very quickly, since each agent can take in knowledge and feedback from the other agents. Agents specializing in certain areas can help fill any information gaps. This parallel work and distributed setup generally leads to higher performance than single-agent systems.

Single agent vs. multi-agent systems

As we’ve quickly moved through generative AI’s maturity curve over the past few years, AI agents have become the next wave of possibility. An agent can use tools and learn from interactions, so they can help automate a typical manual task or serve as a customer service chatbot, with a single role and defined output.

In a single-agent system, AI agents don’t communicate with other agents. Each AI agent operates in isolation and bases its decisions on rules and strategies that were either preset or learned. AI agents operating alone can be useful for use cases like customer service chatbots, fraud detection monitoring, or analyzing large datasets and providing specified insights or summaries. These agents are typically easier to develop and don’t require a lot of computational overhead. But they are also easily overwhelmed by more complex or dynamic tasks or those that need particular expertise. 

But taken as a group, AI agents working in multi-agent systems draw from the collective of resources, automation, and optimization, so each agent can learn on its own, then share with the group within a shared environment for even more efficiency. There’s still predefined or desired output in multi-agent systems, but it’s achieved by multiple agents in coordination. Each agent is responsible for solving a particular part of the problem and communicates with others to achieve that goal. 

Multi-agent systems are complex to design, though they are also available as platforms to buy to meet different specialized needs, whether that’s IT ticketing, patient care in a hospital, or factory floor automation. 

Use cases for multi-agent systems

Multi-agent systems are already in use across industries. Here are some of the primary use cases.

Transportation

Multi-agent systems are a good fit for accurately and safely running train networks, shipping operations, or in trucking. These systems can share live traffic and route details for traffic to move smoothly, or for boats visiting the same ports. The combination of collaboration, planning, and real-time, updated data access is ideal for multi-agent systems. Self-driving cars are another example: one car is in itself a multi-agent system, while multiple self-driving cars are also a multi-agent system, sharing information with each other and the infrastructure to drive safely. 

IT service desk automation

Multi-agent systems bring together specialized agents under one umbrella to entirely automate the IT ticketing process and help solve user problems without taking up valuable technician time. In the case of Atera’s IT Autopilot and AI Copilot, for example, one agent researches existing knowledge base information, one checks the most common technician issues, and another delivers the correct response to the user seeking help.  

Sensors, robotics, and autonomous systems

Multi-agent systems are well-suited for circumstances where there are many distributed, intelligent nodes or other individual but connected instances. Robots packing customer orders in a warehouse might be run by a multi-agent system to fulfill orders correctly, in the right order, and without robots interfering with each other.

Sensor applications for multi-agent systems also include environmental monitoring, smart building systems, retail store inventory, or smart traffic management. Agents in smart building IoT networks, for example, can monitor occupancy, temperature, and humidity to then collaborate and adjust any of those elements along with turning off lights or other efficiency actions. Or, in healthcare, sensors can track vital signs and environmental data to track patient health and alert staff to any changes.

Supply chain management

Supply chains are notoriously complex and interdependent, encompassing manufacturing, freight, and customer sales, plus many more steps and variables depending on the industry. Multi-agent systems can connect all the different components, with agents able to make real-time adjustments, along with negotiating and collaborating on inventory and other resource management. 

Building the future of IT with multi-agent systems

IT automation is an area ripe for innovation. Multi-agent systems like Atera’s IT Autopilot and AI Copilot actually embed agentic AI into IT support workflows. Multiple agents work together to quickly address and resolve IT user tickets, with each agent tackling a separate piece of the problem for fast, efficient resolution.
The end result of this multi-agent system is to free up time and resources so that technicians can tackle the more complex or underlying issues. Atera’s AI-driven tools can automatically resolve tickets, manage endpoints, and cut response times dramatically. These outcomes all help to streamline workflows, reduce manual workloads, and ultimately offer users a better, faster resolution. As IT environments keep growing in complexity, maturing AI technologies can tackle many technical support problems.