Multi Agent Systems: How do they work?

Imagine you’ve a team of experts—each with deep domain knowledge, capable of making decisions independently, and constantly learning. They can all collaborate, adapt to changes in real time, and execute your business strategy faster than any traditional workflow.

Now, imagine every individual expert on your team is an AI agent. That’s a multi agent system for you. This is an oversimplification, really, but you get the gist. 

You've got several AI agents instead of just one doing everything. Each one has specific skills, specific jobs, but they're all working on a common goal.

For leaders like yourself in enterprises, dealing with operations that are all tangled up and always changing, these multi agent systems (MAS) become essential.

In this article, let’s try and understand what makes these systems tick. How they work, what the upside is, the common ways they're structured, and what you need to think about if you're considering using them.

We want to try to give you a clear picture without drowning you in tech speak.

Let’s go!

Introduction to multi agent systems

Multi agent systems are networks of autonomous entities, known as agents, that interact and work together to achieve individual and shared goals. These systems have gained significant attention in recent years due to their ability to solve complex problems that are difficult for single-agent systems to tackle.

In a multi agent system, multiple agents collaborate and coordinate their actions to achieve a common objective, making them particularly useful in applications such as supply chain management, transportation systems, and healthcare.

Unlike single-agent systems, where one agent is responsible for all tasks, multi agent systems distribute tasks among multiple agents. This distribution allows for greater flexibility and efficiency, as each agent can specialize in specific tasks and share information with other agents. By leveraging different agents and distributed problem solving, these systems enhance decision-making and task management.

By leveraging the strengths of multiple agents, these systems can handle dynamic and complex environments more effectively.

What is an Agentic System? 

Before getting into why multi-agent systems are becoming a bigger deal, let’s first nail down what an agentic system is.

An AI Agent can be defined as an autonomous system that can observe its environment, reason about its goals, and take actions using external tools to achieve a desired outcome. - Google

In simple terms, it’s just an LLM that can use tools. A language model in itself can’t do much, but can enable tools to do a specific task. Think of it like an agent that can go fetch data or browse the web, simple enough.

But on a deeper level, it’s about how the system uses the model to decide the workflow and how it controls the application steps. Large language models (LLMs) enhance these systems by enabling nuanced communication and reasoning capabilities. Agents work collaboratively within these LLMs, leveraging their individual strengths to tackle complex tasks effectively.

This is what a single agent looks like. These systems really shine when it comes to specialized tasks, like automating software testing or analyzing massive amounts of data.

‍

You can think of a single agent as a highly skilled expert who can work independently within their area of expertise, learning from experience, and getting better over time.

So, what are multi agent systems?

A single agent who can use tools and take independent decisions is great, but problems arise as soon as your operations increase.

As you scale, you might consider giving this agent multiple tools. However, once you do this, it starts making bad calls about which tool to use when. It’s because as you develop these systems, they get more complex. It’s hard to manage and scale this way, especially when the agent has too many tools at its disposal, leading to suboptimal choices.

And then there’s the context issue. The agent can only remember so much conversation and tool use history, which just tanks its performance. This is particularly challenging in a single thread workflow, where a single agent must manage all tasks and tools, often resulting in ineffective tool usage and limitations in achieving complex responses.

Additionally, a lot of complex jobs need different types of expertise. It often just makes more sense to have specialized agents instead of trying to build one giant super brain.

And that’s where multi agent systems come in. These systems leverage various components, such as agents, tools, and processes, to tackle complex tasks effectively.

They are better suited for handling complex operations compared to single-agent systems, particularly in adapting to dynamic interactions within the system.

Single agent vs multi agent

A single agent might be great at one thing, one analysis, but what happens when that analysis needs to kick off actions elsewhere? That’s where the solo approach kind of hits a wall.

Multiple autonomous agents in a multi-agent system collaborate to enhance industrial automation by optimizing manufacturing and logistics processes through decentralized AI agents that communicate and coordinate in real-time. These systems utilize specialized agents based on their roles and capabilities to handle different subtasks, ensuring efficient collaboration and overall task completion. Multi agent interactions are crucial in this context, as they involve handoffs where one agent transfers control to another, managing control flow and state updates among agents in complex systems.

Let’s explore some key differences between a single and a multi-agent system.

Key Capabilities and Benefits  

AI companies, like OpenAI and others, are finding that the current large language model approaches have limitations. As they continue to scale, they’re seeing diminishing returns in terms of intelligence and capability gains.

But that doesn’t necessarily mean AI progress has diminished. Instead, leading AI labs are looking into new training techniques and architectural innovations. Artificial intelligence plays a vital role in these advancements, particularly in the development of multi-agent reinforcement learning systems.

These new techniques have shown promising results in boosting model performance on complex reasoning tasks. Thus, helping with the development of AI agents—single or multi-agent systems.

Some of the key benefits of multi-agent systems

1. Modularity

Breaking the tasks down for separate agents makes everything—development, testing updates—way easier. You can fix or improve one piece without breaking the whole thing. That’s a massive difference from older monolithic systems.

Additionally, using a single state schema for agent nodes simplifies data management, though distinct state schemas can be crucial for optimizing performance in specialized agents like search agents.

2. Huge scalability

The move toward modular and multi-agent systems enables scalability. You can add more agents as you scale your operations. No need to rebuild everything. This helps with faster decisions, as these agents interact in real time, thus cutting down delays. 

3. Contextual awareness 

Agents working together are great at collaboration, negotiation, and sharing tasks. They are incredibly good at better understanding nuanced human language, context, and intent. Thus, they take context-aware decisions using live data. Also, they’re learning continuously by updating themselves regularly. 

4. Enhanced reasoning and adaptability

New approaches, like post-training optimization and runtime improvements, allow AI agents to reason more effectively. Agents can handle multi-step tasks, make complex decisions, and adapt to unpredictable real-world scenarios, which are essential capabilities for autonomous agents in business and industry

5. Resilience

If one agent fails, the whole thing doesn't necessarily crash. It really does mirror a good human team, like we discussed at the beginning. 

6. Specialization

You can have an agent that's great at research, another one for planning, maybe one just for writing code. And often, just plain better performance than a single agent because they share resources and learn together. Plus, and this is crucial, you get more control over how they interact.

7. Improved efficiency and resource utilization

Resource constraints like data, compute power, and energy are becoming a big deal. So, there’s a shift towards training and efficient architectural methods. 

This helps with developing AI agents that are powerful and sustainable. And that makes it a lot more practical to deploy them for all kinds of applications. 

Multi-agent systems architecture

Here’s an overview of the most common architectures used in MAS:

Multi-agent network 

Agents can communicate with each other freely, and any agent can determine which other agent to call next. This setup leverages decentralized control, allowing independent agents to operate autonomously while achieving collective objectives. This is particularly beneficial for problems that do not have a clear hierarchy or sequence in which agents should operate.

Advantages:

• High flexibility and adaptability.
• Agents can dynamically choose which other agents to interact with based on the current context.

Use Cases:

• Ideal for collaborative environments where agents need to share information and make decisions based on collective input, such as in swarm robotics or distributed sensor networks.

Multi-agent supervisor 

The supervisor agent decides which agent to call next and communicates with each agent individually. This architecture includes a supervisor agent that manages the interactions among subordinate agents.

Advantages:

• Centralized control over agent interactions simplifies coordination.
• Effective for managing complex workflows where tasks need to be distributed among various agents.

Use Cases: 

• Useful in scenarios like customer service systems, where a supervisor agent can route inquiries to the appropriate specialized agents.

Multi-Agent Supervisor (tool-calling) architecture

A supervisor agent can employ a tool-calling LLM to determine which agent tools to call and the arguments to pass to those agents. This is a specific example of a supervisor architecture where individual agents are represented as tools.

Advantages:

• Can adapt to changing requirements by easily adding or removing agents (tools) as needed.
• It can optimize the selection of agents based on their capabilities and the specific needs of the task, leading to more efficient execution of complex processes.

Use Cases:

• A supervisor agent in healthcare could oversee multiple agents that manage different aspects of patient care, including scheduling, medical records, and billing.

Hierarchical architecture

In this multi agent system, you can have a supervisor overseeing other supervisors. This is an expansion of the supervisor architecture, enabling more intricate control flows. Agents are structured in a hierarchical system, where higher-tier agents supervise or assign tasks to lower-tier agents to achieve coordination.

Advantages:

• Clear organization of responsibilities and tasks.
• Facilitates complex decision-making processes by breaking down tasks into manageable units.

Use Cases:

• Suitable for large-scale systems such as smart cities, where different layers of agents manage various aspects like traffic control, energy distribution, and public safety.

Custom multi-agent workflow

In this architecture, agents communicate with a limited number of other agents in the system. Some parts of the workflow are predetermined, and only certain agents can decide which agents to communicate with next.

Advantages:

• Predictable behavior and easier debugging.
• Suitable for applications where the sequence of operations is critical.

Use Cases: 

• Effective in environments where tasks must be completed in a specific order, such as in manufacturing processes or software development pipelines.

What type of architecture is most suitable for multi-agent systems?

The specific requirements of the application, including the complexity of tasks, the need for coordination, and the desired level of flexibility, heavily influence the choice of architecture for a multi-agent system. 

Each architecture presents unique advantages that can be utilized to optimize performance and effectively achieve the system's goals. For example, Crewai is best suited for researchers, whereas langgraph is best for production

How do multiagent systems work?

Fundamentally, multi-agent systems function as digital teams, with each agent observing, reasoning, making decisions, and acting within its own area while maintaining coordination with the overall objective. 

The basic operational cycle is as follows:

1. Perception

Each agent begins by gathering information from its environment using available tools. This could be:

• A logistics agent pulling delivery data from a tracking API
• A finance agent scanning real-time expense reports
• A customer support agent monitoring sentiment in incoming tickets

In a complex system involving several agents, each specialized agent relies on its available tools to effectively reason, plan, and execute subtasks. Think of this as the agent’s “senses.” It reads data feeds, listens to system events, or even queries databases — just like a human would read dashboards or reports.

2. Interpretation

Once the data is in, agents interpret it through the lens of their specific goals and rules:

• Is inventory running low?
• Is this expense unusual compared to last month?
• Is the customer’s tone escalating?

This step is where agents apply logic, thresholds, or even machine learning to understand context.

3. Decision-Making

Now it’s time to decide: what should be done?

Some agents might use AI models to make complex decisions, such as predicting supply chain delays or detecting fraud patterns. Other agents may rely on simpler rules, such as "if over budget, alert finance"

4. Coordination

Here’s where MAS gets powerful. Agents don’t work in isolation — they talk to each other.

• A procurement agent might check with a budget agent before placing an order.
• A compliance agent might flag an action for legal review by another agent.

They coordinate, negotiate, and update one another, ensuring their actions align with enterprise-wide goals.

5. Action

Finally, agents take action. This could be:

• Triggering a payment
• Updating a CRM
• Escalating an issue to a human
• Re-prioritizing a task queue

This closes the loop. But it’s not a one-off — the system continuously cycles through this process, reacting to new data and refining behavior over time.

Key components of multi agent systems

The key components of multi agent systems include agents, environment, interactions, and communication. Let’s look at them one by one. 

Agents 

The system is composed of autonomous entities called agents, each with its own goals and capabilities. The environment encompasses the external context and surroundings, supplying the necessary data and conditions for the agents to operate and interact.

Environment 

The environment is simply the context where the agent operates. This includes the actual physical space, the online space where the agent works, like databases, websites, etc., and the specific situation the agent is dealing with. If a user asks about the weather, the context includes weather data and the user's location.

Simply put, the environment facilitates the agents' interactions.

Interactions 

Interactions involve the exchange of information and coordination of actions between agents. This is where agents collaborate, negotiate, and make joint decisions to achieve their objectives. 

Communication

Communication is the mechanism by which agents share information and negotiate, ensuring that all agents are aligned and working towards the common goal. Effective communication is crucial for the success of multi agent systems, as it enables agents to coordinate their actions and respond to environmental changes.

Effective coordination ensures the system remains efficient and scalable as the number of agents grows. 

Multi agent systems applications

A team approach lets multi agent systems handle some really challenging situations. For instance, when you need a forecast that automatically triggers logistics changes. Or finance needs real-time data from operations for reconciliation.

That's where MAS really shines, in those interconnected tasks. Let’s look at some applications of multi agent systems. 

Supply chain management

Autonomou supply chains

Multi-agent systems can manage complex supply chains by coordinating agents that represent suppliers, manufacturers, and distributors. Distributed systems play a crucial role in this context, providing the necessary infrastructure for these agents to operate efficiently. For example, in supply chain management, distributed systems enable real-time data sharing and decision-making across different nodes, enhancing overall performance.

These agents interact dynamically to negotiate prices, manage inventory, and optimize logistics routes, which leads to improved responsiveness to market changes and customer demands.

Demand forecasting

MAS can analyze historical sales data and market trends through specialized agents that focus on different aspects of demand forecasting.

By synthesizing insights from various data sources, these systems improve the accuracy of inventory management and reduce stockouts or overstock situations.

Finance

Algorithmic trading

Many financial institutions are using multi-agent systems for algorithmic trading to analyze market data, execute trades, and adjust strategies in response to changing market conditions. 

They use a multi-agent framework to process massive amounts of data to enhance the speed and accuracy of trading recommendations.

Fraud detection

Financial transactions can be monitored for fraudulent activity using multi-agent systems, which employ specialized agents to analyze data from multiple sources and identify suspicious patterns or behaviors. 

Compared to traditional single-system methods, this multi-agent approach can detect potential fraud more quickly.

E-commerce

Personalized shopping experiences

E-commerce platforms leverage multi-agent systems to provide personalized product recommendations that enhance customer experience and increase conversion rates. 

By analyzing customer behavior, preferences, and purchase history, these agents can suggest products tailored to each individual customer, leading to higher customer satisfaction.

Automated customer support

In e-commerce, multi agent systems are leveraged to enhance customer support. By employing various agents specializing in specific areas such as order status, product information, and returns, these systems enable efficient and prompt responses to customer inquiries, thereby elevating the overall quality of service.

Healthcare

Multi-agent systems have a wide range of potential applications in healthcare. For instance, they can help in disease prediction and prevention by employing genetic analysis. This has valuable implications for medical research, particularly in areas like cancer.

Also, multi-agent systems can simulate and prevent the spread of epidemics. By using advanced neural networks and machine learning techniques to analyze extensive datasets, these systems can offer crucial insights for public health policy.

Multi agent systems challenges

Agent malfunctions are one. If your agents are all based on the same foundation models, a weakness in the model could cause widespread problems. So diversity in models is important. Additionally, good data governance, rigorous testing are crucial as well.

Another challenge is just the complexity of coordinating them, getting agents to cooperate and negotiate effectively. That’s not trivial to design. Multi-agent systems often utilize reinforcement learning to enhance decision-making and distinct problem-solving methods through collaboration, making them suitable for intricate applications across various fields.

And finally, especially in those decentralized systems, you can get unpredictable behavior. Agents might do conflicting things, making it harder to track down and fix problems.

Wrapping up…

Multi agent systems are a pretty big step forward for AI capabilities. They’re a powerful way to tackle complexity by using collaboration, specialization, and adaptability, just like effective human teams do. 

So, the takeaway for you is to start thinking. 

• How could these ideas apply in your world? 
• What challenges or opportunities are you seeing?
• What specific workflow, what tasks that you deal with, could be truly transformed by a well-designed AI team? 

As AI keeps moving towards being more understanding multi agent systems, it's going to be increasingly important for anyone involved in intelligent automation. 

And, Zams is helping enterprises lead the shift. Schedule a demo with us today to know how Zams can help you build multi agent systems that are robust, explainable, and future-proof.