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, through multi agent interactions.
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 managing dynamic environments.
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 a single agent system 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 a single agent system, where one agent is responsible for all tasks, multi agent systems distribute tasks among different 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 distributed problem solving and strong agent communication, these systems enhance decision-making and task management.
By leveraging the strengths of multiple agents, these systems can handle dynamic environments and complex workflows 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 agent based 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 problems and multi step tasks effectively.
This is what a single agent looks like. These systems really shine when it comes to specialized agents handling tasks like automating software testing or analyzing massive amounts of data.
You can think of a single agent system 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 system that 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 unexpected behaviors.
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 problems need different agents with 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 workflows compared to a single agent system, particularly in adapting to dynamic environments and multi agent interactions within the system.
Single agent vs multi agent
A single agent system 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 supply chain processes through decentralized agent systems that communicate and coordinate in real-time. These systems utilize specialized agents based on their roles and capabilities to handle specific tasks and distributing tasks, 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 other agents, managing control flow and state updates among different agents in complex workflows.
Let’s explore some key differences between a single agent system and a multi agent system.
Key Capabilities and Benefits
AI companies, like OpenAI and others, are finding that the current large language model (LLM) 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 and other agent based systems.
These new techniques have shown promising results in boosting model performance on complex problems and multi step tasks. Thus, helping with the development of AI agents, whether single agent systems or multi agent systems.
Some of the key benefits of multi-agent systems
1. Modularity
Breaking the tasks down for separate agents makes development, testing, and updates easier. In a multi agent architecture, you can fix or improve one piece without breaking the whole system. Additionally, a shared schema across different agents supports consistent decision-making.
2. Huge scalability
The move toward modular and multi agent systems enables scalability. You can add more specialized agents or scale to multi agent interactions without rebuilding everything, supporting complex workflows in real time.
3. Contextual awareness
Agents working together are excellent at agent communication and negotiation. By sharing context and subtasks, intelligent agents can collaborate across dynamic environments and make better decisions.
4. Enhanced reasoning and adaptability
New approaches allow AI agents to reason more effectively. They can handle multi step tasks, make complex decisions, and adapt to unexpected behaviors, essential for modern autonomous systems.
5. Resilience
If one agent fails, others continue to function. This mirrors a strong human team and highlights the robustness of agent based systems in dynamic environments.
6. Specialization
You can assign specific tasks to specialized agents, such as research, planning, or compliance. This division of labour allows other agents to focus on their strengths, improving efficiency and reducing coordination complexity.
7. Improved efficiency and resource utilization
Resource constraints like compute power and data access are managed better in multi agent systems, where agents access distributed resources and generate structured output for scalability.
Multi-agent systems architecture
Here’s an overview of the most common multi agent architectures used in MAS:
Multi-agent network
Agents can communicate with each other freely, and any agent can determine which other agents to call next. This setup leverages decentralized control, allowing individual agents to operate autonomously while achieving collective objectives.
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 structure improves agent coordination and helps distribute specific tasks efficiently across different 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 external tools to call and the arguments to pass. This shows how LLM agents integrate with workflows to ensure smooth multi agent interactions.
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. This hierarchy reduces coordination complexity and allows agent systems to manage complex workflows in dynamic environments.
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, specialized 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 multi agent systems work?
Fundamentally, multi agent systems work as digital teams, with each agent observing, reasoning, making decisions, and acting within its own scope while maintaining multi agent interactions with the overall objective. These systems are powered by groups of AI agents and independent agents, each contributing unique skills to the collective process.
The basic operational cycle is as follows:
1. Perception
Each agent begins by gathering information from its environment using available external tools. For example:
- 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 multi agent architecture, the system often includes specialized agents (sometimes called sub agents) that rely on available tools to effectively reason, plan, and execute specific tasks. 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. This reduces the risk of unexpected behaviors and enables complex agents within autonomous systems to adapt to dynamic environments.
3. Decision-Making
Now it’s time to decide: what should be done?
Some agents might use large language models (LLMs) to make complex decisions, such as predicting supply chain delays or detecting fraud. Other agents may rely on rules like "if over budget, alert finance," or be assigned as a target agent for task-specific actions.
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.
Agent communication and agent coordination allow multiple agents to negotiate and update other agents, ensuring collaboration across agent systems without unnecessary coordination complexity.
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. By producing structured output, MAS continuously cycle through this loop, reacting to new data and refining behaviors over time.
Key components of multi agent systems
The key components of multi agent systems include agents, the environment, interactions, and communication. Together, these elements explain how multi agent systems work in practice.
Agents
The system is composed of autonomous entities called agents, often designed as AI agents or software agents. Some operate as independent agents, making their own decisions, while others may act as sub agents under a supervisor. In some workflows, a target agent is chosen to perform a specific task. Collectively, these entities form robust agent systems.
Environment
The environment provides the context where agents operate. It can be physical, digital, or a hybrid dynamic environment. For instance, in a supply chain, autonomous agents monitor shipments and resources to adapt in real time. The environment also sets constraints and provides data for complex agents to function effectively.
Interactions
Interactions describe how multiple agents collaborate. They include negotiation, task-sharing, and handoffs between different agents. Well-designed interaction mechanisms prevent unexpected behaviors and allow several agents to coordinate without conflict, especially when handling complex problems.
Communication
Communication is how agents share knowledge and maintain alignment. Strong agent communication and agent coordination enable other agents to stay updated on goals and constraints. In more advanced systems, a model context protocol may be used to ensure that tool calls and outputs generate accurate, structured output.
Effective coordination ensures the system remains efficient and scalable as the number of agents grows.
Applications of multi agent systems
A team approach lets multi agent systems handle challenging scenarios, from logistics to healthcare. By combining AI agents, software agents, and independent agents, these frameworks achieve goals that single systems cannot.
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.
