What are multi agent systems? A guide for business leaders

What are multi agent systems?

Multi-agent systems (MAS) leverage multiple autonomous AI agents to collaborate and solve complex problems in a shared environment. Each agent works independently to process information, make decisions, and take action while coordinating with others to optimize performance.

For example, in supply chain management, one agent might track inventory while another forecasts demand. By distributing tasks and sharing outputs, these specialized agents can together help a business to operate more efficiently, respond to change faster, and make smarter decisions with less manual effort.

Multi agent systems vs single AI agents

Unlike a solo AI agent that operates on its own, multi-agent systems have numerous AI agents working together to achieve a common goal. It’s the difference between a lone problem-solver and a coordinated team that can tackle multiple challenges simultaneously.

For example, a single AI agent in customer service might handle basic chatbot interactions, while a multi-agent system could combine chatbots, recommendation engines, and fraud detection algorithms to provide a seamless, intelligent customer experience.

In short, multi-agent systems provide a more flexible and resilient approach to AI-driven decision-making. This makes them ideal for businesses that want to streamline and automate complex, multi-step processes that exceed the capabilities of a single AI agent.

Learn more: What is an AI agent?

How do multi agent systems work?

Learning the key characteristics, components, and architecture of multi-agent systems can help you better understand how agents communicate, make decisions, and collaborate effectively.

Characteristics of multi agent systems

The key characteristics of a multi-agent system include:

Autonomy: Each autonomous agent operates independently, making decisions without human intervention as part of AI agentic workflows. This allows for decentralized problem-solving and increased flexibility and even AI workflow automation of routine processes and tasks.

Collaboration: Agents share information and work together to achieve common goals. This improves efficiency, especially in large-scale, dynamic environments.

Adaptability: Multi agent systems can perceive their environment and adapt in real time to changing inputs, making them effective in dynamic environments reliant on real-time data, such as finance, logistics, and cybersecurity.

Proactivity: Besides reacting to changes, agents can take initiative, predict future needs, and act accordingly. This allows businesses to anticipate challenges rather than just respond to them.

Communication: Agents exchange data via predefined protocols, ensuring smooth coordination. Effective communication helps prevent conflicts and enables synchronized decision-making.

Scalability: New agents can be added or removed without disrupting the system, making MAS highly scalable for growing business needs.

Coordination: To tackle common objectives, coordination is required to manage how agents communicate and ensure their actions are in sync with the larger goal. This requires AI orchestration.

Components of multi agent systems

Multi-agent systems rely on specific components to function effectively and achieve their goals, including:

Agents: The core units of a multi-agent system, each with its own decision-making capabilities. Agents can be software-based (e.g., virtual assistants) or physical (e.g., autonomous robots).

Environment: The space where agents interact, whether a digital marketplace, a smart factory, or a logistics network. The environment provides data that agents use to make decisions.

Communication mechanism: The protocols and methods agents use to exchange information. This ensures seamless collaboration and coordination between agents.

Perception and sensors: Tools that allow agents to observe their environment. In physical MAS, these can be cameras and sensors. In digital MAS, they can be APIs or data streams.

Decision-making engine: The logic or algorithms that guide an agent’s choices. This can include rule-based systems, machine-learning models, or optimization techniques.

Learning mechanism: Some MAS incorporate machine learning to improve over time. Agents analyze past interactions and refine their strategies for better performance.

Architecture of multi agent systems

A multi-agent system can also be structured differently depending on how agents interact and make decisions. The two primary architectures are centralized and decentralized, each with advantages and trade-offs.

Centralized Architecture

In a centralized MAS, a central entity (such as a master agent or a control hub) oversees and coordinates all agent activities. Agents follow directives from the central system and report back their results.

• Advantages: Easier to manage, ensures consistency, and simplifies troubleshooting.

• Disadvantages: A single point of failure can disrupt the system, and it may struggle to scale efficiently.

• Example: A fleet management system where a central AI assigns delivery routes to autonomous vehicles.

Decentralized Architecture

In a decentralized MAS, agents operate independently and make decisions based on local information and peer-to-peer communication. There is no single point of control, making the system more dynamic and resilient.

• Advantages: More robust, scalable, and adaptable to changing conditions.

• Disadvantages: Coordination can be more complex, and inconsistencies may arise if agents have conflicting goals.

• Example: A swarm of drones collaborating on search-and-rescue missions without a central commander.

Many real-world MAS use a hybrid approach, combining centralized oversight with decentralized agent decision-making to balance control and flexibility. The choice of architecture depends on business needs, scalability requirements, and the complexity of the tasks being automated.

Multi-agent system architectures can also be understood in terms of how AI agents operate in relation to each other, including:

1. Network: Agents can communicate directly with any other agent in the system, each deciding which other agent to interact with next.

2. Supervisor: A central supervisor agent coordinates communication and allocates tasks among other agents, making decisions on which agent should be called next for a task.

3. Supervisor with tool calling: Individual agents are represented as tools (APIs, software integrations, etc.) and invoked by the supervisor agent as needed.

4. Hierarchical: A multi-tiered system where supervisors manage groups of agents or other supervisors, allowing for control of more complex AI workflows and scalability.

5. Custom workflow: Agents communicate with only a subset of other agents based on predefined rules,. Some parts of the workflow are deterministic, while others allow agents to decide on next steps.

Types of multi agent systems

As we touched on above, multi-agent systems can have different interaction models depending on their objectives. Some focus on collaboration, while others involve competition between agents. Here’s a closer look at how AI systems can structure the interactions between different AI agents.

Cooperative agents

Cooperative multi-agent systems work together toward a shared goal, optimizing efficiency and decision-making. Each agent contributes by sharing information, coordinating actions, and adapting to changes in real-time. Cooperative agents are particularly useful in environments where collaboration leads to better outcomes than individual efforts. 

For example, in supply chain management, a team of specialized agents can manage the respective tasks of managing inventory, forecasting demand, and optimizing delivery routes to maximize the performance and precision of the overall system.

Adversarial agents

Adversarial multi-agent systems involve agents that compete against each other, often in scenarios where strategic decision-making is crucial. Each agent tries to outsmart the others by anticipating their moves, maximizing its outcome while minimizing the opponent's success. These systems are common in security, finance, and competitive markets where different entities have conflicting objectives.

For instance, in finance, adversarial agents might simulate traders' actions to predict market fluctuations, exploit pricing inefficiencies, or engage in high-frequency trading strategies that aim to outperform others in the market.

Mixed-agent systems

Mixed-agent systems combine elements of both cooperative and adversarial agents within the same environment. In these systems, some agents work collaboratively toward shared goals, while others may act independently or even in opposition, depending on the context. This hybrid approach is ideal for complex scenarios in which different agent strategies are needed to balance cooperation and competition.

For example, mixed-agent systems can be helpful in ecommerce. Some agents could work together to recommend products to customers based on shared user data, while others compete to optimize pricing strategies, ensuring the platform remains competitive.

Hierarchical multi agent systems

Hierarchical multi-agent systems organize agents into a hierarchy, where higher-level agents have greater control and oversight over lower-level ones. This structure is useful for tasks that require delegation, coordination, and supervision, where each agent's role is defined within a broader organizational framework.

For instance, hierarchical multi-agent systems can be used in manufacturing to manage production lines. Higher-level agents may oversee scheduling, inventory management, and quality control, while lower-level agents handle specific tasks like machinery operation or assembly.

Heterogeneous multi agent systems

Heterogeneous multi-agent systems consist of agents that vary in their capabilities, roles, or functions. These systems are beneficial when tasks require specialized agents with different skills or types of knowledge to tackle a wide range of problems. For example, a heterogeneous multi-agent system in healthcare could involve agents with specialized roles such as medical diagnosis, patient monitoring, and administrative tasks.

Examples of multi agent systems

No matter your industry, you’re likely dealing with complex, evolving tasks that could benefit from smarter, more adaptive solutions. Here are some examples of how you can leverage MAS, depending on your industry.

Customer service

Delivering top-notch AI customer service requires a mix of speed, personalization, and efficiency. This is where multi-agent systems excel.

Picture a system where a frontline chatbot manages FAQs and live inquiries, ensuring customers get instant responses. Meanwhile, a CRM-integrated agent analyzes customer history, prioritizing urgent cases for human representatives. In the background, another AI-driven agent reviews feedback, detecting trends and suggesting improvements.

The result? Faster resolutions, smoother interactions, and a more tailored experience for every customer.

Cybersecurity

Cyber threats evolve constantly, and businesses need security systems that can detect, respond to, and adapt in real time. Multi-agent systems provide that extra layer of intelligence, continuously monitoring and neutralizing risks before they cause damage.

You can assign specialized agents to different security tasks. For example, one agent monitors network traffic, detecting unusual patterns that might indicate an attack. Another focuses on email security, identifying phishing attempts before they reach employees. Meanwhile, a response agent isolates compromised systems and blocks suspicious activity automatically.

Unlike static defense systems, these agents also learn to adapt to new threats over time. And since MAS operates 24/7, your security never sleeps.

Ecommerce

Success in ecommerce depends on anticipating customer needs, managing inventory efficiently, and staying ahead of competitors. A multi-agent system can streamline these processes, making operations more responsive and intelligent.

You might have one agent analyzing browsing behavior, predicting what products customers want next. Another tracks inventory in real-time, preventing stockouts and overstock. Meanwhile, a pricing agent monitors competitor trends, dynamically adjusting offers to stay competitive.

Beyond logistics, MAS can also enhance customer service for ecommerce stores. Virtual agents can handle inquiries, track orders, and troubleshoot issues.

Finance

Multi-agent systems can give businesses in the financial sector an edge by automating tasks such as fraud detection, market analysis, and portfolio management.

For example, one agent analyzes market trends, quickly spotting new opportunities. Another monitors transactions in real-time, identifying and flagging potential fraud. A third assesses risk, adjusting portfolios to protect against losses and optimize returns.

With MAS handling these critical tasks, you can respond faster to market changes, reduce financial risks, and improve overall efficiency.

Healthcare

Healthcare moves fast, and every second counts. Multi-agent systems help providers speed up diagnoses, personalize treatments, and make sure patients get the care they need when they need it.

Imagine a system where agents work together to track a patient’s health journey. One agent could monitor vital signs in real-time, sending alerts to doctors if there are significant changes. Another agent could coordinate the scheduling of medical tests and follow-up appointments. Meanwhile, a third agent could analyze treatment effectiveness and recommend adjustments based on the patient’s progress.

MAS also has the potential to improve hospital efficiency by managing resources such as staff availability, bed capacity, and medical supply levels.

Marketing

Marketing is all about the right message at the right time. Multi-agent systems can help make that happen. And they don’t just automate campaigns. They can also analyze customer behavior, predict trends, and fine-tune strategies in real-time.

Think about the last time you browsed a website and later saw an ad for the exact product you were considering. With MAS, different agents can handle each step of the process. One tracks browsing habits to understand customer interest. Another agent looks at past purchases to suggest the best promotions. A third adjusts messaging across email, social media, and ads to boost engagement.

MAS can also help you maximize your marketing budget. For example, agents could monitor ad performance and adjust bids on platforms like Google Ads and Facebook Ads to ensure every dollar drives the best ROI.

Real estate

Managing properties, analyzing market trends, and closing deals all require quick decision-making and seamless coordination. Multi-agent systems help real estate professionals automate key tasks, improve client interactions, and optimize property management.

For example, one agent could track market trends and property values, providing real-time insights on pricing. Another could manage client interactions, schedule showings, and automate follow-ups. Meanwhile, a third agent could handle contract processing and documentation, ensuring smooth transactions. With a multi-agent system, real estate businesses can respond to market shifts faster and enhance the buying or renting experience for clients.

Supply chain management

Keeping a supply chain running smoothly isn’t easy. Delays, shortages, and shifting demand can throw everything off balance. Multi-agent systems can help businesses stay ahead of these challenges.

Imagine a system where one agent tracks inventory levels, automatically reordering stock before you run low. Another monitors shipping routes, adjusting deliveries to avoid traffic, weather delays, or bottlenecks. And if a supplier suddenly falls through, MAS can instantly reroute shipments or suggest alternatives to keep everything on track.

Ultimately, a multi-agent system can make supply chains more efficient, adaptable, and resilient. It can also help businesses reduce costs, minimize waste, and improve delivery times.

Advantages of multi agent systems for enterprises

Let’s imagine you need to explain to your team the core benefits of adopting a multi-agent system. What should you highlight to make the advantages clear?

Enhanced decision-making through distributed intelligence

In a multi-agent system, each agent can analyze and process information simultaneously, drawing on their unique perspective. By integrating their insights, your team can act on more comprehensive information.

Real-time responsiveness and adaptability in dynamic environments

As conditions change, whether it’s shifts in customer demand or market fluctuations, MAS can adjust instantly. For example, if one agent detects a spike in demand for a product in a retail business, it can alert other agents to reorder or adjust marketing strategies accordingly.

Increased efficiency and automation

A MAS can automate repetitive tasks, reducing the need for manual intervention and freeing up employees for higher-value work. For example, a team of vertical AI agents that specialize in scheduling, process optimization, or decision-making can collaborate in a MAS to automate complex tasks and processes.

Scalability and flexibility in handling diverse tasks

With MAS, you can add more agents to handle specific tasks as your business needs change. That means companies can grow and adapt without requiring a complete overhaul of their systems.

Cost savings over time

MAS helps businesses reduce operational costs by improving efficiency, reducing waste, and automating key processes. Fewer inefficiencies mean lower expenses, higher profitability, and a better return on investment.

How to implement multi agent systems

If you're considering implementing a multi-agent system for your business, you might wonder: do you need to build one from scratch, or are there ready-made solutions?

The good news is that there are multiple ways to access MAS, depending on your business needs, technical expertise, and budget. Here’s how companies typically approach MAS adoption.

1. Build a MAS in-house

Building an in-house multi-agent system offers complete control and customization. However, it also requires a strong AI and software engineering team with expertise in artificial intelligence, distributed systems, and agent-based modeling. The process includes:

• Defining system goals and agent roles (e.g., decision-making, automation, optimization).

• Choosing an architecture (centralized, decentralized, or hybrid).

• Developing AI-powered agents using machine learning, rule-based systems, or heuristic models.

• Implementing inter-agent communication to ensure seamless coordination.

• Testing, iterating, and optimizing based on performance data.

2. Use open-source MAS frameworks

For businesses that want to develop MAS without starting from scratch, open-source frameworks provide a solid foundation. These platforms offer pre-built tools for designing and managing multi-agent environments. Some widely used frameworks include:

• Autogen

• CrewAI

• LangGraph

• Phidata

3. Purchase MAS solutions from software providers

If developing MAS in-house isn’t an option, many companies turn to MAS software providers that offer ready-made or customizable solutions. Purchasing an MAS solution can provide a quicker implementation with built-in support and maintenance.

Which option is right for you?

Choosing the right approach depends on your business goals, technical resources, and timeline. No matter which path you take, MAS can provide a powerful edge in automation, decision-making, and efficiency.

Learn more: How to build an AI agent: The 8 key steps

Challenges of multi agent systems

Although multi-agent systems offer immense benefits, you’re still dealing with a technology that requires careful planning and execution. And it can become a costly, inefficient endeavor if you don’t address the key challenges upfront.

Here’s what you need to consider to ensure a successful MAS implementation.

Technical complexity

One of the biggest technical hurdles in MAS implementation is ensuring smooth coordination and communication between agents. Each agent must have the necessary data, processing power, and network bandwidth to function smoothly. And if the agents aren’t correctly orchestrated, conflicts can arise, leading to inefficiencies or errors.

Autonomy and decision-making authority

Should an AI agent have the authority to make high-stakes decisions without human oversight? What happens when agents make conflicting choices? These questions highlight the ethical dilemmas of a multi-agent system.

Establishing clear guidelines for human decision-making, accountability structures, and fail-safe mechanisms can help mitigate potential risks while maintaining the benefits of automation.

Regulatory frameworks and compliance issues

Many industries have strict compliance requirements that MAS must adhere to, from financial regulations to healthcare data protection laws. However, because MAS is still an emerging technology, legal frameworks may not yet fully address its implications.

Therefore, businesses must proactively align MAS operations with existing compliance standards while staying updated on evolving regulations.

Security and privacy concerns

With multiple agents operating independently, the risk of data leaks, unauthorized access, or malicious interference increases. Each agent may also have different access levels and permissions, making it difficult to enforce security protocols uniformly.

Businesses must implement strong encryption, authentication measures, and real-time monitoring to prevent vulnerabilities. Additionally, privacy regulations, such as GDPR or CCPA, must be carefully considered when the system processes sensitive data.

Trust and transparency

For MAS to be fully integrated into business operations, stakeholders—whether employees, customers, or regulators—must trust the system. However, MAS often operates as a “black box,” making it difficult to understand how decisions are made.

Lack of transparency can lead to skepticism, resistance, or even legal challenges. To address this, businesses should invest in explainable AI (XAI) techniques, clear reporting mechanisms, and open communication about MAS processes and limitations.

Getting started with multi agent systems

Multi-agent systems are transforming how businesses operate, offering a flexible and effective approach to intelligent automation that maximizes the decision-making and efficiency of individual AI agents.

If you're ready to explore how multi agent systems can streamline operations and improve your AI customer service, Sendbird can help. Our robust AI agent platform makes it easy for any team to build AI agents upon a foundation of enterprise-grade infrastructure that offers the scalability, security, and adaptability required for any environment or application. 

To learn more, contact our team of AI experts. 

If you want go deeper, you might enjoy these related topics about AI:

• The AI workforce: What is it? How do you build it?
• The rise of the AI agent-to-agent (A2A) economy
• What is agentic AI?

Frequently asked questions about multi agent systems

1. What is the primary goal of MAS in AI?

The main goal of multi-agent systems (MAS) is to enable multiple autonomous entities to work together to solve complex problems more efficiently than a single AI agent could. By distributing tasks across multiple agents, MAS can improve decision-making, optimize resources, and enhance adaptability in dynamic environments. This makes MAS ideal for industries requiring real-time coordination, such as finance, supply chain management, and cybersecurity.

2. What is an example of a multi-agent?

A great example of a multi-agent system is automated stock trading. In this system, multiple AI agents operate independently yet collaboratively to analyze market trends, execute trades, and manage risk. One agent might monitor global news and economic indicators, another could track stock price movements, and a third might adjust investment strategies based on risk assessment models. By working together, these agents help traders and financial institutions execute faster, data-driven decisions while minimizing risk and maximizing returns.

3. Where are multi agent systems used?

Multi-agent systems are used across various industries, including supply chain management, customer service, e-commerce, finance, and healthcare. For example, in e-commerce, MAS can manage inventory, personalize recommendations, and automate customer support simultaneously.

4. How do you build a multi agent system?

Building a multi-agent system involves several steps, including defining business objectives, selecting the right AI frameworks, ensuring data availability, and designing agent communication protocols. Businesses must also establish security measures, compliance guidelines, and performance benchmarks to monitor effectiveness. A pilot program is often the best way to test and refine the system before full deployment. An AI agent builder can help simplify the process of creating and deploying AI agents.