A multi-agent system consists of multiple artificial intelligence (AI) agents that act autonomously but work collaboratively to understand user inputs, make decisions, and perform tasks to achieve a collective goal.

Multi-agent systems solve complex, multi-step, large-scale problems, freeing teams to focus on higher-value work.

A few examples of multi-agent systems in business include:

• Customer service: AI agents can work together to track a customer’s issue in a tech support call, recommend fixes, escalate the solution, and adjust billing or issue a refund.
• Supply chain: Agents representing different suppliers can collaborate in real-time to predict inventory needs, allocate resources, and adjust operations as needed.
• Security and fraud detection: AI agents can monitor for fraudulent activity, assess the risk, and adapt an organization’s actions to reduce threats.

Understanding multi-agent systems

The capabilities of multi-agent systems go far beyond simply automating workflows, in part, thanks to AI agents, which are essentially the next frontier of generative AI. AI agents will far exceed the capabilities of simple chatbots and advance what is possible with AI copilots. Think of a single human operating independently: One person can only have so much expertise and, working in isolation, can accomplish only so much. The same is true of AI agents: Collaborating achieves much more than working solo. Multi-agent systems autonomously collaborating with one another to handle more complex workflows can improve an organization’s productivity and efficiency.

One real-world example of a multi-agent system is in HR, where agents autonomously support the recruiting process through screening, ranking, and recommending candidates.

Another example is across the supply chain, where AI agents autonomously assess the impact of machines’ downtime, reschedule impacted orders, reallocate inventory, and plan and schedule maintenance.

• AI: The brain power at the core of an AI agent
• AI agent: A subset of intelligent agents that takes its own agency a step further through specialization, and autonomously making decisions and performing tasks
• Large language models (LLMs): An AI system trained on massive amounts of data so that AI agents can understand human language and respond conversationally—answering questions, generating text, and making other decisions based on the context it’s learned.
• Orchestration: The information exchanges between AI agents
• Environment: Physical, digital, and simulated space where AI functions

How does a multi-agent system work?

A multi-agent system distributes tasks and communication among individual agents, each of which brings their specialized talent to collectively achieve a goal and learn from it in a shared environment. This dividing of tasks is the key to a multi-agent system’s ability to solve complex problems.

Key multi-agent systems architectures

A multi-agent system typically operates as either a centralized or decentralized network.

• In a  centralized network, a single server controls the AI agents’ interactions and information. This orchestrator (in a human scenario, the project manager) has the ability to reason over the overall process and system, simplifying communications and standardizing information. The primary drawback for using a centralized multi-agent system is that it can create a single point of failure.
• In a  decentralized network, AI agents control their own direct interactions with each other instead of a single server (“project manager”) controlling them. The specialized AI agents have a common understanding and shared responsibility of what they’re trying to accomplish. While more robust and scalable than a centralized network, the primary drawback for using a decentralized multi-agent system is that it requires more complex coordination.

What's the difference between a multi-agent system and a single agent?

There are several differences between multi-agent systems and single agent systems.

• Single AI agents work autonomously within their own environment to perform a requested task. They use LLMs to understand user inputs, they design workflows, and they can call on tools to execute the workflows they plan.
• In a multi-agent system, multiple AI agents interact with each other fluidly and iteratively, bringing together their individual properties and expertise to not only to achieve the task, but also to learn. A multi-agent system can have thousands of individual agents.

Collaboration is a strategy every business uses to make teams greater than the sum of their parts, and these tactics can include project management, scrum meetings, and discussion forums. Collaboration allows AI agents to achieve more than when they act independently; for example, missing opportunities outside their specialization. By talking to each other, AI agents act more like a human team does and can fill gaps that otherwise go unaddressed.

The difference between the two systems is having a single expert performing their individual specialty as a cog in a wheel versus a team of experts coordinating and succeeding in real time.

A key distinction between a single agent system and a multi-agent system is the latter’s superior ability to understand the problem’s complexity and its effectiveness in addressing that problem.

Think of a project manager who brings together a team of individual specialists—for example, a software engineer, a designer, a product manager, and so on—to do greater things by collaborating. A multi-agent system is like a project manager or a project plan; it can do more using a team of specialists. Assigning AI agents to tasks according to their specialization helps the LLM prioritize what to focus on so it can deliver a better performance.

Using specialized AI agents in a multi-agent system also gives developers a framework to follow, allowing them to break down their tasks into subtasks that are easier to code. Finally, many teams using multi-agent systems may see them outperform single agent systems, driving new innovations and developer productivity.

When to choose a multi-agent system

Generally speaking, any organization already using AI agents can realize the benefits of a multi-agent system. Choosing between a single agent system and a multi-agent system depends on an organization’s or project’s specific needs; achieving the goal comes down to training, maintenance, and processing outputs—the same tasks that are required to grow a team of humans.

• A single agent system is ideal when tasks are straightforward and well-defined.
• A multi-agent system is ideal when tasks are complex and require expertise across multiple disciplines.

Examples of real-world multi-agent systems

Thanks to their flexibility and adaptability, multi-agent systems are ideal for roles in nearly every industry.

• Automated manufacturing lines: Reducing downtime with predictive maintenance AI agents that audit equipment and communicates with another agent to schedule necessary repairs
• Smart power grids: Optimizing energy distribution using one agent to monitor weather systems and a second agent to use that data to predict energy demand
• Autonomous vehicles: Increasing safety with the AI agent controlling the camera systems collaborating with the on-screen display agent to guide the driver
• Patient healthcare and coordination: Accelerating diagnosis and intervention using agents representing different healthcare specialists that work together to design a comprehensive treatment plan
• Supply chain management: Responding faster to changes using one AI agent to monitor sales trends and communicate with another agent to adjust reordering levels
• Transportation systems: Improving navigation using one agent to monitor traffic conditions that it shares with a second agent that optimize routes for public transportation

Benefits of multi-agent systems applications in technology and AI

Given a complex task like writing code, a multi-agent system would distribute the job as assignments for individual agents representing the software engineer, product manager, designer, quality assurance engineer, and other roles needed for the task. Each AI agent does its part, and the overall multi-agent system coordinates the collective work and enables the agents to collaborate, reasoning on next steps and beyond to ultimately accomplish the overall goal.

While single AI agents are powerful on their own, they can deliver even more accuracy, scalability, and flexibility when part of a multi-agent system. A multi-agent system can free up staff to focus on higher value, more strategic work instead of spending time overseeing manual, repetitive, and labor-intensive workflows.

Overall benefits of a multi-agent system include:

• Collaboration: Leveraging the collective intelligence of a team of AI agents can understand and solve increasingly complex problems.
• Performance: Enabling a bigger pool of specialized AI agents to interact and learn in their environment can accomplish more, faster, than single agents acting independently.
• Efficiency: The multi-agent system design pattern gives developers a framework for how to break down complex tasks into subtasks that are easier to code.

Building a multi-agent system

When building a multi-agent system, it’s important to consider the quality and depth of the data available to an organization.

Designing a multi-agent system

Empowering smarter decisions and achieving efficiencies at scale starts with a system tailored to an organization’s unique data landscape and the nuances of its industry. This ensures the AI agents comprising the organization’s multi-agent system have the most relevant, reliable, and trustworthy data available.

1. Determine project needs and choose the most ideal LLM to meet those needs. The best LLMs for multi-agent systems offer advanced reasoning capabilities, reading comprehension, language understanding, and code generation.
2. Define the role and objectives for each AI agent. Make sure each AI agent knows what to do as part of the greater goal. Assign the correct LLM and any necessary tools the AI agents might need.
3. Initiate a workflow for each AI agent. Orchestrate AI agents so their tasks are done correctly, and collaboration is harmonious and effective. Workflow initiation includes establishing the AI environment, defining tasks, launching agents, monitoring communication, and generating outputs.

Key considerations when implementing a multi-agent system

Every system an organization deploys must operate efficiently, ethically, and within established regulations, which requires constant evaluation and a governance framework.

• Establish ethical practices for using AI.

• Identify metrics for how each AI agent performs.

• Retest system performance when the number of AI agents and/or tasks increases.

• Evaluate the system’s ability to recover from errors, adapt to changes, and deliver business continuity.

• Continually monitor and audit the multi-agent system to identify areas for improvement.

Governance-specific considerations

• Enforce standards that protect data privacy and prevent bias and comply with regulatory laws and industry standards.
• Program AI agents to monitor the activity of other agents and identify any ethical breaches.
• Maintain visibility on AI agent decision-making to establish trust.
• Establish transparency on multi-agent system operations to meet regulation compliance.
• Identify and mitigate risk to reduce errors and increase reliability.

Human oversight

• Use a human-in-the-loop model for workflows to maintain alignment with human value.
• Include human touch points to monitor and prevent unsanctioned autonomous actions.

Challenges of multi-agent systems

While multi-agent systems are highly capable, they come with some challenges to consider.

• AI still needs to become proficient in addressing complex tasks, workflows, and business processes that are not easily preconfigured or require multiple steps to complete.
• Having more AI agents increases the system’s complexity, configuration, and needed maintenance.
• A decentralized multi-agent system can experience unpredictable behavior among its AI agents that pass along incorrect information on the basis it’s true. Detecting the source of the inaccuracy and managing behavior based on bad data can be difficult.
• Humans using AI must also follow the rules and guidelines for the ethical use of AI.

What's next for using AI multi-agent systems

AI agents represent a major shift in the way work gets done, from improving operational efficiencies to delivering more service value for less effort.

Emerging trends and predictions

As AI becomes more capable and data management more rigorous, multi-agent systems will evolve to generate increasingly accurate, applicable, and adaptive outcomes. Some scenarios include virtual customer service to answer common questions, monitoring supply chains and managing inventory, forecasting market trends and recommending potential growth opportunities, updating job postings and generating candidate lists, and tracking and preventing fraud by monitoring transactions in real time.

One future trend will be to combine multi-agent systems with increasingly dynamic machine learning algorithms to advance data analyzation and application development. Another trend leverages the increasing intelligence and capabilities of the individual AI agents that contribute to the efficacy of a multi-agent system.

Implications for AI and technology

As AI agents continue to adapt and learn, multi-agent systems will drive AI deeper into more complex problems facing organizations of every size, focus, and industry. These capabilities position AI to have a much bigger impact on businesses and society.

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