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-stage, large-scale problems, enabling teams to focus on higher-value work.

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

• Customer service: AI agents can collaborate to track a customer’s issue during a technical support call, recommend solutions, escalate the matter, and adjust billing or issue a refund.
• Supply chain: Agents representing different suppliers can collaborate in real time to predict stock requirements, allocate resources, and adjust operations as needed.
• Security and fraud detection: AI agents can monitor for fraudulent activity, assess the risk, and adapt an organisation’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 co-pilots. Consider a single person working independently: One individual can possess only a certain amount of expertise and, working alone, can achieve only so much. The same applies to AI agents: Collaborating achieves much more than working alone. Multi-agent systems autonomously collaborating with one another to handle more complex workflows can improve an organisation’s productivity and efficiency.

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

Another example is across the supply chain, where AI agents autonomously assess the impact of machinery downtime, reschedule affected orders, reallocate stock, and plan and schedule maintenance.

• AI: The brainpower at the core of an AI agent
• AI agent: A subset of intelligent agents that takes its own agency a step further through specialisation, and autonomously making decisions and performing tasks
• Large language models (LLMs): An AI system trained on vast amounts of data so that AI agents can understand human language and respond in a conversational manner—answering questions, generating text, and making other decisions based on the context it has learnt.
• Orchestration: The information exchanges between AI agents
• Environment: Physical, digital, and simulated space where AI operates

How does a multi-agent system work?

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

Key multi-agent system architectures

A multi-agent system typically operates as either a centralised or decentralised network.

• In a centralised 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 about the overall process and system, simplifying communications and standardising information. The main disadvantage of using a centralised multi-agent system is that it can create a single point of failure.
• In a decentralised network, AI agents control their own direct interactions with each other instead of a single server (“project manager”) controlling them. The specialised AI agents have a common understanding and shared responsibility for what they are trying to accomplish. While more robust and scalable than a centralised network, the primary drawback of using a decentralised multi-agent system is that it requires more complex coordination.

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

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

• Individual AI agents operate autonomously within their own environment to carry out a requested task. They use LLMs to understand user inputs, they design workflows, and they can call upon 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 not only to accomplish the task, but also to learn. A multi-agent system can have thousands of individual agents.

Collaboration is a strategy that 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 enables AI agents to achieve more than when they act independently; for example, missing opportunities outside their specialisation. By communicating with one another, AI agents behave more like a human team and can address gaps that would otherwise remain unaddressed.

The difference between the two systems is having a single expert performing their individual speciality 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 complexity of the problem and its effectiveness in addressing that problem.

Imagine 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 achieve greater things through collaboration. A multi-agent system is like a project manager or a project plan; it can achieve more by using a team of specialists. Assigning AI agents to tasks according to their specialisation helps the LLM prioritise what to focus on so it can deliver a better performance.

Using specialised AI agents in a multi-agent system also provides developers with a framework to follow, enabling them to break down their tasks into subtasks that are easier to code. Finally, many teams using multi-agent systems may find that they outperform single-agent systems, leading to new innovations and increased developer productivity.

When to choose a multi-agent system

Generally speaking, any organisation already using AI agents can realise the benefits of a multi-agent system. Choosing between a single-agent system and a multi-agent system depends on an organisation’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 people.

• 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 communicate with another agent to schedule necessary repairs
• Smart power grids: Optimising energy distribution using one agent to monitor weather systems and a second agent to use that data to predict energy demand
• Autonomous vehicles: Enhancing safety with the AI agent controlling the camera systems, working together with the on-screen display agent to guide the driver
• Patient healthcare and coordination: Accelerating diagnosis and intervention using agents representing different healthcare specialists who work together to design a comprehensive treatment plan
• Supply chain management: Responding more quickly to changes by using one AI agent to monitor sales trends and communicate with another agent to adjust reordering levels
• Transport systems: Improving navigation by using one agent to monitor traffic conditions, which it shares with a second agent that optimises routes for public transport

Benefits of multi-agent system applications in technology and AI

Given a complex task such as writing code, a multi-agent system would allocate the work as assignments for individual agents representing the software engineer, product manager, designer, quality assurance engineer, and other roles required 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 about next steps and beyond to ultimately accomplish the overall goal.

While individual AI agents are powerful on their own, they can provide even greater 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 labour-intensive workflows.

Overall benefits of a multi-agent system include:

• Collaboration: Harnessing the collective intelligence of a team of AI agents can comprehend and solve increasingly complex problems.
• Performance: Enabling a larger pool of specialised AI agents to interact and learn in their environment can achieve more, more quickly, than individual agents acting independently.
• Efficiency: The multi-agent system design pattern provides developers with a framework for breaking down complex tasks into subtasks that are easier to code.

Constructing a multi-agent system

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

Designing a multi-agent system

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

1. Determine project requirements and select the most suitable LLM to meet those requirements. The best LLMs for multi-agent systems offer advanced reasoning abilities, reading comprehension, language understanding, and code generation.
2. Define the role and objectives for each AI agent. Ensure that each AI agent knows what to do as part of the overall goal. Assign the correct LLM and any necessary tools the AI agents may require.
3. Initiate a workflow for each AI agent. Orchestrate AI agents so that their tasks are completed 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 organisation 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.

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

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

Governance-specific considerations

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

Human supervision

• Use a human-in-the-loop model for workflows to maintain alignment with human values.
• Include human points of contact to monitor and prevent unauthorised 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 pre-configured or require multiple steps to complete.
• Having more AI agents increases the system’s complexity, configuration, and required maintenance.
• A decentralised multi-agent system can experience unpredictable behaviour among its AI agents that pass along incorrect information on the basis that it is true. Detecting the source of the inaccuracy and managing behaviour based on poor 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 is carried out, from improving operational efficiencies to delivering greater service value with less effort.

Emerging trends and forecasts

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 stock, forecasting market trends and recommending potential growth opportunities, updating job adverts 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 analysis and application development. Another trend takes advantage of the increasing intelligence and capabilities of the individual AI agents that contribute to the effectiveness of a multi-agent system.

Implications for AI and technology

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

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