Teams that embrace artificial intelligence (AI) are often looking for ways to work more efficiently or streamline operations in ways that previously would not have been possible.
Multi-agent AI systems enable teams to automate sophisticated workflows and tasks with a flexible, scalable structure that quickly learns and adapts to improve future performance.
In this guide, we’ll discuss what multi-agent AI systems are, how they work, and possible applications in a business setting.
What is multi-agent AI?
Multi-agent systems (MAS) involve multiple artificial intelligence (AI) agents working together, each completing dedicated tasks and workflows that could not be accomplished through a single agent or AI model.
The individual agents in a multi-agent system have specialized areas of expertise or are designed for a specific goal. The combined system is built to handle complex problems or tasks through collaboration, information sharing, and delegation.
In turn, multi-agent systems are scalable and adaptable to different scenarios and problem-solving. They enable quicker decision-making and better efficiency during high-demand periods or complex problem sets.
Single-agent vs multi-agent systems
As the names might suggest, the key difference between a single-agent and a multi-agent system is the number of AI agents the structure contains.
As a result, single-agent systems are not equipped to handle the same complex workflows that a multi-agent system can handle. Instead, they are best for a well-defined task or problem, without requiring further delegation or collaboration.
How does multi-agent AI work?
Here are the main components that enable a multi-agent system to work as intended:
- Independent agents: Each agent in the system has specific expertise and is designed to handle one unique task or sub-task proficiently.
- Collaboration and communication: The system enables messaging between agents, allowing them to share information and coordinate decision-making workflows.
- Orchestration: There is a logic system that delegates tasks to the appropriate agents, given the context of the request, and routes them properly until the workflow is complete.
- Learning and adaptation: The agents share a knowledge base that they can access as needed, helping them learn and adapt over time to improve performance.
Structures of multi-agent systems
Depending on the specific workflow or goal, multi-agent systems may be structured in a few different ways to ensure proper handling. This includes:
Hierarchical structure
In a hierarchical structure, agents are organized with different levels of authority in the decision-making process.
Visually, these systems have a tree-like structure, with fewer agents holding more of the decision-making power than others. Agents at the lower levels are designed to handle more basic tasks and escalate to higher levels for more complex issues or decisions.
Pros:
- Roles and responsibilities are clearly defined
- There’s a straightforward path for escalation
Cons:
- Not as efficient as other structures
- Bottlenecks are possible when tasks reach the top-ranked agents
Holonic structure
A system with a holonic structure has agents that are organized into different “holons”, which are comprised of an agent and its sub-agents, where relevant.
The holons all interact with each other as if they were sole agents. However, the inner workings of a holon may involve a number of sub-agents, each responsible for a specific task.
Pros:
- These systems are highly scalable; new sub-agents can be added to a holon to handle new tasks
- There is a clear hierarchy within a holon for task delegation and decision-making
Cons:
- Inefficiencies are possible since sub-agents in one holon aren’t able to communicate with those in another holon directly
- Takes a bit more coordination and time to set up and organize this system compared to others
Coalition structure
Multi-agent systems may also have a coalition structure. In this type of system, agents organize themselves temporarily into different groups or “coalitions” based on their unique areas of expertise and the specific problem at hand.
Then, once the workflow is complete, they disband until they need to form a new coalition for a new problem.
Pros:
- Immense flexibility as agents are not exclusive to one group, and can be delegated where needed for optimal efficiency
- Quicker problem-solving and decision-making as different agents pool together their knowledge and resources to tackle a task
Cons:
- Challenging to program agents for this level of flexibility and autonomy
- Potential for misaligned goals or miscommunication as coalitions are formed on demand
Key benefits of multi-agent AI systems
There are several key advantages of using a multi-agent AI system over a single-agent system, including:
- Improved efficiency: Dedicated agents handle specific workflows based on their expertise, working in parallel with other agents to provide better overall efficiency.
- Scalability and flexibility: Systems can handle increasingly complex workflows and higher volumes by adding more dedicated agents.
- More advanced decision-making:
Applications of multi-agent AI
Multi-agent AI systems can be used in any environment where complex decision-making or workflow coordination takes place.
The following are some of the practical examples of how businesses may implement these systems to automate sophisticated workflows:
Business process automation
Teams that want to automate repetitive tasks and workflows, like invoice processing, account reconciliation, or payroll processing, may implement a multi-agent AI system for accounting to accomplish this.
Instead of relying on individual employees to handle the specific tasks in these workflows manually, businesses can coordinate individual AI agents that specialize in each subtask for better speed, efficiency, and accuracy.
Complex data analysis
Another business application of multi-agent systems is for complex data analysis, providing a level of speed and accuracy that a single agent or human would not be able to provide.
For example, one agent may be responsible for extracting data inputs from multiple sources, another agent may be designed to parse the web for supplemental information, and another may process the information within the provided parameters or model.
Logistics and robotics
Businesses can streamline logistics and supply chain management with the help of a multi-agent system.
Each agent can be dedicated to a specific task or segment of the supply chain, coordinating task execution and decision-making among robots or digital tools.
This might include monitoring inventory levels to trigger reordering at a certain point, or providing customers with real-time updates on their orders upon request.
Customer service
Another potential application of multi-agent AI systems is to streamline customer service and enhance the chatbot experience.
Teams can configure support agents to ingest customer queries, answer information requests based on the shared knowledge base, and delegate as needed based on the context of the interaction.
Challenges in implementing multi-agent systems
Multi-agent AI systems can provide a number of benefits in terms of better efficiency and process automation. However, there are still some potential challenges to keep in mind, which we’ll discuss below.
Technical hurdles and integration issues
Multi-agent AI systems are technologically advanced architectures that rely on proper setup and configuration to ensure they perform as intended.
Teams that don’t have technical expertise may face hurdles during implementation or need to seek out external support to set up these systems initially.
Further, their existing tech stack must be able to integrate and interact with AI agents, which can be a limiting factor, depending on what systems they use.
Managing communication and coordination among agents
Additionally, these systems must be configured so that agents have a clearly defined role, helping to avoid instances of confusion or redundancy.
To avoid this, tasks must be broken down into specific sub-tasks, and a clear communication infrastructure must be in place so agents can coordinate activities with one another.
For example, the AI agent for accounts payable must have a clear distinction in responsibilities from the AI agent for accounts receivable, and there may even be sub-agents in each of these categories to further specialize their roles, which must be clearly designated.
Ethical considerations and governance
The risk of delegating workflows to autonomous agents is that certain tasks or requests are handled according to certain rules and parameters, and won’t be considered with the same nuance or emotion that a human would provide to deliver a fair experience.
For instance, in a support context, a customer who expresses concern or confusion about a given issue may not receive the same level of care and empathy from a chatbot as they would from a human agent.
The future of multi-agent AI
As AI technology continues to advance, multi-agent systems will be able to handle more complex and sophisticated workflows independently from human workers.
On the back end, these systems will become more dynamic, capable of problem-solving, coordinating with other agents, and choosing the best model or solution based on their knowledge and experience.
It will also eventually be easier for non-technical employees to set up and manage these systems using natural language, with limited coding or programming knowledge required.
Possible applications of multi-agent AI systems already span industries, and it's likely that adoption will continue to grow as available tooling and platforms become more widely available.
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