We’ve all experienced single AI models working independently, whether it's a bot answering questions or an algorithm making recommendations. But what happens when a single AI system isn't enough for a business?
When your call center deals with complex, multi-step customer queries, or you need a broader perspective on stock marketing trading and risk management, single AI agents can fall short. It’s no surprise that AI agent applications in customer service and virtual assistants are expected to account for approximately 78.65% of the market share by 2035.
If you’re considering multi-agent AI systems for your business, it’s only natural to wonder: what is a multi-agent system? What are some multi-agent system examples, and applications? We’ll cover all this and more in this blog post.
Let’s get started.
What is a multi-agent system?
A multi-agent system (MAS) is a network of single AI agents that work together to achieve business goals.
For instance, if your online store deploys a single AI chatbot, it can handle answering FAQs, giving order status updates, etc. But if a customer raises multiple queries to track an order and request a refund, a single AI agent struggles to juggle both.
In this case, introducing multiple AI agents to assist customers with refund requests, return policies, sentiment analysis, and more, reduces response time and increases customer satisfaction. In fact, 57.7% of AI-driven customer support agents now operate using a MAS to handle complex interactions.
It operates on three core principles:
1. Autonomy
Each agent in a MAS operates independently, without waiting for a response or instruction from a central system. For instance, in an AI-driven customer support system, a refund policy handling agent retrieves the policies without waiting for instructions from another agent.
2. Collaboration
While agents in an MAS work independently, all these distributed AI systems communicate and coordinate to solve problems. For queries like, "Where's my order, I want to cancel it." The ‘order tracking’ agent and the ‘cancel orders’ agent collaborate to provide a response that addresses both queries.
3. Distributed control
Multi-AI agents distribute tasks that self-organize based on real-time inputs. For example, Google Maps doesn’t have a central system dictating traffic patterns, road closures, and delays.
Now that we’ve covered what MAS is, let’s break down how it operates in real-world scenarios.
How multi-agent systems work
Multi-agent systems work as a well-coordinated team, where each agent has a role, and follows certain rules. Here's a quick breakdown of the key components:
- Agents: These are independent decision-makers. In customer support, one might handle FAQs, another may tackle refund requests, and so on.
- Environment: This is a space where agents operate, like a website, within transaction data, or customer interactions.
- Perception and data collection: This is when agents sense surroundings (customer tone, transaction data).
- Communication protocols: Agents need structured ways to talk to each other. For example, how drivers (agents) communicate in ride-sharing apps or when riders request a ride.
- Decision-making mechanisms: Each agent must then decide how to act based on the situation. For instance, sending a cancellation confirmation or rerouting calls.
- Coordination strategies: Agents must collaborate efficiently, just as robots coordinate to pick and ship items without colliding.
- Learning and adaptation: Smart agents learn from experience. If multiple customers have order or refund-related inquiries, AI-driven agent-based modeling analyzes these patterns to help you make informed decisions.
Control architecture: Multi-agent systems can be centralized, where a single entity directs all agents (like an air traffic control tower managing flights), or decentralized, where agents operate independently while coordinating with each other (like multiple drones adjusting their paths in real-time).
Real-world applications of multi-agent systems
Multi-agent systems are working behind the scenes in many industries. They help manage traffic, improve customer support, and even assist in public health efforts.
Here’s how they make a difference in the real world.
Manage transportation systems
Traffic congestion during peak hours or roadblocks during accidents can bring an entire city to a standstill. Multi-AI agents tackle this with real-time voice updates and intelligent coordination.
In Pittsburgh, the deployment of the Scalable Urban Traffic Control (SURTRAC) system led to a 25% reduction in travel times and a 40% decrease in vehicle wait times at intersections. They used multi-AI agents that communicate to adapt traffic signals based on real-time conditions, effectively easing congestion and improving traffic flow.
So if a major highway causes a traffic jam, one AI agent can instantly analyze traffic while another delivers voice updates via navigation apps, guiding routers to take alternative routes.
Prediction and prevention in healthcare and public health
Missed appointments and no-shows in the U.S. healthcare system are leading causes of healthcare inefficiency. Even charging full fees for no-shows or missed appointments will cost you patient frustration and retention issues.
MAS bridges this gap by improving patient engagement and ensuring timely care.
For instance, Plivo's smart healthcare interactive voice response (IVR) system helps providers automate patient communication and optimize call handling.
When a patient calls a clinic, Plivo’s smart IVR can screen symptoms using AI-driven voice interactions and direct them to the appropriate specialist based on urgency. Instead of long wait times or misrouted calls, the system efficiently prioritizes critical cases while handling routine inquiries without human intervention.
Automate customer support
93% of customers are more likely to stay loyal to businesses with great service but achieving that at scale can be a challenge. In fact, long wait times and misrouted calls frustrate customers and overwhelm support teams.
AI-powered multi-voice agents improve customer service efficiency since businesses automate routine queries, ensuring customers get instant responses without waiting on hold. Smart call routing directs calls to the right department, reducing frustration, while speech recognition and customer relationship management (CRM) integration can send personalized follow-ups — whether it’s confirming appointments or providing order updates.
Streamline routine financial tasks
Managing routine financial tasks shouldn't feel like a constant back-and-forth. Multi-AI systems work together to streamline everything from payment confirmations to loan processing.
For instance, if you use one of Plivo's AI-powered voice agents to automate reminders, others can take care of fraud alerts, and give account updates in real-time. Depending on your customer base and their queries, you can customize these AI agents to provide contextual responses.
Connect components of supply chain management
Supply chain inefficiencies cost businesses an average of $1.3 trillion annually, representing nearly 10% of the global gross domestic product (GDP).
The power of MAS is that it makes every part of the supply chain, from warehouses to delivery trucks, streamlined. You can use one agent for supply management, another for inventory management, and one for demand forecasting.
It’s also convenient to send voice notifications for order confirmation, delays, and even emergency alerts in case of weather delays, stock shortages, etc.
Benefits of multi-agent systems
Multi-agent systems bring a lot to the table, making complex processes more efficient and intelligent. Let’s take a look at what makes them so useful.
Flexible
AI is already making 81% of companies respond faster to market shifts. MAS adds to this benefit as it adapts to changing business needs and automates processes like logistics, customer support, and compliance.
Scalable
Multi-agent systems are inherently scalable. As the complexity or scope of a problem increases, additional agents can be introduced to handle new tasks or responsibilities. This scalability makes these systems suitable for an array of applications, and dynamic environments.
Businesses can handle fluctuating demand without adding human resources, which reduces operational costs.
Robust
Multi-agent systems improve fault tolerance so if one AI component fails, another takes over. This ensures continuity, especially critical for industries like healthcare and finance.
Efficient
One of the primary benefits of multi-agent systems is the separate modeling-based AI agents. As each agent focuses on a singular task, agents can perform more efficiently and reduce manual errors.
Make multi-agent systems smarter with Plivo’s AI-powered voice agents
Autonomous AI agents often hit a dead-end as businesses scale, frustrating customers with repetitive, or worse, generic responses. Plivo's AI-powered voice agents understand your existing business gaps and fill those with adaptable solutions across industries.
These agents assess customer intent in real time so that callers don't waste time navigating endless IVR menus. Moreover, they sense customer sentiment, redirect them to the right department, and help gauge honest feedback to make informed decisions across tech support, appointment scheduling, or billing.
Plivo also offers data-driven analytics, where you can gain business insights and optimize customer interactions. Whether you're in healthcare, finance, or retail, Plivo's AI-powered voice agents ensure every conversation is efficient, personalized, and frustration-free.
You can use AI agents to speak your brand's language, and personality, get a 360-degree customer snapshot to make context-aware conversations, or resolve queries by utilizing internal business documents like FAQs, SOPs, and blog posts.
Contact us to deploy AI voice agents using Plivo, convert leads, resolve queries instantly, and send timely promotions in over 220 countries and territories.
