What Are Multi-Agent Systems? A Beginner’s Guide for Startup Founders

Whether you are building an intelligent ecommerce platform or launching a new SaaS solution, you have implemented automation. But if you want to make your products smarter, you can introduce multi-agent systems (MAS) to streamline operations and get insights about how users interact with your product.

Multi-agent systems are applied in digital platforms more and more. You’re likely seeing a MAS in action whenever you encounter automated product listings updates, product recommendations, or inventory management.

Companies, like crewAI, DFKI, Palona AI, and many more, experiment with MAS and deliver top-notch systems to businesses.

If you plan to use MAS in your ecommerce or SaaS product, this post is for you. Learn from our experts how MAS work, what their benefits and use cases are, and how you can build one.

A brief definition of multi-agent systems

A multi-agent system (MAS) includes multiple artificial intelligence (AI) agents that collaborate to fulfill tasks from a user or another system. Each agent has unique characteristics in these distributed artificial intelligence systems, but all agents collaborate to reach common goals as a whole. MAS help us solve complex tasks and can include hundreds, even thousands, of agents.

To clarify, an agent is a software program or system that can perform tasks autonomously. It operates on behalf of a user or another system and can adjust its workflow to achieve specific goals. Thanks to large language models, which are central to these autonomous agents, they can process natural language, understand and respond to user input. Additionally, agents can utilize various tools, such as external data sources, web searches, and APIs, to complete tasks effectively.

In contrast to single-agent systems, MAS are based on agents’ cooperation. Single agents can call other agents as tools, but in this case, they do not share goals, memory, or a plan of action.

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How multi-agent systems work

To understand how you can use a MAS for your startup, first let’s break down the crucial components that make MAS work. We list them below:

• Each agent in a MAS serves with a specific role. Agents work independently, yet collaboratively based on inputs from their environment.
• First, agents collect data from various sources, including databases, sensors, APIs, or user interactions. For example, agents in MAS collect data on user preferences, product availability, or traffic flow, depending on your app’s designation.
• Next, agents in MAS make decisions. They process information using built-in logic, rules, LLMS or machine learning models and decide how to act. For example, a recommendation agent might decide which products to suggest based on browsing behavior. This step also involves agents’ communication as they exchange information and coordinate actions.
• Once decisions are made, agents take action. These may include updating a product listing, triggering a notification, or rerouting a delivery. Agents’ learning components, if present, allow them to adapt over time based on feedback or results.
• Agents act independently, but together they create smart, efficient outcomes that no single agent was programmed to produce on its own. This emergent intelligence, coming from the collaborative actions of individual agents, is what makes MAS effective to solve dynamic, decentralized problems.

Benefits of multi-agent systems for startups

Being sophisticated in their essence, MAS simplify workflows and bring other benefits to startups thanks to their intelligence. Here are the advantages that you get from MAS as a startup:

• Workflow automation and effectiveness. Startups can automate tasks with MAS and boost productivity. AI agents work together, speeding up processes and reducing the necessity of allocating more resources.
• Cost reduction. Automation of tasks, intelligent resource allocation, and modularity of MAS can lead to a significant reduction in costs.
• Smarter, real-time decision-making. MAS continuously gather and interpret data from various sources in real-time and provide actionable insights.
• Modular growth and adaptability. Thanks to the MAS modular structure and agent autonomy, startups can add and refine agents at scale. Thus, there is no need to redo the whole system, which is a significant benefit when a business evolves.
• Resilience to failures. Because MAS are modular and agents operate independently, one of them may fail without impacting the whole. This fault tolerance is priceless for startups when iterating rapidly under uncertain conditions.

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Real-world applications of multi-agent systems

Ecommerce

Personalization and dynamic pricing are the two primary applications of MAS in ecommerce.

In fact, personalized shopping assistants in ecommerce platforms enable intelligent, adaptive shopping experiences by simulating several roles. These include the following:

• Updating user preferences;
• Suggesting relevant products in real-time;
• Checking availability and delivery time;
• Handling inquiries, returns, and support.

Also, collaborative agents help make pricing more responsive and competitive through distributed intelligence. Thus, they can do the following:

• Track competitor pricing and demand fluctuations;
• Adjust prices in real-time based on data;
• Simulate buyer-seller bargaining to personalize offers or bulk discounts;
• Coordinate with marketing systems to trigger discounts or flash sales.

Examples of MAS in ecommerce

• Salesforce's Agentforce AI integrates autonomous agents across its platforms to enhance customer service, marketing, and productivity;
• Shopify integrates AI tools like Shopify Magic and Sidekick to assist merchants in sales;
• OpenAI's Operator is an AI agent designed to handle web-based tasks. Early adopters include eBay, Etsy, and Instacart.

Healthcare

In this realm, MAS enables intelligent, real-time observation of patients' health. They collect vital signs, such as heart rate and blood pressure, from wearable or in-hospital devices, interpret the data, and trigger emergency alerts.

Also, they analyze data from EHR records and suggest possible diagnoses based on probabilistic reasoning or rule-based logic. Moreover, MAS also assist in patients’ home monitoring.

Examples of MAS in healthcare

• Doctronic uses a system of AI agents to assess patient symptoms and generate potential diagnoses;
• Cera Care uses AI algorithms to predict health risks, such as hospitalizations and falls, enabling preventative measures.

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Finance

In algorithmic trading, MAS analyzes market data, triggers buy/sell decisions, optimizes trade execution, monitors exposures, and reallocates assets based on thresholds or market volatility. Also, MAS provide comprehensive control of transactions and prevent fraud by the following:

• Monitoring individual transactions in real-time for suspicious patterns;
• Tracking the typical behavior of account holders;
• Triggering responses when anomalies are detected.

MAS can freeze accounts, flag transactions, and provide evidence to analysts.

Examples of MAS in finance

• Feedzai prevents fraud with top-notch GenAI agents;
• Adaptive Modeler by Altreva creates market simulation models for virtual traders, which eventually help generate price forecasts and trading signals;
• ThetaRay provides AI-powered anti-money laundering solutions with transaction monitoring, customer screening, and customer risk assessment.

Transportation

MAS in transportation are used to coordinate individual vehicles within the transport infrastructure. MAS in vehicles power self-driving cars, coordinate multiple autonomous vehicles, manage interactions at junctions to avoid collisions, and provide updates about road conditions, hazards, or speed limits in real time.

Moreover, smart traffic lights equipped with MAS coordinate traffic flow in order to avoid congestions and delays. They also collect data from cameras and IoT sensors and aggregate data to reroute traffic, manage congestion, or respond to incidents.

Examples of MAS in transportation

• Transports Metropolitans de Barcelona equipped each bus with AI to prevent bunching;
• Electronic Road Pricing (ERP) system in Singapore brought a 15% drop in expressway traffic;
• Split Cycle Offset Optimization Technique (SCOOT) in Birmingham is helping with adaptive traffic control.

Building a multi-agent system: Key considerations

When building a MAS as part of AI development services, several considerations should be made to get the best product. Our experts below provide a step-by-step explanation of the process.

Define objectives: What the system should achieve

This is the foundational step that allows you to define what your MAS will achieve, both on the global level (the system) and the level of each agent.

With defined objectives, agents will operate independently while still achieving the shared purpose. Otherwise, agents might fail to collaborate effectively and waste resources while pursuing misaligned goals.

At Codica, we define MAS and agents’ objectives during product discovery sessions. In this stage, we define the following, as exemplified with possible options for an ecommerce personalization MAS:

• Global system goal: Deliver tailored product recommendations to users based on their behavior, preferences, and context;
• Breaking the goal into subgoals: Track browsing history, manage product attributes, collect user feedback on recommendations, and more;
• Specify performance metrics: For instance, click-throurgh rates, conversion rates, repeat purchase rate, response time, recommendation freshness, and more;
• Include constraints and priorities: How many recommendations and users a system can handle in X milliseconds or seconds.

The project discovery phase ensures that you get a clear starting point that will provide correct system behavior as a result.

Design agent architecture: Roles, behaviors, and interactions

The purpose of this step is to outline what each agent does, how each agent makes decisions, and how it communicates and coordinates with others based on communication protocols and coordination models. This stage involves the following steps:

• Define what each agent is responsible for as part of system;
• Specify agent behavior and how they act or react in each scenario;
• Choose agent architecture type regarding the internal structure of agents;
• Define interaction and coordination to help agents talk and work;
• Outline the knowledge and data access for each agent;
• Determine failure handling and autonomy boundaries.

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Choose communication protocols: Establish a method for sharing information between agents

This intricate process requires several tools and protocols that ensure communication between agents goes smoothly. Here is what is necessary to achieve that:

• Define the communication model. There are three models you can choose from: direct, centralized, and distributed. Direct is simple and low-latency, centralized is great for task coordination, and distributed is decoupled and scalable.
• Choose the agent communication language or format. Different formats serve different purposes. For example, FIPA-ACL, custom JSON/XML formats, Protocol Buffer are simple, easy to implement, and efficient.
• Implement the communication layer. Depending on your tech stack and architecture, you can use different tools and frameworks that align with your messaging language. For instance, JADE (Java Agent Development Framework) supports FIPA-ACL.
• Define communication protocols (behavioral rules). This step helps you determine the logic for structured agent conversations. Different protocols are used depending on the purpose of MAS. For example, in the Contract Net Protocol (CNP), a manager suggests a task to several agents.

Implement coordination mechanisms: Ensure agents’ harmonious work

Coordination in MAS ensures that agents collaborate and synchronize rather than act in isolation. Aligned coordination helps agents to achieve shared goals, avoid conflict, and optimize performance.

First, choose a coordination model that suits your MAS. Agents can get tasks from manager, bid on tasks, get guidance from one agent, and more. Then, embed coordination logic in agent behavior as they must exchange messages, detect and resolve conflicts, and break down shared goals into parts through agent negotiation.

Ensure agents’ behavior for meeting deadlines, fallback strategies, and prioritizing tasks. Agents must understand whether to drop or escalate tasks, take a task if another agent fails, and understand which agent’s decision takes precedence.

Test and iterate: Check the system and refine agent behaviors

It is not a final step. It is a continuous process that ensures the stable work of your MAS under real-world and edge-case conditions. Also, you ensure that you can adjust MAS based on performance, coordination issues, or evolving business goals.

Thorough quality assurance services start with setting a simulation environment. Next, perform tests: unit tests for one agent, interaction tests to check behavior between two or more agents, and full system tests to check the MAS. Then, make sure that you can track agents’ work and tweak the system as needed, based on evaluation metrics.

Also, make sure to create a staging MAS cluster where you will test updates. This agent-based simulation will help you model real-world scenarios without rolling out the MAS, risking customer experience.

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Challenges in implementing multi-agent systems

Multi-agent systems (MAS) offer flexibility, autonomy, and scalability. Still, there are challenges you encounter while achieving the payoff of MAS implementation:

• Complex agent coordination. If poorly coordinated, multiple autonomous agents’ work can lead to conflicts, delays, or inefficiencies. Ensure that agents negotiate, synchronize, or delegate tasks based on well-designed communication protocols and conflict-resolution mechanisms.
• Designing and balancing MAS. Design your agents with well-defined roles, behavior, and reasoning. Also, ensure they have balanced autonomy for the best results of MAS.
• Communication overhead. Frequent inter-agent messaging, especially in real-time systems, can be overwhelming for networks and reduce system responsiveness. Balancing message frequency and decision accuracy is critical to performance.
• Testing and debugging difficulty. Unlike traditional systems, MAS presents emerging behavior, making it challenging to predict outcomes at a system level. Testing and debugging must include agent-based modeling of numerous interaction scenarios, which increases complexity and cost.
• Security and trust. Ensure that agents work securely, especially with different users or systems. Tampering, impersonation and malicious agents are potential risks that may disrupt your MAS operations.

Future trends in multi-agent systems

The AI world is rapidly evolving, and MAS is leading the game. As MAS are widely adopted across industries, several trends shape their future:

• Integration with Large Language Models (LLMs) will take the next step. When integrated with LLMs, MAS can perform sophisticated and dynamic tasks, understand natural language, and make context-based decisions. Thus, tackling unstructured content becomes more nuanced.
• MAS systems will become more complex, dealing with a greater number of tasks. They are organized in layers, like in hierarchical MAS, and produce complex, adaptive behavior following local rules, like in MAS combined with swarm intelligence.
• Secure and ethical MAS are the focus. Specialists work on making agent-based software engineering transparent, verified, and compliant according to standards.
• Scalable, cloud-native architectures will evolve. Cloud-native MAS will allow developers to spin up, scale, and orchestrate thousands of agents as microservices. This will bring a massive amount of decentralized systems and apps in fintech, healthcare, and autonomous systems.
• Human-agent collaboration brings efficiency. MAS not only tackles tasks, but they are also great helpers who work alongside humans. While taking tasks, they provide alerts, leaving the final decisions to us. Such MAS systems learn from human input and make their messages understandable to people.

Wrapping up

Multi-agent systems provide versatile help for startups. Bringing effectiveness with intelligent coordination, they help you automate tasks, gain insights, and improve your products and operations. Thanks to the parallel and autonomous operation of agents in MAS, they perform tasks in milliseconds to seconds.

At Codica, we’ve seen startups implementing MAS effectively. If you need MAS for your product or operations, we are eager to build it. Our custom software development services are tailored to each business’s unique needs and goals for the best outcomes. Check out our portfolio for more successful projects.

If you have a project in mind, contact us. Our experts will guide you through the details of MAS implementation and will provide you with a free quote.