Most of us know that self-driving cars rely on artificial intelligence. We may not realize that self-driving cars rely on multi-agent AI systems to navigate traffic safely. A self-driving car doesn't just use AI to analyze its environment. It also communicates with other nearby vehicles, traffic signals, emergency responders, and even cloud-based data repositories to make real-time driving decisions. As a result, multi-agent AI systems can help solve complex challenges through seamless collaboration and intelligent decision-making. This article will offer valuable insights to help you achieve similar goals. In particular, you'll learn how to build a multi-agent AI system that efficiently solves complex challenges through seamless collaboration and intelligent decision-making.
Lamatic's generative AI tech stack can help you achieve your objective of building a multi-agent AI system. By streamlining the development process, our solution will enable your multi-agent AI system to communicate and collaborate effectively, ensuring optimal performance, even in the most complex scenarios.
What is a Multi-Agent AI System?
Multi-agent AI refers to systems where multiple autonomous agents interact, either cooperatively or competitively, to solve problems or achieve goals. These agents can represent different processes, applications, or even entities and work in a coordinated manner. Multi-agent systems (MAS) consist of multiple artificial intelligence (AI) agents working collectively to perform tasks on behalf of a user or another system. Each agent within a MAS has individual properties, and all agents behave collaboratively to achieve the desired global properties. Multi-agent systems are valuable in completing large-scale, complex tasks encompassing hundreds, if not thousands, of agents.
How Do Multi-Agent AI Systems Work?
Central to this idea are artificial intelligence (AI) agents. An AI agent refers to a system or program capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and using available tools. At the core of AI agents are large language models (LLMs).
These intelligent agents leverage LLMs’ advanced natural language processing techniques to comprehend and respond to user inputs. Agents work through problems step-by-step and determine when to call on external tools.
AI Agents vs. Traditional LLMs: Tools, Decision-Making, and Adaptive Learning
What differentiates AI agents from traditional LLMs is the use of tools and the ability to design a plan of action. The tools available to an agent can include:
- External datasets
- Web searches
- Application programming interfaces (APIs)
Similarly to human decision-making, AI agents can also update their memory as they acquire new information. The information-sharing, tool usage, and adaptive learning allow AI agents to be more general-purpose than traditional LLMs.
What’s the Difference Between Single-Agent and Multi-Agent Systems?
Single-agent intelligent systems engage with their environment to:
- Autonomously plan
- Call tools
- Produce responses
The tools made available to an agent provide information that is otherwise unavailable to the agent. As previously described, this information can be a database acquired through an API or another agent.
Single vs. Multi-Agent Systems: Communication, Cooperation, and Shared Goals
There is a distinction here between single and multi-agent systems. When calling another agent as a tool, that secondary agent is part of the original agent’s environmental stimuli. That information is acquired, and no further cooperation takes place. Multi-agent systems differ by involving all agents within the environment to model each other’s:
- Goals
- Memories,
- Plans of action
Communication between agents can be direct or indirect by altering the shared environment.
How Do Multi-Agent AI Systems Behave?
Each entity within a multi-agent system is an autonomous agent to some extent. This autonomy is typically seen by the agent’s:
- Planning
- Tool calling
- General reasoning
In a multi-agent system, agents remain autonomous but cooperate and coordinate in agent structures. Agent communication and distributed problem-solving are key to solving complex problems. This type of agent interaction can be described as multi-agent reinforcement learning.
Experience Sharing in AI: Enhancing Learning through Sensory Data and Episodic Information
The information shared through this form of learning can include instantaneous information acquired through sensors or actions. An agent’s experiences in the form of episodic information can be shared.
These episodes can be sequences of sensations, actions, and learned policies. Agents can share their experiences in real-time to prevent other agents from repetitively learning the same policies.
How Do Multi-Agent AI Systems Perform?
Individual agents are powerful on their own. They can create subtasks, use tools, and learn through their interactions. The collective behavior of multi-agent systems increases the potential for:
- Accuracy
- Adaptability
- Scalability
Multi-agent systems outperform single-agent systems due to the larger pool of shared resources, optimization, and automation. Instead of multiple agents learning the same policies, one can share learned experiences to optimize time complexity and efficiency.
What Are the Architectures of Multi-Agent Systems?
Multi-agent systems can operate under various architectures. In centralized networks, a central unit contains the global knowledge base, connects the agents, and oversees their information. A strength of this structure is the ease of communication between agents and uniform knowledge. A weakness of the centrality is the dependence on the central unit; if it fails, the entire system of agents fails.
Decentralized Networks: Benefits and Challenges of Agent Coordination Without a Central Unit
Agents in decentralized networks share information with their neighboring agents instead of a global knowledge base. Some benefits of decentralized networks are robustness and modularity.
The failure of one agent does not cause the overall system to fail since there is no central unit. One challenge of decentralized agents is coordinating their behavior to benefit other cooperating agents.
What are the Structures of Multi-Agent Systems?
There are also many ways of organizing agents within a multi-agent system, including:
Hierarchical Structure
A hierarchical structure is tree-like and contains agents with varying levels of autonomy. Within a simple hierarchical structure, one agent can have decision-making authority, while in a uniform hierarchical structure, the responsibility can be distributed among multiple agents.
Holonic Structure
Within this architecture type, agents are grouped into holarchies. A holon is an entity that cannot operate without its components. For instance, the human body is a holon because it cannot function without working organs.
Similarly, in holonic multi-agent systems, the leading agent can have multiple sub-agents while appearing to be a singular entity. These sub-agents can also play roles in other holons. These hierarchical structures are self-organized and created to achieve a goal through the collaboration of the sub-agents.
Coalition Structure
Coalitions are helpful in cases of underperforming singular agents in a group. In these situations, agents temporarily unite to boost utility or performance. Once the desired performance is reached, the coalitions are disbursed. Maintaining these coalitions in dynamic environments can become difficult. Regrouping is often necessary to enhance performance.
Teams
Teams are similar in structure to coalitions. In teams, agents cooperate to improve the group’s performance. Unlike in coalitions, agents in teams do not work independently. They are much more dependent on one another, and their structure is more hierarchical than coalitions.
What are the Behaviors of Multi-Agent Systems?
The behaviors of agents within a multi-agent system often reflect behaviors occurring in nature. The following agent behaviors can apply to both multi-software and multi-robot agents.
- Flocking: The collective behavior seen in multi-agent systems can resemble that of birds, fish, and humans. In these systems, agents share an objective and require some organization to coordinate their behavior. Flocking pertains to directional synchronization, and these heuristics can describe the structure of these flocks:
- Separation: attempt to avoid collision with nearby agents.
- Alignment: attempt to match the velocity of nearby agents.
- Cohesion: attempt to remain close to other agents.
In the context of software agents, this coordination is crucial for multi-agent systems managing transportation networks such as railroad systems.Swarming: The spatial positioning of agents in a multi-agent system can be compared to the swarming in nature. For instance, birds fly in sync by adjusting to neighboring birds. From a technical perspective, swarming is the emergent self-organization and aggregation among software agents with decentralized control.
A benefit of swarming is that one operator can be trained to manage a swarm of agents. This method is less computationally expensive and more reliable than training an operator for each agent.
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Why Use Multi-Agent AI Frameworks?
Multi-agent AI systems can perform various tasks to help automate business operations, especially in IT-driven environments.
For example, these systems can help with:
- Call analytics
- Robotic process automation
- Finance
- Marketing
- Sales
- Technology
We discuss key areas where multi-agent systems can improve business efficiency and effectiveness.
Call and Other Analytics
Multi-agent systems can analyze calls and other business communications to help improve customer interactions. For example, these systems can analyze participants’ video calls to get insights into people’s:
- Sentiments
- Intent
- Satisfaction levels
Multi-agent systems are excellent at analyzing and reporting users’:
- Intents
- Demographics
- Interactions
Their analytics/reporting capabilities help businesses to target or market to the required customers. Multi-agent systems can also automatically categorize calls based on participants’ network bandwidth and strength for efficient handling.
Marketplace Listening
Multi-agent systems can monitor and analyze customer sentiments across a marketplace app’s channels. Businesses can use the insights generated by this process to target specific customers and improve their overall experience.
For example, if a multi-agent system detects negative feedback about a product, the business can quickly respond to mitigate any damage.
Polls and Review Analytics
Multi-agent systems can automate analyzing customer feedback, reviews, and polls to gain insights and improve the customer experience. Rather than relying on human workers to sift through this data, multi-agent systems can quickly detect trends in the information and highlight areas that need attention.
Travel and Expense Management
Multi-agent systems can help automate travel and expense management. They can track travel, automate expense reporting, and handle approval processes. This can help businesses improve compliance with travel policies and ensure employees are reimbursed quickly.
Conversational Banking
Multi-agent systems can enable customers to perform banking tasks via AI-powered chat or voice assistants. Their ability to process information quickly and accurately means they can handle complex requests and improve the customer experience.
Generic AI Support Chatbot
Multi-agent systems can help automate customer service operations. For example, support agents powered by AI can troubleshoot, fix customer complaints, and delegate complex tasks to other agents. This not only improves response times but also enhances the overall customer experience.
Finance
Financial multi-agent systems can be used in industries to predict economic, stock, and market trends and provide tangible and viable investment recommendations. For example, they can analyze massive datasets of historical financial data to improve their forecasting accuracy.
Marketing
Industries’ marketing teams can use AI agents to create personalized content and campaign copies for different target audiences, resulting in high conversion rates. Multi-agent systems can analyze vast amounts of data to determine what types of content appeal to specific demographics and automatically generate relevant copy for marketers.
Sales
Sales teams can leverage the capabilities of multi-agent systems to analyze a system’s customer interaction patterns. This helps enterprise sales teams focus on converting leads rather than wasting time on unqualified prospects.
Technology
In technology industries, AI coding agents assist developers and engineers in working efficiently and improving their work through:
- Faster code completion and generation
- Automation
- Testing
- Error fixing
Advantages of Multi-Agent Systems
Several characteristics of multi-agent systems provide advantages, including:
Flexibility
Multi-agent systems can adjust to varying environments by adding, removing, or adapting agents.
Scalability
The cooperation of several agents allows for a more excellent pool of shared information. This collaboration enables multi-agent systems to solve more complex problems and tasks than single-agent systems.
Domain specialization
Single-agent systems require one agent to perform tasks in various domains, whereas each agent in a multi-agent system can have specific domain expertise.
Greater performance
Multi-agent frameworks tend to outperform singular agents. This is because the more action plans an agent has, the more learning and reflection occurs. An AI agent incorporating knowledge and feedback from other AI agents with specialties in related areas can be helpful for information synthesis.
This backend collaboration of AI agents and the ability to fill information gaps are unique to agentic frameworks, making them a powerful tool and a meaningful advancement in artificial intelligence.
Limitations of AI Agents
Although several frameworks exist for building agentic assistants, only limited agent-based applications and systems, such as Cursor and Windsurf, are in production for AI-assisted coding. The following limitations explain why a small number of these applications are in production.
- Quality: These agents may lack the ability to deliver high-quality results in various scenarios.
- Cost of building: Developing, maintaining, and scaling AI agents for production environments can be costly. Training requires both computational costs and AI experts.
- High latency: The time taken for AI agents to process user prompts and deliver responses can hinder user experience in real-time services like live customer interactions, ordering, and issue reporting.
- Safety Issues: Putting these agents into production may have ethical and security concerns for enterprise use cases.
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How to Build a Multi-Agent AI System
Define the Purpose and Scope: What Are You Solving?
Clearly defining the purpose and scope of your multi-agent AI system will help you understand the system's objectives and how it will function before implementing it. Start by answering a few questions:
- What is your multi-agent AI solving in this context?
- Why does the problem require multiple agents instead of a centralized AI system?
- What roles will individual agents play?
For example, in disaster response systems, you might assign roles such as surveillance (drones), logistics (ground vehicles), and communication (control hubs). These agents work together to provide comprehensive solutions that no AI can handle efficiently.
Remember that multi-agent AI solutions are ideal for problems requiring distributed intelligence, such as dynamic environments where centralized systems face scalability or latency challenges. They allow individual agents to focus on their specialties while contributing to a collective goal.
Choose the Right Multi-Agent AI Framework: What’s Your Building Material?
The framework you select will dictate how your agents:
- Interact
- Coordinate
- Execute tasks
A framework acts as a blueprint, offering pre-built modules for communication, lifecycle management, and deployment.
Popular Multi-Agent AI Frameworks: Lamatic, JADE, SPADE, and More
Popular frameworks for multi-agent AI include:
1. Lamatic
Lamatic offers a managed Generative AI Tech Stack. Our solution provides:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
Edge deployment via Cloudflare workers and Integrated Vector Database (Weaviate). Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt.
2. JADE
JADE (Java Agent Development Framework): A Java-based framework with built-in libraries for agent messaging, lifecycle management, and task distribution. It is widely used in academic and research contexts for its comprehensive features.
3. SPADE
SPADE (Smart Python Agent Development Environment): Designed for Python developers, SPADE simplifies agent creation with XMPP-based messaging protocols. It’s an excellent choice for real-time systems where agents need to exchange messages rapidly.
4. PyMAS
PyMAS: A Python-based framework designed for implementing lightweight and modular multi-agent systems. It’s highly customizable, making it an excellent choice for prototyping and experimentation.
5. ROS
ROS (Robot Operating System): Ideal for physical systems like robotics, ROS offers tools for sensor integration, task scheduling, and inter-agent communication.
6. GAMA
GAMA: Focused on large-scale simulations, GAMA allows developers to model spatially distributed multi-agent systems, such as urban planning and environmental simulations.
7. Phidata
Phidata is a Python-based framework for converting large language models into agents for AI products. It works with closed and open LLMs from prominent providers like:
- OpenAI
- Anthropic
- Cohere
- Ollama
- Together AI, and more.
With its database and vector store support, you can easily connect your AI system with:
- Postgres
- PgVector
- Pinecone
- LanceDb, etc.
Using Phidata, you can build essential AI agents and advanced ones using function calling, structured output, and fine-tuning. Phidata also provides free, Pro, and enterprise pricing. Check out their website to learn more and get started.
8. OpenAI Swarm
Swarm is an open-source, experimental agentic framework recently released by OpenAI. It is a lightweight multi-agent orchestration framework.
Note: When this article was written, Swarm was in the experimental phase. It can be used for development and educational purposes but should not be used in production. This phase may change, so check out its GitHub repo above for more updates.
Swarm uses Agents and handoffs as abstractions for agent orchestration and coordination. It is a lightweight framework that can be tested and managed efficiently. The agent component of Swarm can be equipped with tools, instructions, and other parameters to execute specific tasks.
Define the Multi-Agent AI System Architecture: How Will Agents Collaborate?
The architecture determines how agents collaborate and execute tasks. Choosing the right structure is essential for scalability and efficiency.
These are the different architectural types:
Centralized: A single supervisory agent oversees all operations, delegating tasks to sub-agents. An example is a logistics system in which a central agent allocates delivery routes to individual agents representing delivery vehicles.
Decentralized: Agents interact directly without a central authority, ideal for flexible and resilient systems. For example, in a smart grid, agents representing energy sources and consumers negotiate directly to balance energy distribution.
Hybrid: Combines centralized control with decentralized collaboration. An example is autonomous vehicle fleets, where a central agent monitors the overall network, but vehicles communicate locally for lane coordination.
Establish Communication Mechanisms: How Will Agents Talk to Each Other?
Agents in a multi-agent AI system must communicate effectively to share information, resolve conflicts, and coordinate actions.
This involves implementing robust communication protocols.
- Key concepts: Agent communication language (ACL): A standardized language defining message syntax and semantics, ensuring clarity in inter-agent communication.
- Shared state communication: Agents access a common data repository to exchange information. While efficient, this approach requires strict synchronization mechanisms to avoid data conflicts.
- Tool-call communication: Agents use APIs or external tools to perform specific tasks, such as fetching real-time data or executing computationally intensive operations.
- Messaging protocols: Protocols like XMPP (used in SPADE) or FIPA (Foundation for Intelligent Physical Agents, used in JADE) define how agents send, receive, and interpret messages.
To understand these concepts in action, in an e-commerce platform, one agent retrieves customer browsing data, another analyzes it to recommend products, and a third agent ensures secure payment processing.
Implement Multi-Agent AI Security Technology: How Will You Keep the System Safe?
Security is paramount in multi-agent AI systems, as agents often exchange sensitive data. Multi-agent AI security technology safeguards the system against breaches and ensures reliable communication.
Key security measures to implement for multi-agent AI systems:
- End-to-end encryption: Ensures messages are secure during transmission.
- Authentication protocols: Validates agent identities to prevent unauthorized access.
- Intrusion detection systems (IDS): Monitors agent activity for suspicious behavior.
- Fault tolerance: Ensures the system remains operational even if some agents fail.
For example, financial platforms like payment and investment tools require agents to comply with industry-standard encryption and authentication protocols to secure sensitive data.
Develop and Train Individual Agents: How Will You Prepare Agents for Their Roles?
Each agent in the system must be specialized for its role. This involves selecting appropriate machine learning models and datasets.
Training approaches:
- Reinforcement Learning: For agents requiring adaptability in dynamic environments, like robots navigating unknown terrains.
- Supervised learning: For agents performing specific tasks, such as text classification or image recognition.
- Natural language processing (NLP): For communication agents interacting with humans or other agents.
For example, in a disaster response system, drone agents trained with reinforcement learning algorithms can learn to navigate complex environments and identify areas requiring immediate attention.
Simulate, Test, and Optimize: How Will You Ensure the System Works Properly?
Before deployment, simulate the multi-agent system in a controlled environment to identify and address potential issues. Simulation tools like SPADE are great for testing communication-heavy systems. On the other hand, GAMA is ideal for large-scale simulations involving spatial data.
Test scenarios should include:
- Varying workloads to test scalability.
- Simulated failures to evaluate fault tolerance.
- Conflict scenarios to test resolution protocols.
Continuous Monitoring and Updates: How Will You Keep the System Running Smoothly?
Deploying a multi-agent system is not the end—it’s the beginning of continuous optimization. To ensure the smooth operation of workflows through multi-agent AIs, use monitoring tools to track agent performance, detect anomalies, and implement improvements.
Here are some key practices for continuously optimizing multi-agent AI systems:
- Regularly retrain agents with updated data.
- Refine communication protocols for efficiency.
- Expand system capabilities by adding new agents or roles.
Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic offers a managed Generative AI Tech Stack. Our solution provides:
- Managed GenAI Middleware
- Custom GenAI API (GraphQL)
- Low Code Agent Builder
- Automated GenAI Workflow (CI/CD)
- GenOps (DevOps for GenAI)
- Edge deployment via Cloudflare workers
- Integrated Vector Database (Weaviate)
Lamatic empowers teams to rapidly implement GenAI solutions without accruing tech debt. Our platform automates workflows and ensures production-grade deployment on edge, enabling fast, efficient GenAI integration for products needing swift AI capabilities. Start building GenAI apps for free today with our managed generative AI tech stack.
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