AI agents can help businesses tackle challenges by breaking down processes, analyzing data, and automating tasks. Multi-agent AI systems — groups of AI agents that work together to solve problems — boost these systems' capabilities even further. In this blog, we will explore the different types of multi AI agents and how they work so you can select the right ones to enhance your business operations.
At Lamatic, we combine a cutting-edge generative AI tech stack and multi-agent systems to help businesses automate processes, enhance decision-making, and boost productivity.
What is an AI Agent?
AI agents are autonomous software programs that perform tasks or make decisions independently. They achieve this by observing their environment, learning from it, and applying their intelligence to execute specific goals.
AI agents are distinct from traditional AI systems because they can operate independently of human intervention. They can:
- Monitor data streams
- Automate complex workflows
- Execute tasks without constant human supervision
Driven by Automation, NLP, and Personalization
As businesses seek more sophisticated automation solutions, these agents are gaining popularity: the AI agents market was valued at USD 3.86 billion in 2023 and is expected to grow rapidly, with a 45.1% annual increase projected from 2024 to 2030. This growth is fueled by:
- Rising demand for automation
- Advances in Natural Language Processing (NLP)
- A push for more personalized customer experiences
For example, healthcare organizations use AI agents to automate revenue cycle tasks like eligibility verification and claims management. At the same time, software development teams deploy agents to detect and diagnose system performance issues in their applications automatically.
What Technologies Power AI Agents?
What makes AI agents truly transformative isn’t just their features; how these capabilities work together to solve real business challenges. Let’s explore the key powers that set modern AI agents apart:
Large Language Models LLMs
The Foundation of Intelligence Large Language Models represents a quantum leap in AI capabilities, enabling agents to engage in meaningful, productive customer conversations. These advanced models process language like experienced human agents—grasping context, remembering details, and generating relevant responses.
The result? AI agents don’t just answer queries but provide insightful, nuanced dialogue that genuinely supports and engages customers.
Natural Language Processing NLP
The Art of Understanding: No more keyword matching and rigid commands. Today’s AI agents truly grasp human language in all its complexity. They understand the subtle differences between “I can’t log in” and “My password isn’t working,” picking up on context and intent like humans would.
When a customer asks a multi-part question or explains a complex problem, these agents follow along effortlessly, maintaining context throughout the conversation.
Machine Learning ML
Getting Smarter Every Day Unlike traditional systems that stay frozen in time without extensive human intervention, AI agents evolve with every interaction.
- They constantly learn from conversations.
- They analyze outcomes.
- They refine their responses.
When encountering new scenarios or unusual requests, they adapt their approach based on what’s worked before. It’s like having a team member who gets better at their job daily, learning from successes and missteps to deliver increasingly accurate and helpful responses.
Neural Networks: The Decision-Making Engine
Think of neural networks as the brain behind the operation. They process countless data points simultaneously, understanding how different pieces of information connect and influence each other. This sophisticated processing power enables AI agents to make nuanced decisions based on complex criteria, just like an experienced professional would.
Whether detecting patterns in customer behavior or solving multi-step problems, neural networks provide the intelligence that makes AI agents truly powerful.
How Do AI Agents Work?
AI agents range from simple task-specific programs to sophisticated systems that combine perception, reasoning, and action capabilities. The most advanced agents in use today present the full potential of this technology:
- Operating through a cycle of processing inputs
- Making decisions
- Executing actions while continuously updating their knowledge
Perception and Input Processing
AI agents begin by gathering and processing input from their environment. This could include parsing text commands, analyzing data streams, or receiving sensor data. The perception module converts raw inputs into a format the agent can understand and process.
For example, when a customer submits a support request, an AI agent could process the ticket by analyzing text content, user history, and metadata like priority level and timestamp.
Decision-Making and Planning
Using machine learning models like NLP, sentiment analysis, and classification algorithms, agents evaluate their inputs against their objectives. These models work together: NLP first processes and understands the input text; sentiment analysis evaluates its tone and intent, and classification algorithms determine the most appropriate response category.
This layered approach enables agents to process complex inputs and respond appropriately. They generate possible actions, assess potential outcomes, and select the most appropriate response based on their programming and current context.
For instance, when handling a support ticket, the AI agent could evaluate content and urgency to determine whether to hold it directly or escalate it to a human agent.
Knowledge Management
Agents maintain and use knowledge bases that contain:
- Domain-specific information
- Learned patterns
- Operational rules
Through Retrieval-Augmented Generation (RAG), agents can dynamically access and incorporate relevant information from their knowledge base when forming responses. In our support ticket example, the agent uses RAG to pull information from product documentation, past cases, and company policies to generate accurate, contextual solutions rather than relying solely on its training data.
Action Execution
Once a decision is made, agents execute actions through their output interfaces. This could involve generating text responses, updating databases, triggering workflows, or sending commands to other systems. The action module ensures the chosen response is formatted correctly and delivered. Continuing our example, the customer support agent might:
- Send automated troubleshooting steps.
- Route the ticket to a specialized department.
- Flag it for immediate human attention.
Learning and Adaptation
Advanced AI agents can improve performance over time through feedback loops and learning mechanisms. They analyze the outcomes of their actions, update their knowledge bases, and refine their decision-making processes based on success metrics and user feedback.
Using reinforcement learning techniques, these agents develop optimal policies by balancing exploration (trying new approaches) with exploitation (using proven successful strategies).
Continuous Learning Through Support Interactions
In the support scenario, the agent learns from resolution success rates and satisfaction scores to improve its future responses and routing decisions, treating each interaction as a learning opportunity to refine its decision-making model.
What Makes AI Agents Different?
What sets modern AI agents apart is their ability to operate with intelligence, adaptability, and, most importantly, autonomy. Here are the defining factors that make AI agents – whether text AI agents or voice AI agents – uniquely suited for complex enterprise needs:
They Get the Context
No more frustrating “I don’t understand” responses. AI agents grasp the full picture of conversations like a skilled human would. They remember previous interactions, understand nuanced requests, and can follow complex discussions without losing the thread. This contextual awareness enables them to:
- Navigate multi-step processes
- Interpret nuanced inquiries
- Respond based on real-time situational cues
They Learn and Adapt Like Pros
Every interaction makes them more intelligent. Using machine learning, they pick up on new situations and adjust to changing needs and emerging patterns to fine-tune their responses.
They Make Smart Decisions on Their Own
These aren’t your basic “if-this-then-that” bots. AI agents act more like experienced professionals who know the ropes. Using advanced neural networks, they can:
- Orchestrate entire workflows.
- Make judgment calls based on multiple factors.
- Route customer inquiries to the correct department.
- Spot potential issues before they become problems.
They Think Outside the Script
AI agents can handle curveballs, unlike old-school chatbots that stick to rigid scripts. Unexpected question? No problem. Unique situation? They’ll figure it out. This flexibility means they can tackle many tasks while keeping interactions natural and helpful, ultimately providing a richer customer experience.
They Scale Without Breaking a Sweat
Most CX teams and traditional systems start to crack under pressure, but AI agents thrive on it. They can jump from handling a handful of tasks to thousands without missing a beat or sacrificing quality.
While traditional systems force you to choose between speed and accuracy, AI agents deliver both. Each customer gets the same high-level support; each process runs with the same precision without additional costs.
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13 Main Types of AI Agents
Regarding artificial intelligence, the term "agent" can signify different things depending on the context. In the case of AI, an agent is a system that perceives its environment and takes actions to achieve specific goals. AI agents can be categorized in several ways, including their decision-making logic, structures, and functional roles. Let’s look at the types of AI agents, their actions, and how they differ.
7 AI Agent Types by Decision Logic
We can distinguish between different types of AI Agents based on the kind of decision logic they use to make choices and take actions. Decision logic defines how an agent processes information, evaluates options, and selects the best action. Differentiating AI agents by their decision logic highlights the range of capabilities and levels of autonomy they can achieve.
From simple rule-following systems to sophisticated, adaptive agents, understanding these categories helps businesses choose the right tools for specific tasks and workflows.
1. Simple Reflex Agents: The Most Basic AI Agents
Simple reflex agents are one of the most basic forms of artificial intelligence. These agents make decisions based solely on their current sensory input, responding immediately to environmental stimuli without needing memory or learning processes. Their behavior is governed by predefined condition-action rules, which specify how to react to particular inputs.Though they are limited in complexity, this straightforward approach makes them highly efficient and easy to implement, especially in environments where the range of possible actions is limited.
Key Components
- Sensors: Much like human senses, these gather environmental information. Sensors are basic input devices for a simple reflex agent that detect specific environmental conditions like temperature, light, or motion.
- Condition-action rules: These predefined rules determine how the agent responds to specific inputs. The logic is direct—if the agent detects a particular condition, it immediately performs a corresponding action.
- Actuators: These execute the decisions made by the agent, translating them into physical or digital responses that alter the environment in some way, such as activating a heating system or turning on lights.
Use Cases
Simple reflex agents are ideal for transparent, predictable environments with limited variables.
- Industrial safety sensors that immediately shut down machinery when detecting an obstruction in the work area.
- Automated sprinkler systems that activate based on smoke detection.
- Email auto-responders that send predefined messages based on specific keywords or sender addresses.
2. Model-Based Reflex Agents: A Step Up from Simple Agents
Model-based reflex agents are a more advanced form of intelligent agents designed to operate in partially observable environments. Unlike simple reflex agents, which react solely based on current sensory input, model-based agents maintain an internal representation, or model, of the world. This model tracks how the environment evolves, allowing the agent to infer unobserved aspects of the current state. While these agents don’t actually “remember” past states like more advanced agents do, they use their world model to make better decisions about the current state.
Key Components
- State tracker: Maintains information about the current state of the environment based on the world model and sensor history.
- World model: Contains two key types of knowledge:
- How the environment evolves independent of the agent
- How the agent’s actions affect the environment.
- Reasoning component: Uses the world model and current state to determine appropriate actions based on condition-action rules.
Use Cases
These agents are suitable for environments where the current state isn’t fully observable from sensor data alone.
- Smart home security systems: Using models of standard household activity patterns to distinguish between routine events and potential security threats.
- Quality control systems: Monitoring manufacturing processes by maintaining a model of normal operations to detect deviations.
- Network monitoring tools: Tracking network state and traffic patterns to identify potential issues or anomalies.
3. Goal-Based Agents: Intelligent Systems with Specific Objectives
Goal-based agents are designed to pursue specific objectives by considering the future consequences of their actions. Unlike reflex agents that act based on rules or world models, goal-based agents plan sequences of actions to achieve desired outcomes. They use search and planning algorithms to find action sequences that lead to their goals.
Key Components
- Goal state: A clear description of what the agent aims to achieve.
- Planning mechanism: The ability to search through possible sequences of actions that could lead to the goal.
- State evaluation: Methods to assess whether potential future states move closer to or further from the goal.
- Action selection: The process of choosing actions based on their predicted contribution toward reaching the goal.
- World model: Understanding how actions change the environment, used for planning.
Use Cases
Goal-based agents are suited for tasks with clear objectives and predictable action outcomes.
- Industrial robots: Following specific sequences to assemble products.
- Automated warehouse systems: Planning optimal paths to retrieve items.
- Smart heating systems: Plan temperature adjustments to efficiently reach desired comfort levels.
- Inventory management systems: Planning reorder schedules to maintain target stock levels.
- Task scheduling systems: Organizing sequences of operations to meet completion deadlines.
4. Learning Agents: Intelligent Systems That Improve Performance Over Time
A learning agent is an artificial intelligence system capable of improving its behavior over time by interacting with its environment and learning from its experiences. These agents modify their behavior based on feedback and experience, using various learning mechanisms to optimize performance.
Unlike more straightforward agent types, they can discover how to achieve their goals through experience rather than purely relying on pre-programmed knowledge.
Key Components
- Performance element: The component that selects external actions, similar to the decision-making modules in more straightforward agents.
- Critic: Provides feedback on the agent’s performance by evaluating outcomes against standards, often using a reward or performance metric.
- Learning element: Uses critic’s feedback to improve the performance element, determining how to modify behavior to do better in the future.
- Problem generator: Suggests exploratory actions that lead to new experiences and better future decisions.
Use Cases
Learning agents are suited for environments where optimal behavior isn’t known in advance and must be learned through experience.
- Industrial process control: Learning optimal settings for manufacturing processes through trial and error.
- Energy management systems: Learning patterns of usage to optimize resource consumption.
- Customer service chatbots: Improving response accuracy based on interaction outcomes.
- Quality control systems: Learning to identify defects more accurately over time.
5. Utility-Based Agents: AI That Aims to Maximize Goal Achievement
A utility-based agent makes decisions by evaluating the potential outcomes of its actions and choosing the one that maximizes overall utility. Unlike goal-based agents that aim for specific states, utility-based agents can handle tradeoffs between competing goals by assigning numerical values to different outcomes.
Key Components
- Utility function: A mathematical function that maps states to numerical values, representing the desirability of each state.
- State evaluation: Methods to assess current and potential future states regarding their utility.
- Decision mechanism: Processes for selecting actions that are expected to maximize utility.
- Environment model: Understanding of how actions affect the environment and resulting utilities.
Use Cases
Utility-based agents are suited for scenarios requiring a balance between multiple competing objectives.
- Resource allocation systems: Balancing machine usage, energy consumption, and production goals.
- Smart building management: Optimizing between comfort, energy efficiency, and maintenance costs.
- Scheduling systems: Balancing task priorities, deadlines, and resource constraints.
6. Hierarchical Agents: Complex AI with Layered Architectures
Hierarchical agents are structured in a tiered system, where higher-level agents manage and direct the actions of lower-level agents. This architecture breaks down complex tasks into manageable subtasks, allowing for more organized control and decision-making.
Key Components
- Task decomposition: Break down complex tasks into simpler subtasks that lower-level agents can manage.
- Command hierarchy: Defines how control and information flow between different levels of agents.
- Coordination mechanisms: Ensures different levels of agents work together coherently.
- Goal delegation: Translates high-level objectives into specific tasks for lower-level agents.
Use Cases
Hierarchical agents are best suited for systems with clear task hierarchies and well-defined subtasks.
- Manufacturing control systems: Coordinating different stages of production processes.
- Building automation: Managing basic systems like HVAC and lighting through layered control.
- Robotic task planning: Breaking down simple robotic tasks into basic movements and actions.
7. Multi-Agent Systems: Complex AI with Layered Architectures
A multi-agent system involves multiple autonomous agents interacting within a shared environment, working independently or cooperatively to achieve individual or collective goals. While often confused with more advanced AI systems, traditional MAS focuses on simple agents interacting through basic protocols and rules.
Types of Multi-agent Systems
- Cooperative systems: Agents share information and resources to achieve common goals—for example, multiple robots work together on basic assembly tasks.
- Competitive systems: Agents compete for resources following defined rules, like multiple bidding agents in a simple auction system.
- Mixed systems: Combines both cooperative and competitive behaviors, such as agents sharing some information while competing for limited resources.
Key Components
- Communication protocols: Define how agents exchange information.
- Interaction rules: Specify how agents interact and what actions are permitted.
- Resource management: Methods for handling shared resources between agents.
- Coordination mechanisms: Systems for organizing agent activities and preventing conflicts.
Use Cases
- MAS best suits scenarios with clear interaction rules and relatively simple agent behaviors.
- Warehouse management: Multiple robots coordinate to move and sort items.
- Basic manufacturing: Coordinating simple assembly tasks between multiple machines. - Resource allocation: Managing shared resources like processing time or storage space.
6 AI Agent Types by Functional Roles
We can also distinguish AI Agents by their functional roles within businesses, from supporting customers to assisting with data processing. Here are five primary types, and the first two (customer and employee agents) could be classified more broadly as conversational agents.
8. Customer Agents: AI That Supports Users
Customer agents are designed to engage with users, answer inquiries, and handle routine customer service tasks, usually 24/7. Equipped with natural language processing (NLP), these AI Agents can communicate conversationally, providing seamless support and improving customer satisfaction. They can even route complex issues to live agents or escalate to specialized teams.
Example 1
Volkswagen US partnered with Google’s Gemini and developed a virtual assistant for its myVW app. It can address driver questions such as “How do I change a flat tire?” It also provides explanations for indicator lights using a phone camera.
Example 2
Conversational AI offers interactive support in customer service applications, like virtual agents, that assist customers with billing inquiries or product troubleshooting.
9. Employee Agents: AI That Supports Internal Teams
Employee agents assist in HR, administrative, and productivity tasks, helping employees manage schedules, training, and day-to-day operations. By automating routine activities, these AI Agents enable employees to focus on more strategic responsibilities.
Example 1
Onboarding agents guide new employees through training modules, helping them complete paperwork and track onboarding progress, reducing the workload for HR teams.
Example 2
Uber uses employee agents to optimize driver onboarding by automating background checks, training module assignments, and support ticket resolution, enhancing efficiency and reducing processing time.
10. Creative Agents: AI That Supports Content Creation
Creative agents support content creation by generating text, images, or video content based on specific inputs. These agents leverage generative AI models to create outputs that meet brand guidelines and maintain a consistent tone. Content agents assist marketing teams by drafting social media posts, generating ad copy, or designing basic graphics, allowing creative teams to focus on more high-level strategy. Example 1
Look at our case study for Talent Inc., where we built their resume-writing AI Agents with Content AI.
Example 2
PUMA leverages Imagen to generate customized product photos for their website, streamlining the process while tailoring visuals to local markets.
11. Data Agents: AI That Supports Data Management Tasks
Data agents handle large-scale data processing tasks, from data cleaning to analytics. They work as information retrieval agents to extract insights from massive datasets, helping businesses make data-driven decisions quickly. Example 1
Financial institution data analysis agents can process real-time market data, identify patterns, and offer predictive insights for traders or analysts.
Example 2
In another case study, we implemented Database AI to let sales representatives extract data from a single database. This enhanced the speed and accuracy of query responses and improved customer satisfaction.
12. Code Agents: AI That Assists Software Developers
Code agents assist software developers in creating and maintaining applications and systems by streamlining several tasks, such as:
- Detecting and resolving bugs
- Recommending code optimizations
- Generating code snippets from natural language inputs
- Enhancing code quality
- Speeding up the development lifecycle
These agents work as productivity boosters for technical teams, enabling them to write, refine, and optimize code more efficiently. Example 1
Google Cloud developed Vertex AI Agent Builder, enabling businesses to develop AI assistants with minimal coding effort.
Example 2
GitHub’s Copilot is an AI-powered code assistant that helps developers accelerate the coding process. It integrates seamlessly with popular IDEs, enabling developers to focus on problem-solving rather than repetitive coding tasks.
13. Security Agents: AI That Enhances Organizational Security
Security agents monitor systems continuously, detect anomalies, and respond to threats in real time. By leveraging artificial intelligence, they enhance organizational security, safeguard sensitive data, and effectively mitigate risks. Example 1
Security agents in banking applications use AI to detect fraudulent transactions by analyzing patterns in customer behavior. They instantly flag and block suspicious activity, protecting accounts and reducing fraud losses.
Example 2
Microsoft Security Copilot is an AI-powered tool that assists Security Operations Center (SOC) teams by enhancing threat detection, investigation, and response capabilities. It integrates with Microsoft's security products to provide real-time insights and recommendations.
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