Types of AI Agents

Key takeaways

AI agents are autonomous programs that use reasoning and planning to make decisions, complete user-defined tasks, and pursue goals. Here are some important things to know: 

• A 2025 PwC survey found that 66 percent of the companies in the United States that had already adopted AI agents reported measurable productivity gains [1].

• AI agents can range from rule-based systems that respond to static input to advanced LLM-based models that dynamically adapt over time.

• You can apply AI agents for robotic warehouse systems, smart building management, customer service, autonomous vehicles, personalized marketing campaigns, and more. 

Explore the various types of AI agents currently available, and discover their primary functions and key applications across industries. If you’re ready to learn more about agentic AI, enroll in the IBM RAG and Agentic AI Professional Certificate. You’ll have the opportunity to gain experience with retrieval-augmented generation, multimodal AI applications, and tools for building AI agents, like LangGraph and CrewAI, in as little as eight weeks. 

What is agentic AI?

Agentic AI refers to artificial intelligence (AI) systems that can autonomously complete specific tasks without requiring human supervision. These systems consist of multiple specialized AI agents that use machine learning to learn, adapt, and solve problems in real-time. Building on generative AI (genAI), agentic AI systems leverage large language models (LLMs) to not only generate content but also apply it towards achieving objectives, such as optimizing delivery routes according to real-time traffic conditions for improved scheduling, for example.

You set the goals or parameters, and the AI agents will interact with their environment, collect data, and use that data to decide on the best way to achieve those goals. Each AI agent performs specific subtasks based on the predefined parameters, and agentic AI systems coordinate and orchestrate multiple AI agents to automate larger, more complex workflows.

The adoption of multi-agent AI systems is on the rise, as businesses realize the benefits of agentic AI in automating cross-functional workflows. In a 2025 PwC survey, 79 percent of the 300 senior executives surveyed reported that their companies had already adopted AI agents [1]. Of these companies, 66 percent reported productivity increases as the number one benefit of adopting AI agents, followed by cost effectiveness, faster decision-making, and improved customer service [1].

What are the different types of AI agents?

AI agents, including simple reflex, model-based, goal-based, utility-based, learning, hierarchical, and large language model agents, form the foundation of agentic AI systems. You can classify them based on their intelligence level, decision-making approach, and interaction with their environment. These intelligent agents, which work as components within the broader framework of agentic AI, range from rule-based systems that respond to static input to advanced LLM-based models that use reason, problem-solving, and interactive capabilities to dynamically adapt over time.

Explore the different types of AI agents and their key features and applications in more detail for a better understanding of how each works. 

Simple reflex agents

Simple reflex agents rely on current sensory input to respond to environmental stimuli without using memory or learning. As their name implies, they are simple, operating on predefined condition-action rules, that is, if a condition is met, then a certain action is performed, making them suitable for predictable environments and tasks that don’t require elaborate training.

Key features:

• Use sensors to collect environmental data, like temperature, light, etc., and actuators to output the corresponding action

• Decides on an action based on static, predefined rules for that condition

• Cannot store past data or apply reasoning beyond the set conditions or situation you program it to deal with

Example use cases:

• A thermostat that turns on the heater when detecting a temperature drop below a certain level

• Industrial safety sensors that turn off machinery when detecting that something is blocking up the system

• Email auto-responders that send predetermined responses based on keywords

Model-based agents

Building upon the condition-action rules of simple reflex agents, model-based agents form an internal representation of the world they perceive and use this understanding to make decisions. They track alterations to the current state of the environment and use the internal world model to infer unobservable aspects of the environment, driving improved decision-making.

Key features:

• Uses a world model to store data about how the environment evolves and how its actions influence it, helping make more context-aware decisions

• Maintains a current understanding of the environment based on sensor history and the world model

• Applies condition-action rules, along with the world model and current state data, to determine suitable actions 

• Operates effectively in environments where sensors can’t capture every variable 

Example use cases:

• Smart thermostats that adjust settings based the occupancy in the room and user schedules

• Network monitoring tools that learn about network conditions to detect issues with IT infrastructure

• Smart home security systems that can differentiate between normal and abnormal activity based on a model of typical activity patterns of the building’s occupants

Goal-based agents

Goal-based agents incorporate planning and reasoning to perform actions that lead toward specific objectives. Using search and planning algorithms, these AI agents compare different actions and their potential future outcomes to determine which sequences get them closer to their goal.

Key features:

• Follows a clearly defined target outcome or goal

• Searches through possible actions to identify those that help reach the goal

• Measures how potential future states align with the target outcome

• Incorporates a world model and an understanding of environmental dynamics to plan action sequences

Example use cases:

• Project management software that can organize tasks to meet goals within a deadline

• Robotic warehouse systems that use sensors to plan optimal routes for moving inventory

• Complex simulation systems with well-defined objectives that require real-time flexibility to adapt according to circumstances for improved decisions 

Is ChatGPT considered an AI agent?

No, ChatGPT in general isn’t an AI agent but a generative AI tool that can produce text and images or answer questions, which an AI agent may use to complete tasks on its own. However, recently, OpenAI has launched a new agent feature for ChatGPT, which allows you not only to generate content but also to direct ChatGPT to utilize its computing engine to handle complex tasks on your behalf.

Utility-based agents

Extending the function of goal-based agents, utility-based agents employ a “utility function” to evaluate and select outcomes that are most desirable or beneficial. These agents consider multiple possible outcomes and choose one that maximizes overall utility, like cost-savings or safety. This makes them ideal for balancing tradeoffs between competing objectives in complex, dynamic environments. 

Key features:

• Assigns numerical values to potential outcome states based on their desirability to aid in comparison

• Continuously evaluates current and possible future states to determine the optimal action

• Understands dynamic environmental conditions and how different actions affect utilities

Example use cases:

• Stock trading bots that measure profit, risk, and market conditions against your investing preferences to provide personalized financial advice

• Smart building management systems that can optimize energy consumption, security, and comfort

• Self-driving cars that can plan optimal routes based on fuel efficiency and safety

Learning agents

Learning agents use past experiences and environmental feedback for enhanced performance over time. Compared to rule-based agents, learning agents can adapt their behavior over time by interacting with the environment and refining their behavior through experience, using reinforcement learning and other learning mechanisms. This makes them suitable for decision-making in complex, continuously evolving environments.

Key features: 

• Contains a performance element that executes decisions based on its current knowledge base

• Uses an internal critic that evaluates the agent's actions and provides feedback, often as rewards or penalties

• Incorporates a learning element that updates its knowledge and performance based on the critic’s feedback and past experiences

• Applies a problem generator that suggests exploratory actions so that the agent can form new strategies and perform experiments to refine its performance

• Uses machine learning techniques to refine its behavior

Example use cases:

• Customer service chatbots that can learn from user interactions to provide better responses in the future

• Streaming services that analyze user behavior to provide personalized recommendations

• Industrial process control systems that can learn the most efficient settings for manufacturing processes through experimentation

Hierarchical agents

Hierarchical agents work as a tiered system, in which high-level agents organize or coordinate lower-level agents. Higher-level agents break complex problems into manageable subtasks and delegate them to lower-level agents, mimicking human organizational structures. These agents are most effective in large-scale or multi-step systems with clearly defined subtasks and objectives.

Key features:

• Divides tasks into smaller, actionable subtasks for lower-level agents

• Establishes a clear command hierarchy, where lower agents report on progress and higher-level agents monitor and coordinate results

• Ensures all agent levels work cohesively to achieve the objectives

• Reduces computational load by having each tier focus on specialized functions

Example use cases:

• Smart factories that use hierarchical coordination for quality control, maintenance scheduling, and supply chain management

• Autonomous vehicles that use high-level agents for route planning and lower-level agents for steering 

• Building automation systems that employ layers of AI control to manage functions like heating and cooling and lighting

Large language model agents

LLM agents are AI systems built on large language models that combine memory, planning, and logical reasoning to understand questions and create human-like, detailed responses. Utilizing LLMs trained on large volumes of data, these agents apply sequential reasoning to think ahead, remember past conversations, and adjust their response based on the context.

Key features: 

• Breaks down complex queries into smaller subtasks, combines answers, and provides accurate outputs, allowing them to create plans, generate summaries, write code, etc.

• Analyzes past actions and performance to enhance future outputs

• Integrates with external tools like web searches or code testers to verify information and make corrections

• Can work in tandem with other LLM or AI agents, where one generates responses and another provides feedback

Example use cases:

• LLM sales agents that can automate outreach to potential customers, qualify leads, and send follow-ups to customers

• Service-based AI agents that can resolve customer queries, troubleshoot issues, and escalate cases to human agents when needed

• AI marketing agents that can segment audiences and create personalized marketing campaigns for the target audience

Agentic AI vs. generative AI

GenAI uses AI systems to create new content, like text, images, code, or video, based on a user’s prompt, while agentic AI can independently make decisions and perform actions in pursuit of higher-level goals. GenAI generates content based on the data it was trained on, whereas agentic AI can learn from experience and environmental data to continuously improve its performance. 

Read more: How to Learn AI

Getting started with AI agents and agentive AI concepts

Developing a strong grasp on the basics offers an excellent starting point. To continue learning about agentic AI topics, or about the field of AI in general, which can help you later develop and implement your own AI agent, consider the following steps. 

Learn the fundamentals.

Develop your understanding of the foundational concepts that form the basis of agentic AI systems, like machine learning and data science. Start by exploring beginner-level tutorials or guides online, like The Agentic AI Handbook by freeCodeCamp, to gain an elementary understanding of agentic AI concepts. Once you’re ready, you can take an online course that offers a structured learning roadmap to better understand how to apply AI agents in business scenarios. For example, the AI Foundations for Everyone Specialization offered by IBM could help you begin building your AI knowledge.

Practice with AI tools or projects.

Once you have some familiarity with the basics, consider building practical experience with popular AI tools like ChatGPT, AI-powered business applications, and AI platforms specifically for your industry to better understand how the technology works. You can also explore open-source projects and data sets on sites like Kaggle and GitHub, or join communities on Reddit or LinkedIn to communicate and learn from other learners. If you’re confident about your fundamental knowledge of AI, you might begin practicing building AI agents with the AI Agents in LangGraph project on Coursera.

Apply to real-world problems.

Once you’ve developed the foundations for building AI agents, start applying AI to real problems. This can involve automating a task in your workflow or training an AI model for a basic task like classifying pictures. Explore areas of your workplace that could be made more efficient with AI agents. This will not only help you when you’re building an agent later, but also reinforce your theoretical understanding into practical experience.

Build and deploy your AI agent.

Now that you’ve mastered how AI systems work, you can start building your own AI agent for your business. You’ll find a brief overview of the steps you’ll need to follow below:

• Clearly define what you want your AI agent to achieve and the tasks and functions it’ll perform.

• Collect high-quality data to train your AI model, ensuring you feed it the appropriate data for the type of interactions it will perform.

• Choose the right type of AI agent and machine learning model based on your agent’s functions and the data you collected.

• Test the AI agent to ensure it performs according to your expectations.

• Integrate your AI agent with existing software or platforms.

• Perform routine checks and improvements on your AI agent to ensure its continued effectiveness.

Explore free resources to take your skills further

Continue building your expertise and get practical tips and the latest career insights by joining our LinkedIn newsletter, Career Chat! Or, if you’re looking to learn more about AI skills, concepts, and courses, check out our free resources below:

• Take the Quiz: Which AI Course Should You Take?

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