13 Types of AI Agents and Agentic AI (with Examples)

There are 13 types of AI agents, grouped in two ways: seven by decision logic (simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, learning agents, multi-agent systems, and hierarchical agents) and six by functional role (customer, employee, creative, data, code, and security agents). Decision logic describes how an AI agent thinks. A functional role describes the job it performs. Understanding both lenses of AI agent types helps you match the right agent to the right work.

That match decides outcomes. Gartner projects that over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value, or inadequate risk controls. Many failures start with a mismatch: rules assigned to judgment work, or autonomy deployed where simpler designs would do.

What Are the Types of AI Agents?

The table below compares the key characteristics of all 13 AI agent types, with real-world examples for each.

What Is the Difference Between AI Agents and Agentic AI?

An AI agent is a system that uses artificial intelligence to perceive its environment and act on it to reach an objective. Intelligent agents range from a thermostat to autonomous systems that run entire workflows. Agentic AI is the broader category of AI systems designed to act autonomously toward specific goals, deciding their own sequence of actions instead of following a fixed script. One intelligent agent handles a bounded task, while agentic AI systems chain perception, reasoning, and action to complete complex tasks, often calling external tools and coordinating multiple agents.

Most modern agentic AI runs on large language models. LLM-based agents attach memory, planning, and access to tools around the model.

What Are the 7 Types of AI Agents by Decision Logic?

The classic academic answer is that there are five main types of AI agents: simple reflex, model-based reflex, goal-based, utility-based, and learning agents. Modern enterprise AI agents add two architectures that matter in production: multi-agent systems and hierarchical agents. Architectures also split into single-agent systems, where a single autonomous agent performs the entire task, and multi-agent systems, where work is distributed.

1. Simple Reflex Agents

Simple reflex agents follow predefined condition-action rules: if the input matches a condition, take the mapped action. They react instantly to the current input and do not retain past information or experiences. A thermostat is the classic example, and the same pattern runs much of industrial automation and control systems.

These reactive agents are effective in stable environments, and because they carry no memory, reflex agents can repeatedly make the same mistakes in new situations the fixed rules never anticipated. Rule-based systems are cheap and predictable until the work requires judgment.

Chris Ortega, who leads technology transformation at Lake Michigan Credit Union, described that ceiling on Multimodal's Main Street AI podcast:

"Your automation, at that time, is generally rules-based. And that was great... The tricky thing comes when you hit fuzzy logic... now your choices are so myriad that you need a human to be able to discern that." — Chris Ortega, Technology Transformation Leader, Lake Michigan Credit Union

2. Model-Based Reflex Agents

Model-based reflex agents extend simple reflex agents by incorporating an internal model of the world. The rules now consult an internal representation of the environment, an internal state that persists between inputs. That internal world model enables model-based agents to track past interactions and infer future states, thereby improving robustness in partially observable environments. Autonomous vehicles exemplify model-based reflex agents in action. Unlike simple reflex agents, a model-based agent tracking a loan file knows which stipulations remain open even when today's input is a single fax page.

3. Goal-Based Agents

Goal-based agents evaluate actions based on desired outcomes rather than matching rules. Unlike reflex agents, these deliberative agents weigh future outcomes, plan sequences of actions to achieve a goal, and adjust plans as obstacles arise during execution. GPS navigation systems operate as goal-based agents. They are common in workflow orchestration and resource scheduling: a KYC agent collects documents, runs checks, and replans when one fails, with no human scripting each branch of the decision-making.

4. Utility-Based Agents

Utility-based agents assign values to outcomes through a utility function and select actions that maximize expected utility, making them effective in complex, dynamic environments where multiple factors compete. They balance competing objectives such as speed and cost, or speed and safety, scoring options on multiple criteria and selecting the option with the highest expected value. Dynamic pricing and resource allocation are common commercial examples; in a back office, a utility-based agent decides which files to work first. One caveat: designing utility functions is hard, and a poorly weighted one optimizes for the wrong outcome at machine speed.

5. Learning Agents

Learning agents adapt their behavior based on environmental feedback. The standard architecture has four components: a performance element that acts, a learning element that improves it, a critic that scores results, and a problem generator that proposes new things to try. Through that loop, often driven by reinforcement learning, learning agents improve over time, turning past experiences and previous interactions into training signals. That powers recommendation systems, virtual assistants, and parts of autonomous driving. In financial services, fraud monitoring is the natural home, with ongoing evaluation and human-in-the-loop oversight.

6. Multi-Agent Systems

Multi-agent systems distribute decision-making across multiple interacting agents. Different agents specialize, and they may cooperate or compete for resources. Multi-agent systems solve complex problems by decomposing them into smaller tasks, and coordination is essential to avoid conflicting actions; coordinated agents optimizing warehouse operations are a well-known example. In lending, one agent classifies documents, another extracts data, a third validates, and a fourth routes exceptions.

Jeffrey Staw, Chief Information and Innovation Officer at Firefighters First Credit Union, compared this generation of agents to the RPA wave on the Main Street AI podcast:

"We did a lot of RPA... it drove some value but the margins of that value were not nearly what we thought they were going to be... Agentic is great because at scale you really can do a lot... you could probably support a lot more different implementations than you could with RPA because RPA was so system-specific." — Jeffrey Staw, Chief Information and Innovation Officer, Firefighters First Credit Union

7. Hierarchical Agents

Hierarchical agents organize decision-making into multiple levels: a higher-level agent plans and delegates tasks to lower-level agents. Hierarchical agents help manage complexity by breaking down tasks and improving coordination and efficiency in complex environments. In customer service, a main agent routes requests to specialized sub-agents; in lending, an orchestrator owns the loan file and delegates downstream.

A note on hybrid agents: they combine multiple approaches, integrating reactive, goal-based, and learning AI capabilities to stay adaptable in complex environments; a self-driving car is the standard example. Most production-grade enterprise AI agents are hybrids of the seven types above, so we treat hybrid as a design pattern rather than a separate type.

What Are the 6 Types of AI Agents by Functional Role?

Buyers usually shop by role: what job does the agent do? These role-based agents are how Google Cloud groups its published deployments across multiple industries.

• Customer agents handle service interactions: answering questions, resolving routine requests, escalating the rest. Scope is the design decision: answering a rate question is customer-agent work; processing the resulting application is workflow-agent work.
• Employee agents support human teams with drafting, summarizing, retrieval, and admin work, usually with a person reviewing output.
• Creative agents use generative AI to produce text, images, and video under brand and compliance constraints. Gen AI assistants are a familiar example; institutional use includes drafting disclosures and member communications for approval.
• Data agents clean, reconcile, and move data across multiple systems for migrations, merger integrations, and reporting.
• Code agents write, review, and test software. GitHub Copilot is a widely known example.
• Security agents monitor systems, detect anomalies, and respond to threats. Microsoft Security Copilot is a prominent example.

How Do You Choose the Right Type of AI Agent?

Choosing among AI solutions is a matching exercise between the shape of the work and the decision logic it needs. Five questions settle most cases:

1. Fixed rules or judgment? Cases that resolve with predefined rules suit a simple reflex design. Fuzzy logic and exceptions call for goal-based agents or stronger.
2. Can the agent see everything it needs? Partially observable environments need an internal representation of state: start with model-based agents.
3. Are there trade-offs to weigh? If success means balancing speed, cost, risk, and member experience, you are describing a utility function.
4. Does the pattern shift over time? Stable processes run on fixed rules. Shifting patterns, such as fraud, favor learning agents that use reinforcement learning and disciplined evaluation.
5. One task or many? Bounded tasks fit a single agent. Processes that decompose into many steps are well suited to multi-agent systems, often with hierarchical supervision.

Two filters sit on top. Vertical agents typically outperform horizontal generalists in regulated work, and regulated institutions cannot run fully autonomous agents without a review path: the realistic target is minimal human intervention on clean cases and human review on exceptions.

How Do Credit Unions Use Different Types of AI Agents?

Four patterns dominate what we see in agentic AI for credit unions:

• Document intake and stipulation clearing. A multi-agent system classifies, extracts, and validates each document while a hierarchical supervisor routes exceptions to staff. FORUM Credit Union processed 70% more loans this way, with processing time dropping from 45 to 8 minutes per file.
• Fraud and anomaly monitoring. Learning agents fit because fraud is a moving target in a dynamic environment.
• KYC and member verification. Goal-based agents own the outcome "member verified," planning checks and replanning in the event of failures.
• Member service. Customer agents answer questions; workflow agents process the file. Different jobs, done by different agents.

Starting from zero? Our guide to the first workflow a credit union should automate covers sequencing.

Frequently Asked Questions About the Types of AI Agents

What are the types of AI agents?

There are 13 types of AI agents across two groupings: seven by decision logic (simple reflex, model-based reflex, goal-based, utility-based, learning, multi-agent systems, and hierarchical agents) and six by functional role (customer, employee, creative, data, code, and security agents).

What are the 5 main types of AI agents?

The five main types from the classic taxonomy are simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. Modern enterprise practice adds multi-agent systems and hierarchical agents.

What are the types of agentic AI?

Goal-based agents, utility-based agents, learning agents, multi-agent systems, and hierarchical agents are the core types of agentic AI, defined by autonomous action toward goals rather than by fixed rules.

What is the difference between an AI agent and a chatbot?

A chatbot answers questions in conversation. An AI agent takes actions to complete work: retrieving data, calling external tools, updating systems, finishing multi-step tasks.

Are multi-agent systems better than a single AI agent?

Neither is universally better. A single agent suits one bounded task. Multi-agent systems win when a large problem can be decomposed into specialized tasks that benefit from coordination.

Which types of AI agents are best for credit unions?

Credit unions get the fastest measurable returns from multi-agent systems with hierarchical supervision for document-heavy lending workflows, learning agents for fraud monitoring, and goal-based agents for KYC.

What type of AI agent is ChatGPT?

Base ChatGPT is a large language model assistant rather than a full agent. With access to tools, memory, and multi-step execution, it operates as an LLM-based agent, closest to a goal-based agent with learning components.

Put the Right Agent Types to Work on Your Loan Files

The taxonomy is the easy part. Results come from matching agent types to the shape of your work, then holding them to production standards: measurable accuracy, human-reviewed exceptions, an audit trail an examiner can follow. AgentFlow was built for exactly that, with multi-agent systems tailored to your workflows and trained on your data.

Book a demo to see a multi-agent system process one of your loan files end-to-end.