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There are 13 types of AI agents, grouped in two ways: 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 by functional role (customer, employee, creative, data, code, and security agents).
The types of agentic AI map directly onto these categories, with one difference in emphasis. Agentic AI describes agents that not only answer but also take action to complete a task from start to finish. In financial services, that gap is concrete. A chatbot answers a member's question, while an agentic system reads the loan file, checks it, and moves it toward a decision.
This guide explains each agent type, with real-world examples, and shows how credit unions and other financial institutions put them to work.
An AI agent is the unit: a system that perceives its environment, reasons over the current input, and acts toward a goal. Agentic AI is the broader capability, usually built from multiple agents that plan, decide, and execute complex tasks with minimal human intervention.
The distinction matters for buyers because adoption is accelerating. Gartner projects that 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024, and that at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from 0% in 2024. The same research firm cautions that over 40% of agentic AI projects will be canceled by the end of 2027, mostly due to unclear business value and weak controls. Execution, not experimentation, separates the two outcomes.
Below, we cover the agent types first by decision logic, then by functional role.
Decision logic defines how an agent processes information, evaluates options, and selects the best course of action. Differentiating AI agents by their decision logic reveals the range of autonomy these systems can achieve, from fixed rules to adaptive behavior. Each agent type has its own key characteristics, and understanding them helps teams match the right agent to a task.
Simple reflex agents act on predefined rules and respond to specific conditions without considering past experiences or future outcomes. These rule-based systems follow condition-action logic: when a trigger appears, they execute a preset action. Because they react instantly to current input, reactive agents of this kind work well in environments with clear, consistent, fixed rules.
Example #1: In banking, simple reflex agents immediately flag transactions that meet predefined criteria for potential fraud.
Example #2: In insurance, they automatically send an acknowledgment email to policyholders upon receiving a claim submission.
Model-based reflex agents maintain an internal model of their environment, allowing them to consider past states when making decisions. This internal representation, sometimes called an internal world model, helps the agent operate in partially observable environments and respond to changing conditions. Unlike simple reflex agents, model-based agents hold an internal state that updates as new information arrives.
Example #1: In supply chain management, inventory agents use a model-based approach to monitor stock levels, project demand, and adjust orders.
Example #2: In lending, a model-based reflex agent tracks an applicant's profile against current underwriting guidelines. When a borrower submits additional documents, the agent updates its internal model, verifies that requirements are met, and flags missing data for review.
Goal-based agents make decisions aimed at achieving a specific outcome. Unlike reflex agents, they evaluate different actions and weigh future states to find the path that moves them closer to defined goals. As deliberative agents, they are well-suited to tasks with multiple possible actions, each carrying different implications for decision-making.
Example #1: Logistics routing agents find optimal delivery routes based on distance and time, adjusting continually to reach the most efficient route.
Example #2: Industrial robots follow specific sequences to assemble products, adjusting their actions to reach predefined assembly goals and desired outcomes.
Utility-based agents pursue goals while maximizing a utility function, a preference scale that ranks outcomes. This utility-based approach lets them handle tasks with multiple possible solutions by weighing multiple factors and criteria to choose the option with the highest expected utility (i.e., expected value). They are well suited to trade-offs across cost, quality, and risk.
Example #1: Financial portfolio management agents evaluate investments by risk, return, and diversification, then choose the options that provide the most value.
Example #2: Resource allocation systems balance machine usage, energy consumption, and production goals to maximize overall output. Dynamic pricing engines work the same way, using a utility function to balance competing objectives in real time.
Learning agents adapt and improve their behavior over time based on experience and feedback. They often act as predictive agents, using historical data and past interactions to anticipate outcomes and adjust actions to improve future performance. Many modern learning agents are LLM-based, built on large language models and refined through reinforcement learning.
Example #1: Recommendation systems on e-commerce sites refine product suggestions based on user interactions and preferences.
Example #2: Customer service assistants improve response accuracy over time by learning from previous interactions and adapting to user needs.
Multi-agent systems consist of several AI agents working collaboratively, or sometimes competitively, within a shared environment. Each agent specializes in a task, so the group can handle interdependent workflows that a single agent cannot handle. Multiple agents coordinating in this way make MAS scalable and well-suited to complex environments. They enable a group of autonomous agents to solve problems that no single agent can.
Example #1: Smart city traffic management uses MAS to regulate flow, with agents that manage signals, monitor congestion, and suggest alternative routes. Autonomous vehicles rely on similar coordination among multiple systems.
Example #2: In credit union lending, a multi-agent system processes the whole file. One agent reads the packet, another checks it against guidelines, and another routes it for a decision, which is how agents replace manual re-keying rather than just chatting with a member.
Hierarchical agents operate at different levels, each responsible for distinct tasks or decisions within the structure. They combine multiple agent types into a hierarchy, with lower-level agents handling specific tasks and higher-level agents managing broader objectives. The pattern is common in robotics, automation, and control systems, and any setting where one controller coordinates many workers.
Example #1: In manufacturing, low-level quality control agents inspect individual items while high-level agents analyze the data to spot patterns and improve production.
Example #2: In autonomous drone operations, a low-level agent handles navigation and obstacle avoidance, while a high-level agent manages route optimization, timing, and payload management.
A note on hybrid agents: many production systems are hybrid agents that combine reactive, deliberative, and learning behavior. This behavior-based blend gives a system both quick reflexes and careful planning, which is why few real deployments use a single pure type.
We can also group AI agents by their functional roles inside a business, from supporting customers to processing data. These role-based agents map to the jobs enterprise AI agents do every day. The first two, customer and employee agents, are often described more broadly as conversational agents.
Customer agents engage with users, answer inquiries, and handle routine service tasks, usually around the clock. Equipped with natural language processing, these intelligent agents communicate in a conversational manner and can route complex issues to live agents.
Example #1: Volkswagen US worked with Google's Gemini on a virtual assistant for its myVW app that answers questions such as how to change a flat tire and explains indicator lights through a phone camera.
Example #2: Conversational support agents help customers with billing inquiries or product troubleshooting in real time.
Employee agents assist with HR, administrative, and productivity tasks, helping staff manage schedules, training, and daily operations. By automating routine activities, they free human teams to focus on more strategic work.
Example #1: Onboarding agents guide new hires through training modules and paperwork, reducing HR workload.
Example #2: Uber uses employee agents to streamline driver onboarding by automating background checks, training assignments, and support ticket resolution.
Creative agents support content creation by generating text, images, or video from specific inputs. These agents use generative AI models, often shortened to gen AI, to produce outputs that adhere to brand guidelines and maintain a consistent tone, helping marketing teams move faster.
Example #1: Generative AI assistants draft social posts and ad copy so creative teams can focus on strategy.
Example #2: PUMA uses Imagen to generate customized product photos for its website and tailor visuals to local markets.
Data agents handle large-scale data processing, from cleaning to analytics. They work as information retrieval agents, extracting insights from large datasets so teams can make data-driven decisions quickly.
Example #1: Financial data analysis agents process real-time market data, identify patterns, and surface predictive insights for analysts.
Example #2: Database agents allow non-technical users to query data in plain language, improving response speed and accuracy.
Code agents help developers build and maintain software by detecting bugs, recommending optimizations, generating snippets from natural language, and speeding up the development lifecycle. They act as productivity boosters, connecting to external tools and IDEs.
Example #1: Google Cloud's Vertex AI Agent Builder lets teams build AI assistants with minimal coding.
Example #2: GitHub Copilot accelerates coding inside popular IDEs so developers can focus on problem-solving.
Security agents continuously monitor systems, detect anomalies, and respond to threats in real time. Using artificial intelligence, they protect sensitive data and reduce risk across multiple industries.
Example #1: Security agents in banking detect fraudulent transactions by analyzing patterns in customer behavior, then flag and block suspicious activity.
Example #2: Microsoft Security Copilot helps security teams improve threat detection, investigation, and response.
Businesses combine a range of AI agents to streamline workflows, improve decision-making, and raise customer satisfaction. The right mix depends on the task and the environment, from static rules to dynamic environments.
Customer agents handle routine inquiries, troubleshoot issues, and provide real-time answers, which cuts wait times and lightens the load on human teams.
Data agents process large datasets to deliver actionable insights for finance, healthcare, and retail. In finance, they analyze market data and help automate transactions. In retail, they power recommendation systems and real-time inventory updates.
Agents built on large language models translate plain language into database queries, enabling non-technical users to extract insights. Goal-based and utility-based agents optimize workflows by prioritizing tasks and finding efficient paths for logistics and supply chain planning. These are the AI capabilities that turn manual processes into autonomous systems.
Code agents automate repetitive coding, suggest fixes, and debug, shortening the development lifecycle.
Creative agents help marketing teams produce consistent, brand-aligned content across channels, accelerating campaigns while preserving brand coherence.
Multimodal builds agentic AI solutions for financial institutions on AgentFlow. Rather than a single assistant, AgentFlow coordinates multiple agents as a multi-agent system, enabling a credit union or bank to automate an entire process, not just one step. The agents read documents, validate entries, assess risk, and route files for review, which is the difference between answering a question and completing the work.
The operational case is clearest in lending. FORUM Credit Union processes 70% more loans after deploying AgentFlow, with agents handling document intake and packet preparation that staff previously handled by hand. That is autonomous AI agents solving a real, document-heavy problem, not a demo.
For teams comparing options, the practical test is simple. Ask where an agent stops—reactive and conversational tools answer and hand off. Agentic systems carry the task to a decision-ready outcome with minimal human intervention.
Agentic AI encompasses the same agent types used to classify AI agents: simple reflex, model-based reflex, goal-based, utility-based, learning, multi-agent, and hierarchical. The word agentic emphasizes agents that act autonomously to complete a task, rather than only responding to a query.
An AI agent is a single system that perceives its environment and acts on it. Agentic AI refers to the broader capability, often built from multiple agents, to plan, decide, and carry out multi-step tasks with minimal human intervention.
The five classic types of decision logic are simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. Multi-agent systems and hierarchical agents extend these into more complex setups.
A chatbot answers questions. An AI agent takes action. In lending, for example, an agent does not simply respond to a member; it processes the loan file from intake to a decision-ready package.
For document-heavy lending and back-office work, model-based reflex agents, multi-agent systems, and data agents fit best because they read, validate, and route files rather than only converse with members.
Multi-agent systems are better suited to complex, interdependent workflows in which specialized agents hand off to one another. A single agent is sufficient for narrow, rule-clear tasks that do not require coordination.
Credit unions combine agents to automate loan processing, KYC, and underwriting. The agents extract documents, assess risk, and route files for review, thereby shortening turnaround time and reducing manual data entry. See our credit union AI examples for real workflows.
AI agents are changing how financial institutions operate. With the ability to manage end-to-end processes, they handle multi-step tasks such as document review, KYC, and underwriting support with speed and consistency. The types of agentic AI covered here, from simple reflex agents to multi-agent systems, are the building blocks.
The institutions that capture value will be the ones that move agents into production where the ROI is clear and that govern them well. As Gartner's caution on canceled projects shows, the technology rewards disciplined execution over experiments.
We tailor agentic AI to your specific workflows, so you see results quickly, with agents trained on your data to match how your best people work. To see them run on your documents, book a demo with our team.
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