{"id":21819,"date":"2026-09-22T13:13:04","date_gmt":"2026-09-22T13:13:04","guid":{"rendered":"https:\/\/dianapps.com\/blog\/?p=21819"},"modified":"2026-09-22T13:13:04","modified_gmt":"2026-09-22T13:13:04","slug":"types-of-ai-agents","status":"publish","type":"post","link":"https:\/\/dianapps.com\/blog\/types-of-ai-agents\/","title":{"rendered":"Types of AI Agents: 7 Kinds Explained With Real Examples"},"content":{"rendered":"<p><b>Key Takeaways<\/b><span style=\"font-weight: 400;\">:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI agents range from simple rule-based systems to complex multi-agent architectures with very different capabilities.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The seven types include simple reflex, model-based reflex, goal-based, utility-based, learning, hierarchical, and multi-agent systems.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Learning agents improve through experience, while hierarchical and multi-agent systems coordinate multiple tasks or specialized agents.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The right agent type depends on input variability, task complexity, and how much the system needs to improve over time.<\/span><\/li>\n<\/ul>\n<blockquote><p><b>Quick Answer<\/b><span style=\"font-weight: 400;\">: The seven main types of AI agents are simple reflex, model-based reflex, goal-based, utility-based, learning, hierarchical, and multi-agent systems. They differ in how they use memory, planning, learning, and coordination. Simple reflex agents work well for predictable rule-based tasks, while goal-based and utility-based agents handle planning and trade-offs. Learning agents improve from experience, and hierarchical or multi-agent systems are designed for more complex workflows involving multiple specialized tasks.<\/span><\/p><\/blockquote>\n<p><span style=\"font-weight: 400;\">Not all types of AI agents are built the same way or designed for the same problems. A simple FAQ chatbot and a multi-agent enterprise automation system are both &#8220;AI agents&#8221; but they operate on fundamentally different architectures, have vastly different capability profiles, and are suited to completely different use cases. Building the wrong type for your problem is as costly as choosing the wrong tool in any engineering discipline.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">AI agents can be classified in several ways. This guide covers seven commonly discussed agent architectures and modern agent-system patterns. In 2026, the taxonomy has grown beyond the academy and now includes multi-agent systems, learning agents, and autonomous LLM-driven agents that are able to plan for and act in difficult real-world situations-architectures that have only become feasible with the maturation of large language models.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This guide explains all seven types, compares them in a single reference table, and gives real 2026 examples of each type in production. For businesses looking to apply these architectures in real products, <\/span><a href=\"https:\/\/dianapps.com\/ai-development-services\"><span style=\"font-weight: 400;\">AI Development Services<\/span><\/a><span style=\"font-weight: 400;\"> can help turn the right agent approach into a production-ready AI solution.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Complete Comparison Table: 7 Types of AI Agents<\/span><\/h2>\n<table>\n<tbody>\n<tr>\n<td><b>Agent Type<\/b><\/td>\n<td><b>Memory<\/b><\/td>\n<td><b>Planning<\/b><\/td>\n<td><b>Learning<\/b><\/td>\n<td><b>Complexity<\/b><\/td>\n<td><b>Best For<\/b><\/td>\n<td><b>Example<\/b><\/td>\n<\/tr>\n<tr>\n<td><b>Simple<\/b><\/p>\n<p><b>Reflex<\/b><\/td>\n<td><span style=\"font-weight: 400;\">None<\/span><\/td>\n<td><span style=\"font-weight: 400;\">None<\/span><\/td>\n<td><span style=\"font-weight: 400;\">No<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Very Low<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Fully<\/span><\/p>\n<p><span style=\"font-weight: 400;\">predictable,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">structured<\/span><\/p>\n<p><span style=\"font-weight: 400;\">inputs<\/span><\/td>\n<td><span style=\"font-weight: 400;\">FAQ bot, rule<\/span><\/p>\n<p><span style=\"font-weight: 400;\">based email filter<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Model<\/b><\/p>\n<p><b>Based<\/b><\/p>\n<p><b>Reflex<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Internal<\/span><\/p>\n<p><span style=\"font-weight: 400;\">state<\/span><\/p>\n<p><span style=\"font-weight: 400;\">model<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Minimal<\/span><\/td>\n<td><span style=\"font-weight: 400;\">No<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Low<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Partially<\/span><\/p>\n<p><span style=\"font-weight: 400;\">observable<\/span><\/p>\n<p><span style=\"font-weight: 400;\">environments<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Smart<\/span><\/p>\n<p><span style=\"font-weight: 400;\">thermostat, GPS navigation<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Goal-Based<\/b><\/td>\n<td><span style=\"font-weight: 400;\">State +<\/span><\/p>\n<p><span style=\"font-weight: 400;\">goal<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Multi-step<\/span><\/td>\n<td><span style=\"font-weight: 400;\">No<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Medium<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Tasks with<\/span><\/p>\n<p><span style=\"font-weight: 400;\">defined end<\/span><\/p>\n<p><span style=\"font-weight: 400;\">states<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Search engines, route planners<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Utility<\/b><\/p>\n<p><b>Based<\/b><\/td>\n<td><span style=\"font-weight: 400;\">State +<\/span><\/p>\n<p><span style=\"font-weight: 400;\">utility<\/span><\/p>\n<p><span style=\"font-weight: 400;\">function<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Optimization<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Sometimes<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Medium\u2013<\/span><\/p>\n<p><span style=\"font-weight: 400;\">High<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Competing<\/span><\/p>\n<p><span style=\"font-weight: 400;\">goals<\/span><\/p>\n<p><span style=\"font-weight: 400;\">requiring<\/span><\/p>\n<p><span style=\"font-weight: 400;\">trade-offs<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Recommendation engines, ad<\/span><\/p>\n<p><span style=\"font-weight: 400;\">bidding<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Learning<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Experience memory<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Evolves with learning<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Yes<\/span><\/td>\n<td><span style=\"font-weight: 400;\">High<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Environments that change<\/span><\/p>\n<p><span style=\"font-weight: 400;\">over time<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Fraud detection, AI medical<\/span><\/p>\n<p><span style=\"font-weight: 400;\">coding<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Hierarchical<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Layered<\/span><\/p>\n<p><span style=\"font-weight: 400;\">state<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Decomposed goals<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Sometimes<\/span><\/td>\n<td><span style=\"font-weight: 400;\">High<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Complex<\/span><\/p>\n<p><span style=\"font-weight: 400;\">goals<\/span><\/p>\n<p><span style=\"font-weight: 400;\">requiring sub task<\/span><\/p>\n<p><span style=\"font-weight: 400;\">coordination<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Enterprise<\/span><\/p>\n<p><span style=\"font-weight: 400;\">workflow<\/span><\/p>\n<p><span style=\"font-weight: 400;\">orchestration<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Multi-Agent System<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Shared +<\/span><\/p>\n<p><span style=\"font-weight: 400;\">individual<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Distributed<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Possible<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Very High<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Problems<\/span><\/p>\n<p><span style=\"font-weight: 400;\">requiring<\/span><\/p>\n<p><span style=\"font-weight: 400;\">multiple<\/span><\/p>\n<p><span style=\"font-weight: 400;\">specializations<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Agentforce,<\/span><\/p>\n<p><span style=\"font-weight: 400;\">UiPath Maestro, CrewAI<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<div style=\"background: #EEF2FE; border: 1px solid #DBE2FB; border-radius: 14px; padding: 28px 32px; margin: 38px 0;\">\n<h4 style=\"color: #1b3fae; font-size: 22px; line-height: 1.3; font-weight: bold; margin: 0 0 10px;\"><span style=\"font-weight: 400;\">Not Sure Which AI Agent You Need?<\/span><\/h4>\n<p style=\"color: #4b5563; font-size: 16px; line-height: 1.6; margin: 0 0 22px;\"><span style=\"font-weight: 400;\">Tell us about your workflow, and we\u2019ll help you identify the right agent architecture.<\/span><\/p>\n<p><a style=\"display: inline-block; background: #2563EB; color: #ffffff; text-decoration: none; font-size: 15px; font-weight: 600; padding: 13px 26px; border-radius: 8px;\" href=\"https:\/\/dianapps.com\/contact\">Talk To An AI Expert<\/a><\/p>\n<\/div>\n<h3><span style=\"font-weight: 400;\">Type 1: Simple Reflex Agent<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The concept of simple reflex agents is straightforward as it does not rely on any past percepts of the history, making it completely unable to plan for the future. The design of these agents is restricted to condition-action rules: &#8220;if we see X, do Y.&#8221; Because simple reflex agents keep no memory of the past nor any model of the world (a knowledge that represents something about the world that is not part of the current input), it has no notion of what will happen in the future and consequently no ability to act to change that future in any way.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This is the most basic agent architecture, and for the right kinds of problems &#8211; those where input is fully structured and predictable and there&#8217;s no ambiguity &#8211; it&#8217;s also the most reliable. There are no hallucinations, no confusion over new circumstances, and there&#8217;s always a faithful and consistent response for any given input. The negative is just as clear, though: any input that falls outside the rule set gets no response, or the wrong one.<\/span><\/p>\n<p><b>Real examples of simple reflex agents:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">An email filter that automatically classifies an email as spam if it contains certain words<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A chatbot that searches a predefined set of questions and gives a preprogrammed answer<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">You check the rating of the thermostat: turn it on if the temperature falls below a threshold, and so on.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A script that automatically applies a validation rule to a web form and rejects any invalid entries out of a specified format.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">What is the difference between a simple reflex agent and a learning agent? The rules of a simple reflex agent are hard-coded when the system is built, and do not change. A learning agent adjusts its rules, weights, or behavior according to feedback about how well previous actions performed. The rules of a simple reflex spam filter are hard-coded; a learning spam filter learns based on whether email is marked as spam or not.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Type 2: Model-Based Reflex Agent<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">A model-based reflex agent extends the simple reflex agent by maintaining an internal model of the world,\u00a0 a representation of the parts of the environment it can&#8217;t directly observe at any given moment. This internal model lets the agent act appropriately even when it can&#8217;t see the full current state of the environment.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The model in this context doesn&#8217;t refer to a machine learning model &#8211; here it is an internal understanding of what the world is like and how it changes over time. This allows the agent to infer information about the current state of the world that isn&#8217;t available, before actually using its condition-action rules.<\/span><\/p>\n<p><b>Real examples of model-based reflex agents:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A GPS navigation system that maintains a model of traffic conditions even between GPS updates A smart thermostat that models occupancy patterns and adjusts temperature before residents return home A robot vacuum that maintains a map of a room it has partially explored.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">An inventory management system that models current stock levels based on sales transactions even before physical count.<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Type 3: Goal-Based Agent<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">The goal-based agent builds upon the model-based agent, and on top of the current state, it is explicitly given the goal. This means that the goal-based agent does not just react to the current state, but that it knows its current goal and that it is asked to determine the sequence of actions necessary to get to the goal. This means that planning is required.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Goal-based agents are not restricted to scenarios where an action is immediately goal-satisfying \u2013 they think ahead, even several steps ahead, to find a sequence of actions from where you are now to where you want to be. This is much more powerful than reflex agents, but also much harder to compute.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Goal-based agents are also where the architecture starts becoming more practical for real business workflows. If you&#8217;re looking at how these systems are actually developed, see our guide on <\/span><a href=\"https:\/\/dianapps.com\/blog\/how-to-build-an-ai-agent\/\"><span style=\"font-weight: 400;\">how to build an AI agent<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><b>Real examples of goal-based agents:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A search engine that plans a series of web crawls to index all pages within a domain\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A route-planning system (Google Maps, Waze) that reasons about a sequence of turns to reach a destination A chess engine that evaluates sequences of moves to reach a winning position\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">An AI agent that plans a sequence of API calls to complete a user&#8217;s research request\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">GitHub Copilot Workspace, which plans a sequence of code changes across\u00a0<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Type 4: Utility-Based Agent<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">A utility-based agent extends the goal-based structure with a utility function-something to say &#8216;how good&#8217; each state or result is, rather than a goal (or not). This enables the agent to deal with having more than one way to reach a goal, competing goals, or uncertainty about the optimal path. The agent then chooses the action that will maximize its utility.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">What is responsible for this agent type taking trade-offs: faster but less safe, cheaper but lower quality, higher short-term gain but more long-term risk? The goal-based agent will do the goal by any means, the utility based agent will do the goal in the most maximising way.<\/span><\/p>\n<p><b>Real examples of utility-based agents:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Netflix and Spotify recommendation engines that balance relevance, novelty, and engagement in content suggestions\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Algorithmic trading bots that balance expected return against risk within defined constraints Advertising bid management systems that optimize for conversion value given a cost-per-acquisition constraint AI scheduling systems that optimize appointment slots for revenue and patient satisfaction simultaneously Route planners that balance distance, tim<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Type 5: Learning Agent<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">A learning agent is an agent that learns from its experience. A learning agent is not hard-wired like a reactive agent but has the capacity to learn while acting: it must have a dedicated module for learning that acts on the information it gets either explicitly from a human (learning by instruction) or implicitly by observing the effects of its own actions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This is the kind of AI agent that improves the longer it runs and this is the kind of AI agent that most of the advanced types of AI agents running in production in 2026 are. These are sometimes called learning agents, because training sets their parameters by using backpropagation &#8211; gradient descent on a loss function &#8211; to improve the expected output on novel inputs.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Many modern learning and autonomous agents are built around generative AI models, so understanding the available <\/span><a href=\"https:\/\/dianapps.com\/blog\/generative-ai-platforms\/\"><span style=\"font-weight: 400;\">generative AI platforms<\/span><\/a><span style=\"font-weight: 400;\"> can help when choosing the underlying technology.<\/span><\/p>\n<p><b>Real examples of learning agents:\u00a0<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Fraud detection models that update based on new labeled fraud cases,\u00a0 when a new fraud pattern emerges, the model learns to recognize it\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">AI medical coding tools (Medicodio CODIO, CodaMetrix) that improve first-pass accuracy based on payer denial feedback\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Email spam filters that update based on user feedback (marking as spam \/ not spam)\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Language models that are fine-tuned on domain-specific data to improve performance on specialized content AI customer support agents that incorporate human reviewer feedback to improve resolution accuracy over time\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">LangChain&#8217;s State of AI Agent Engineering reports that 52% of teams building agents run offline evaluations and 37% evaluate agents in production. The teams with high evaluation rates are typically running learning agents,\u00a0 where performance measurement is the prerequisite for improvement.\u00a0<\/span><\/p>\n<div style=\"background: #EEF2FE; border: 1px solid #DBE2FB; border-radius: 14px; padding: 28px 32px; margin: 38px 0;\">\n<h4 style=\"color: #1b3fae; font-size: 22px; line-height: 1.3; font-weight: bold; margin: 0 0 10px;\"><span style=\"font-weight: 400;\">Ready to Build an AI Agent?<\/span><\/h4>\n<p style=\"color: #4b5563; font-size: 16px; line-height: 1.6; margin: 0 0 22px;\"><span style=\"font-weight: 400;\">Build AI agents that learn from feedback and adapt to your business workflows.<\/span><\/p>\n<p><a style=\"display: inline-block; background: #2563EB; color: #ffffff; text-decoration: none; font-size: 15px; font-weight: 600; padding: 13px 26px; border-radius: 8px;\" href=\"https:\/\/dianapps.com\/contact\">Contact Us<\/a><\/p>\n<\/div>\n<h3><span style=\"font-weight: 400;\">Type 6: Hierarchical Agent\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">A hierarchical agent system means several levels of agents with the topmost agent for the broadest goals, then it&#8217;s broken down into sub-goals for lower-level agents, which can further subdivide if they need to, until the lowest-level agents are just carrying out all the atomic actions directly.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Note that this is just how large human organizations tend to work: the general keeps handing down overarching goals and a department manages that goal, then an individual manages how their sub-tasks are carried out, then there&#8217;s a person executing those tasks, and so on.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">These hierarchical architectures are particularly useful for enterprise automation, where multiple workflows and systems need to work together. See our guide to <\/span><a href=\"https:\/\/dianapps.com\/blog\/best-ai-automation-platforms-for-enterprises\/\"><span style=\"font-weight: 400;\">AI automation platforms for enterprises<\/span><\/a><span style=\"font-weight: 400;\"> for a closer look at the platforms supporting these deployments.<\/span><\/p>\n<p><b>Real examples of hierarchical agents:\u00a0<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">UiPath Maestro<span style=\"font-weight: 400;\">: An orchestrator agent coordinates RPA robots and AI agents across complex IT workflows,\u00a0 routing tickets, assigning resolution paths, monitoring completion, escalating exceptions.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A content production system where a planning agent defines the research topics, research agents gather information, a writing agent produces the draft, and an editing agent reviews it.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">A customer onboarding orchestrator that delegates documentation collection to a document agent, account setup to a provisioning agent, and welcome communications to a communications agent.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">An enterprise compliance monitoring system where a master compliance agent delegates jurisdiction-specific monitoring to regional compliance agents.<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Type 7: Multi-Agent System (MAS)\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">A multi-agent system (or MAS) is a number of AI agents that work together in a network to tackle a problem. They can cooperate, compete or communicate with one another to obtain their own (or shared) objectives. Agents in a multi-agent system need not be linked in a hierarchy, as in a hierarchy of agents, and can work as peers.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">2026 is described by multiple framework developers as &#8220;the year of multi-agent systems.&#8221; <\/span><a href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025\"><span style=\"font-weight: 400;\">Gartner<\/span><\/a><span style=\"font-weight: 400;\"> expects 40% of enterprise applications to embed task-specific agents by the end of 2026. The fastest-growing AI agent frameworks,\u00a0 CrewAI (44,600+ GitHub stars), LangGraph (34.5M monthly downloads),\u00a0 are built specifically for multi-agent orchestration.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">What Is an Autonomous AI Agent?\u00a0<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">An autonomous AI system is an agent that can do things on its own without humans needing to trigger the next step: small-scale multi-agent systems and hierarchical agent structures are, in reality, autonomous once they are launched, it is just defining the goal and setting up the structure.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Autonomous systems send themselves or conduct their own operations; it&#8217;s not the case of human-in-the-loop systems. The level of autonomy is essential for governance: autonomous systems take irreversible actions (email, payments, creating records); human-in-the-loop systems take actions but leave users to approve major actions.<\/span><\/p>\n<p><b>Real examples of multi-agent systems and autonomous AI agents in production:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Salesforce Agentforce<\/b><span style=\"font-weight: 400;\">: Multiple specialized agents,\u00a0 service agents, sales agents, marketing agents, employee experience agents,\u00a0 operating within Salesforce CRM data. Each agent specializes in a domain; the platform coordinates which agent handles which workflow.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>UiPath Maestro<\/b><span style=\"font-weight: 400;\">: Coordinates deterministic RPA robots and probabilistic AI agents within the same workflow,\u00a0 using each where it&#8217;s most appropriate and handing off between them based on task type and confidence level.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>CrewAI Content Pipelines<\/b><span style=\"font-weight: 400;\">: A Researcher agent gathers information, a Writer agent produces a draft, an Editor agent reviews it,\u00a0 three specialized agents collaborating sequentially on a shared output.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>DianApps Orby (Enterprise LAM)<\/b><span style=\"font-weight: 400;\">: The first enterprise AI system powered by a Large Action Model,\u00a0 a multi-agent architecture designed to take coordinated multi-step actions across enterprise software systems.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Microsoft Copilot Studio Multi-Agent Workflows<\/b><span style=\"font-weight: 400;\">: Multiple Copilot agents handling different domains (IT support, HR, sales) that hand off to each other and to human specialists based on the nature of incoming requests.<\/span><\/li>\n<\/ul>\n<div style=\"background: #EEF2FE; border: 1px solid #DBE2FB; border-radius: 14px; padding: 28px 32px; margin: 38px 0;\">\n<h4 style=\"color: #1b3fae; font-size: 22px; line-height: 1.3; font-weight: bold; margin: 0 0 10px;\"><span style=\"font-weight: 400;\">Need AI Developers?<\/span><\/h4>\n<p style=\"color: #4b5563; font-size: 16px; line-height: 1.6; margin: 0 0 22px;\"><span style=\"font-weight: 400;\">Hire AI developers to design and build reliable agentic systems for complex workflows.<\/span><\/p>\n<p><a style=\"display: inline-block; background: #2563EB; color: #ffffff; text-decoration: none; font-size: 15px; font-weight: 600; padding: 13px 26px; border-radius: 8px;\" href=\"https:\/\/dianapps.com\/contact\">Get In Touch<\/a><\/p>\n<\/div>\n<h2><span style=\"font-weight: 400;\">Which Type of AI Agent Should You Build?\u00a0<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">The right type depends on three questions: How variable are the inputs? How complex is the task? How much does performance need to improve over time? These considerations also shape <\/span><a href=\"https:\/\/dianapps.com\/ai-agent-development-services\"><span style=\"font-weight: 400;\">AI Agent Development Services<\/span><\/a><span style=\"font-weight: 400;\">, where the architecture, level of autonomy, tools, memory, and orchestration approach need to be matched to the workflow the agent is expected to handle.<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><b>Your Situation<\/b><\/td>\n<td><b>Agent Type<\/b><\/td>\n<td><b>Why<\/b><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Fully predictable, rule-definable inputs; reliability is the primary requirement<\/span><\/td>\n<td><b>Simple Reflex<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Most reliable and auditable; zero<\/span><\/p>\n<p><span style=\"font-weight: 400;\">hallucination risk<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Clear goal; variable path to achieve it; single workflow<\/span><\/td>\n<td><b>Goal-Based<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Can plan the path to the goal rather than needing it specified step-by-step<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Multiple competing objectives; quality vs. cost vs. speed trade-offs<\/span><\/td>\n<td><b>Utility-Based<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Optimizes across competing priorities; handles ambiguous &#8220;best&#8221; outcomes<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Environment changes over time;<\/span><\/p>\n<p><span style=\"font-weight: 400;\">performance needs to improve with use<\/span><\/td>\n<td><b>Learning<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Accuracy improves with feedback; adapts to changing conditions<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Complex goal requiring specialized sub tasks; single coordinating authority<\/span><\/td>\n<td><b>Hierarchical<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Decomposes complex goals; coordinates specialists; maintains accountability<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Multiple distinct expertise areas; parallel workstreams; cross-check requirements<\/span><\/td>\n<td><b>Multi-Agent<\/b><\/p>\n<p><b>System<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Specialist agents for each domain; parallel execution; peer review between agents<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">The practical principle from Anthropic&#8217;s Building Effective Agents guide: &#8220;Find the simplest solution possible, and only increase complexity when needed.&#8221; A simple reflex agent that works reliably is worth more than a multi-agent system that occasionally fails in ways you can&#8217;t predict. Build the minimum viable architecture that solves the problem, then expand as you have evidence more complexity is needed.\u00a0<\/span><\/p>\n<style>.elementor-21823 .elementor-element.elementor-element-2932a52{text-align:left;}.elementor-21823 .elementor-element.elementor-element-2932a52 > .elementor-widget-container{margin:0px 0px 0px 0px;}.elementor-21823 .elementor-element.elementor-element-0b767d1 .elementor-tab-title{border-width:1px;border-color:#00000014;}.elementor-21823 .elementor-element.elementor-element-0b767d1 .elementor-tab-content{border-width:1px;border-bottom-color:#00000014;}.elementor-21823 .elementor-element.elementor-element-0b767d1 > .elementor-widget-container{margin:0px 0px 0px 0px;}<\/style><div class=\"porto-block elementor elementor-21823\">\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-27707ca elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"27707ca\" data-element_type=\"section\">\r\n\t\t\t\r\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\r\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0163611\" data-id=\"0163611\" data-element_type=\"column\">\r\n\r\n\t\t\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\r\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-03a2969 elementor-widget elementor-widget-text-editor\" data-id=\"03a2969\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.14.0 - 26-06-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2932a52 elementor-widget elementor-widget-heading\" data-id=\"2932a52\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.14.0 - 26-06-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-large\">FAQs <\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0b767d1 elementor-widget elementor-widget-toggle\" data-id=\"0b767d1\" data-element_type=\"widget\" data-widget_type=\"toggle.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.14.0 - 26-06-2023 *\/\n.elementor-toggle{text-align:left}.elementor-toggle .elementor-tab-title{font-weight:700;line-height:1;margin:0;padding:15px;border-bottom:1px solid #d5d8dc;cursor:pointer;outline:none}.elementor-toggle .elementor-tab-title .elementor-toggle-icon{display:inline-block;width:1em}.elementor-toggle .elementor-tab-title .elementor-toggle-icon svg{-webkit-margin-start:-5px;margin-inline-start:-5px;width:1em;height:1em}.elementor-toggle .elementor-tab-title .elementor-toggle-icon.elementor-toggle-icon-right{float:right;text-align:right}.elementor-toggle .elementor-tab-title .elementor-toggle-icon.elementor-toggle-icon-left{float:left;text-align:left}.elementor-toggle .elementor-tab-title .elementor-toggle-icon .elementor-toggle-icon-closed{display:block}.elementor-toggle .elementor-tab-title .elementor-toggle-icon .elementor-toggle-icon-opened{display:none}.elementor-toggle .elementor-tab-title.elementor-active{border-bottom:none}.elementor-toggle .elementor-tab-title.elementor-active .elementor-toggle-icon-closed{display:none}.elementor-toggle .elementor-tab-title.elementor-active .elementor-toggle-icon-opened{display:block}.elementor-toggle .elementor-tab-content{padding:15px;border-bottom:1px solid #d5d8dc;display:none}@media (max-width:767px){.elementor-toggle .elementor-tab-title{padding:12px}.elementor-toggle .elementor-tab-content{padding:12px 10px}}.e-con-inner>.elementor-widget-toggle,.e-con>.elementor-widget-toggle{width:var(--container-widget-width);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-toggle\">\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<h3 id=\"elementor-tab-title-1201\" class=\"elementor-tab-title\" data-tab=\"1\" role=\"button\" aria-controls=\"elementor-tab-content-1201\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">What are the types of AI agents?<\/a>\n\t\t\t\t\t<\/h3>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1201\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"1\" role=\"region\" aria-labelledby=\"elementor-tab-title-1201\"><p><span style=\"font-weight: 400;\">The main types are: Simple Reflex agents that act on current conditions; Model-Based Reflex agents that maintain a world model; Goal-Based agents that reason toward goals; Utility-Based agents that choose between competing outcomes; Learning agents that improve through experience; Hierarchical agents that delegate tasks; and Multi-Agent Systems (MAS) where multiple agents cooperate or compete.<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<h3 id=\"elementor-tab-title-1202\" class=\"elementor-tab-title\" data-tab=\"2\" role=\"button\" aria-controls=\"elementor-tab-content-1202\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">What are examples of AI agents in business?<\/a>\n\t\t\t\t\t<\/h3>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1202\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"2\" role=\"region\" aria-labelledby=\"elementor-tab-title-1202\"><p><span style=\"font-weight: 400;\">Business examples include customer support agents, Salesforce Agentforce for sales and service, UiPath Maestro for workflow orchestration, fraud detection agents, clinical documentation agents, lead qualification agents, and AI code review agents.<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<h3 id=\"elementor-tab-title-1203\" class=\"elementor-tab-title\" data-tab=\"3\" role=\"button\" aria-controls=\"elementor-tab-content-1203\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">What is an autonomous AI agent?<\/a>\n\t\t\t\t\t<\/h3>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1203\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"3\" role=\"region\" aria-labelledby=\"elementor-tab-title-1203\"><p><span style=\"font-weight: 400;\">An autonomous AI agent completes tasks without human prompting at every step. Most production agents in 2026 are semi-autonomous, automating routine steps while requiring human review for high-stakes, uncertain, irreversible, or regulated actions.<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<h3 id=\"elementor-tab-title-1204\" class=\"elementor-tab-title\" data-tab=\"4\" role=\"button\" aria-controls=\"elementor-tab-content-1204\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">What is the difference between a simple reflex agent and a learning agent?<\/a>\n\t\t\t\t\t<\/h3>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1204\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"4\" role=\"region\" aria-labelledby=\"elementor-tab-title-1204\"><p><span style=\"font-weight: 400;\">Simple reflex agents follow fixed condition-action rules and do not change their behavior. Learning agents improve through experience and feedback. For example, a basic spam filter uses fixed rules, while a learning filter adapts based on how users classify messages.<\/span><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<h3 id=\"elementor-tab-title-1205\" class=\"elementor-tab-title\" data-tab=\"5\" role=\"button\" aria-controls=\"elementor-tab-content-1205\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">What are examples of agentic AI in business?<\/a>\n\t\t\t\t\t<\/h3>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-1205\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"5\" role=\"region\" aria-labelledby=\"elementor-tab-title-1205\"><p>Examples include Salesforce Agentforce for CRM automation, UiPath for agentic workflows, Microsoft Copilot Studio for enterprise workflows, GitHub Copilot Workspace for multi-file coding tasks, ServiceNow AI Agents for IT automation, and DianApps Orby for multi-step enterprise automation.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<script type=\"application\/ld+json\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What are the types of AI agents?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"<p><span style=\\\"font-weight: 400;\\\">The main types are: Simple Reflex agents that act on current conditions; Model-Based Reflex agents that maintain a world model; Goal-Based agents that reason toward goals; Utility-Based agents that choose between competing outcomes; Learning agents that improve through experience; Hierarchical agents that delegate tasks; and Multi-Agent Systems (MAS) where multiple agents cooperate or compete.<\\\/span><\\\/p>\"}},{\"@type\":\"Question\",\"name\":\"What are examples of AI agents in business?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"<p><span style=\\\"font-weight: 400;\\\">Business examples include customer support agents, Salesforce Agentforce for sales and service, UiPath Maestro for workflow orchestration, fraud detection agents, clinical documentation agents, lead qualification agents, and AI code review agents.<\\\/span><\\\/p>\"}},{\"@type\":\"Question\",\"name\":\"What is an autonomous AI agent?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"<p><span style=\\\"font-weight: 400;\\\">An autonomous AI agent completes tasks without human prompting at every step. Most production agents in 2026 are semi-autonomous, automating routine steps while requiring human review for high-stakes, uncertain, irreversible, or regulated actions.<\\\/span><\\\/p>\"}},{\"@type\":\"Question\",\"name\":\"What is the difference between a simple reflex agent and a learning agent?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"<p><span style=\\\"font-weight: 400;\\\">Simple reflex agents follow fixed condition-action rules and do not change their behavior. Learning agents improve through experience and feedback. For example, a basic spam filter uses fixed rules, while a learning filter adapts based on how users classify messages.<\\\/span><\\\/p>\"}},{\"@type\":\"Question\",\"name\":\"What are examples of agentic AI in business?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"<p>Examples include Salesforce Agentforce for CRM automation, UiPath for agentic workflows, Microsoft Copilot Studio for enterprise workflows, GitHub Copilot Workspace for multi-file coding tasks, ServiceNow AI Agents for IT automation, and DianApps Orby for multi-step enterprise automation.<\\\/p>\"}}]}<\/script>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/div>\r\n\t\t\t\t\t\t<\/div>\r\n\t\t\t\t<\/section>\r\n\t\t<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Key Takeaways:\u00a0 AI agents range from simple rule-based systems to complex multi-agent architectures with very different capabilities. The seven types include simple reflex, model-based reflex, goal-based, utility-based, learning, hierarchical, and multi-agent systems. Learning agents improve through experience, while hierarchical and multi-agent systems coordinate multiple tasks or specialized agents. The right agent type depends on input [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":21821,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_yoast_wpseo_meta-robots-noindex":"","_yoast_wpseo_meta-robots-nofollow":"","_yoast_wpseo_canonical":"","_yoast_wpseo_opengraph-title":"","_yoast_wpseo_opengraph-description":"","_yoast_wpseo_opengraph-image":"","_yoast_wpseo_twitter-title":"","_yoast_wpseo_twitter-description":"","_yoast_wpseo_twitter-image":"","_wp_applaud_exclude":false,"footnotes":""},"categories":[1622],"tags":[2547],"class_list":["post-21819","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","tag-types-of-ai-agents"],"featured_image_src":{"landsacpe":["https:\/\/dianapps.com\/blog\/wp-content\/uploads\/2026\/09\/Types-of-AI-Agents-1140x445.png",1140,445,true],"list":["https:\/\/dianapps.com\/blog\/wp-content\/uploads\/2026\/09\/Types-of-AI-Agents-463x348.png",463,348,true],"medium":["https:\/\/dianapps.com\/blog\/wp-content\/uploads\/2026\/09\/Types-of-AI-Agents-300x169.png",300,169,true],"full":["https:\/\/dianapps.com\/blog\/wp-content\/uploads\/2026\/09\/Types-of-AI-Agents.png",1536,864,false]},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>7 Types of AI Agents: Explained With Real Examples (2026)<\/title>\n<meta name=\"description\" content=\"Know the 7 types of AI agents, from simple reflex and goal-based agents to learning, hierarchical and multi-agent systems, with real-world examples.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dianapps.com\/blog\/types-of-ai-agents\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"7 Types of AI Agents: Explained With Real Examples (2026)\" \/>\n<meta property=\"og:description\" content=\"Know the 7 types of AI agents, from simple reflex and goal-based agents to learning, hierarchical and multi-agent systems, with real-world examples.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dianapps.com\/blog\/types-of-ai-agents\/\" \/>\n<meta property=\"og:site_name\" content=\"Learn About Digital Transformation &amp; 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