Types of AI Agents and Their Uses Explained

It’s anyone’s guess that action-driven AI agents are going to drive the AI revolution, and the early signs are already here. From AI chatbots to AI agents that can read your emails and book appointments, we are about to enter the agentic era. So to better understand AI agents, I have explained different types of AI agents in great detail. In addition, I have listed leading companies and their current AI agents that are already available in the market. On that note, let’s begin.

1. Simple Reflex Agents

Let’s start with the most basic type of AI agents, Simple Reflex Agents. As the name suggests, Simple Reflex Agents perform actions based on the current information, following the ‘if-then’ condition. For instance, a thermostat turns on the heating when the temperature falls below a certain point. It basically performs an action when a condition is true.

However, there are several limitations with these kinds of AI agents. It only takes into account the current information (also called ‘percept’ or perception). It doesn’t remember the past temperature readings or consider future readings — it only acts based on the current temperature.

Simple Reflex Agents do not have memory and only work when the environment is fully observable — a state where all necessary information is available to make a decision. As a result, it doesn’t maintain an internal representation or model of the world.

2. Model-Based Reflex Agents

Next, the Model-Based Reflex Agents improve upon the Simple Reflex Agents. It maintains an internal representation of the world, hence, it has memory. Basically, these agents keep track of actions and how they impact the world, and update the internal model/representation. For example, a self-driving car while navigating traffic remembers vehicles’ positions even when they have moved from their current position.

Now, based on past observations and current information, it creates an internal representation of the world and takes a desirable action. It means that Model-based Reflex Agents can work in partially observable environments. Basically, it uses the internal model to predict the next action.

3. Goal-Based Agents

As the name suggests, Goal-Based Agents are outcome-driven, meaning it considers future actions that will bring them closer to the final goal. These agents can perform search, plan operations and consider different action sequences that will lead them to the desired goal. Goal-based agents approach actions, keeping future consequences in mind.

To give you an example, a GPS system that plans the route for you has to search and consider all paths to the destination. It considers all possible routes based on distance, duration, current traffic, etc. Now, based on this information, the Goal-based agent plans and chooses the best path to reach the goal destination.

4. Utility-Based Agents

Utility-based agents are a kind of Goal-based agents, but they are not tied to a specific goal. For instance, Goal-based agents only think in terms of goal achieved or not achieved — in binary. However, Utility-based agents consider different world states and evaluate different outcomes based on complex preferences, and then pick an action that maximizes their “utility”.

It works by assigning a numerical score to different action sequences and only picks the one that offers the highest utility score. Utility-based agents are designed in cases where the outcome is uncertain. To give you an example, the goal of an AI-powered trading system may be to maximize profit, but it also has to consider how much risk the user can take and what the current market conditions are.

Basically, Utility-based agents consider different preferences and evaluate outcomes before making the decision. Its aim is not just to achieve the goal, but to balance different factors to find the optimal action.

5. Learning Agents

Learning agents, by their very definition, are capable of improving performance over time by learning from past experiences. The best part about Learning Agents is that they can adapt to unknown environments and refine their action based on feedback. In Learning agents, there is a “critic” component that provides feedback as to how well the agent is performing.

To demonstrate an example, think of how spam filters work for emails. Spam filters have a basic set of rules initially; however, as you continue to mark emails as spam, the agent learns via the provided feedback (‘critic’) and adapts its behavior. Now, in the future, such emails are automatically marked as spam and moved to another folder.

6. Hierarchical Agents

Hierarchical Agents are those types of agents that break down complex goals into sub-goals. There are many complex tasks that require multi-step action and problem-solving. In such cases, the tasks are broken into smaller, manageable sub-problems, which are organized in a hierarchy. Now, lower-level agents are delegated to those tasks, and higher-level agents control the strategy and final output.

For instance, when you ask an AI robot to make dinner, the high-level agent plans and breaks down the task, like making pasta and sauce. Then those tasks are further broken down, like turning on the stove and adding water to the pan. Now, in this hierarchical way, the task is completed, from one layer to another.

7. Multi-Agent Systems

Finally, we come to Multi-Agent systems (MAS), which combine multiple autonomous agents to achieve a common goal. It allows multiple agents to communicate, interact, coordinate potential actions, negotiate, and cooperate with each other. In such systems, each agent works independently, and they have their own decision-making capability.

However, all agents must comply with the common multi-agent protocol to avoid conflicts and achieve the common objective. For example, in a supply chain system, there are multiple agents that keep track of the inventory, another agent files a report for procurement based on the inventory requirement, the logistics agent finds the best shipping route, and so on.

Current AI Agents by Leading Tech Companies

There are many types of AI agents already available in the market. OpenAI, Google, Microsoft, Anthropic, Salesforce, and many other companies are building AI agents and frameworks to effectively leverage the power of agentic AI. Here are some AI agents you can check out.

OpenAI

OpenAI is the first company to release a consumer-centric Operator AI agent. It’s a Computer-Using agent that automates tasks on the web. The Operator agent can interact with web browsers and can click, type, and scroll to perform actions. You can use it to fill forms, book flight tickets, order groceries, and more. That said, it’s not fully autonomous yet. You need to complete the payment manually and enter CAPTCHA whenever required.

Operator most likely falls within the Goal-based agent and Learning agent. It’s goal-driven and learns from interaction with websites. Other than that, OpenAI’s Deep Research agent performs complex multi-step research tasks and analyzes text, images, and PDFs to generate a comprehensive report. I would say it’s a combination of Goal-based, Learning, and Hierarchical agent that breaks down tasks into smaller sub-tasks.

Other than that, OpenAI says the latest o3 and o4-mini are not merely AI models, but “AI systems” which are agentic in nature. These new AI systems behave like an agent and can interact with a lot of tools like web search, Python interpreter, image analysis, and more. These are Model-based and Goal-based agents.

Finally, the latest Codex CLI tool by OpenAI, which allows developers to read, modify, and run code from the Terminal, is another type of AI agent. It can automatically fix bugs, build new features, and modify files. Again, this is a Goal-based agent, built with a Learning agent.

Google

So far, Google has only released the Deep Research AI agent on Gemini which works similarly to OpenAI’s agent. It can go to the web, plan what information it needs, and synthesize information to generate a comprehensive report on any subject. I would classify this agent as a Goal-based and Learning agent.

Next, Google has teased Project Mariner, which is currently in development. It works like OpenAI’s Operator AI agent and can automate tasks in the Chrome browser. It can analyze the active screen and perform actions on websites. Google says the agent is being tested with trusted testers, and it will be released in the near future.

Apart from that, Google has introduced a new Agent2Agent (A2A) protocol that allows multiple AI agents to communicate with each other. It’s not an agent, but a standard/framework that will enable Multi-Agent Systems (MAS).

Anthropic

Just like OpenAI, Anthropic has unveiled a Computer Use AI agent which is in beta, and it can interact with computer desktop environments. It can analyze the screen and click, type, and perform file operations. It’s not just limited to web browsers, but can also perform actions at the OS level. It goes without saying that this is a Goal-based and Learning agent.

Besides that, Anthropic recently released a Research tool with Workspace integration on Claude. It can connect to your Gmail, Calendar, and Drive, along with the web to perform research and extract insights. Similarly, Claude Code is an agentic coding tool that runs inside the Terminal. It understands the codebase and can edit files, run tests, and interact with Git as well. Both of them are Goal-based agents.

Lastly, Anthropic has developed the Model Context Protocol (MCP) which is an open standard to connect AI models with external data sources, allowing AI agents to work reliably on API-less services. While it’s not an agent, it enables the communication between AI models and other tools, websites, and data sources. You can learn how to set up MCP in Claude on Windows and macOS.

Microsoft

On the consumer side, Microsoft has announced many new AI agents for its Copilot chatbot. The Deep Research agent in Copilot can perform multi-step research to generate complete reports on given topics. Next, Copilot Actions can book tickets, make reservations, and purchase items from the web. However, it only works on partner websites.

Then, on the enterprise side, Microsoft recently announced the Computer Use AI agent in Copilot Studio. It can directly interact with websites and desktop apps to perform actions, and doesn’t rely on specialized APIs. Then Microsoft has unveiled a Security Copilot agent to assist with phishing alerts, data security, and identity management.

Microsoft has developed many Copilot agents for enterprise customers, and you can even build a custom AI agent for your workflow. You can get started on Copilot Studio and connect MCP servers, APIs, and external sources to automate tasks.

Salesforce

Apart from Microsoft, Salesforce has developed Agentforce for enterprise customers that can deliver customizable autonomous AI agents. Business users can build, deploy, and manage multiple AI agents on Agentforce to generate leads, improve sales, manage marketing, and more.

Salesforce claims that unlike Microsoft’s Copilot, Agentforce agents can autonomously perform actions based on events or predefined triggers. Agentforce agents can update records in a database, send emails, book meetings, resolve pending cases, etc.

So these are the types of AI agents you can explore and find the current AI agents available in the market. As we move forward, AI agents will become a central part of the online experience, be it on the consumer or enterprise side.