12 AI agent examples that show what they actually do
"AI agent" gets stuck on almost every product now, so it is hard to tell what the word actually means. This article walks through 12 real AI agent examples, from the ones running inside Uber and Anthropic to the ones you can run on your own Mac. It is written for anyone deciding whether an agent, a chatbot, or plain automation is the right tool for a task. One honest note up front: we make Buttons, a screen-automation agent for the Mac, so we have a horse in this race, and we will point out where other tools beat ours.
Key takeaways
- An AI agent autonomously performs tasks by designing a workflow with the tools it can use, which is what separates it from a chatbot that only replies with text.
- Real agents already run inside companies you know: Uber's Finch answers financial questions in Slack, and GitHub's Copilot coding agent opens draft pull requests on its own.
- IBM sorts AI agents into five main types, from a simple reflex agent to a learning agent, and those types can be combined into a multi-agent system.
- The most useful agents for individuals do ordinary work, like clearing an inbox or filing receipts, not just enterprise analytics.
What is an AI agent, in one sentence
An AI agent is a system that perceives its environment, decides what to do, and then uses tools or APIs to carry out a multi-step task on its own. IBM puts it plainly: an agent autonomously performs tasks by designing workflows with available tools. The tools and the autonomy are the whole point.
How an agent differs from a chatbot
A chatbot answers you. An agent goes and does the thing. According to IBM, chatbots without tools, memory, or reasoning stay reactive, while an agent chains those pieces together to act. Here is the quick test: if the system can finish a task without coming back to you for each step, it is an agent. If it stops and waits after every reply, it is a chatbot.
The parts every agent shares
Every agent has three moving parts: perception, a decision-making loop, and actions it can take through tools. It reads the current state, picks the next move, runs that move through a tool, checks the result, and repeats until the task is done. The IBM taxonomy describes this loop across all agent types. Strip out the tools or the loop and you are back to a chatbot.
The main types of AI agents (with a quick example each)
There are five main types of AI agents, and IBM names them as simple reflex, model-based reflex, goal-based, utility-based, and learning agents. They climb in sophistication, and you can stack several of them into a multi-agent system.
Simple reflex and model-based agents
A simple reflex agent follows fixed if-then rules with no memory. A thermostat is the classic example: temperature drops below your setpoint, the heat turns on, nothing is remembered. A model-based reflex agent adds an internal picture of the world, so it can track what changed since the last step. The IBM breakdown uses this same split between rule-only agents and ones that keep state.
Goal-based, utility-based, and learning agents
These three plan instead of just reacting. A goal-based agent works backward from a target to choose its steps, the way a GPS app plots a route to your destination. A utility-based agent weighs trade-offs to pick the best option among several, not just any option that reaches the goal. A learning agent improves from feedback over time. IBM lists all three inside its five-type framework.
Multi-agent systems
A multi-agent system is several agents working together on one job, often with a lead agent handing pieces to sub-agents. IBM notes that all five agent types can be deployed together as part of a multi-agent system. Anthropic's research feature, covered below, is a working example of this pattern.
Real AI agent examples from companies you know
These are agents in production at named companies, not demos. Each one does a specific job with real tools, and each one shows a different shape an agent can take.
Uber's Finch financial data agent
Finch is an internal Uber agent that lives in Slack and turns plain-language questions into real financial data. Uber's engineering team describes Finch as an AI agent integrated directly into Slack that transforms natural language queries into structured data retrieval, delivering secure, real-time financial answers. Instead of writing SQL across several systems, a finance analyst just asks.
Anthropic's multi-agent research feature
Anthropic's Research feature uses one lead agent that spawns several sub-agents to search in parallel. In Anthropic's own testing, the lead agent spins up 3 to 5 subagents in parallel, and that design cut research time by up to 90% on complex queries in their internal evaluation. Treat that number as Anthropic's own result rather than an independent benchmark, but the architecture is a clear multi-agent example.
Delivery Hero's product knowledge base agent
Delivery Hero built an agentic pipeline to clean up its product catalog automatically. Its tech team explains that Delivery Hero Quick Commerce automates product attribute extraction and title standardization using agentic AI with LLMs. One agent pulls structured attributes, another writes standardized titles, so catalog work that used to be manual runs on its own.
Intercom's Fin support and voice agent
Fin is Intercom's customer-support agent, and it now works across chat, email, and voice. Intercom reports that customers see meaningful autonomous resolution, with one customer citing a 50% resolution rate with Fin in Intercom's own case studies. Read that as a vendor-reported figure, not an audited industry average, but the agent resolving half of conversations without a human is the notable part.
Coding agents that open pull requests
Coding agents now take a task and open a pull request on their own. GitHub's Copilot coding agent went generally available in September 2025, and GitHub describes it this way: delegate a task and Copilot will open a draft pull request and work in the background in its own environment. A human still reviews and merges, which is the checkpoint that keeps it safe.
Everyday and personal AI agent examples
The agents that matter most for individuals are the ones that do boring, repetitive chores on your own machine. These do not query a data warehouse. They click, type, and move through the apps already open on your screen.
Screen-automation agents on your desktop
Screen-automation agents operate your computer the way a person does, by controlling the cursor, clicking buttons, and typing. Anthropic launched Computer Use in October 2024, teaching Claude to use standard tools and software designed for people, though Anthropic called it experimental and error-prone at launch. OpenAI shipped a similar browser agent, Operator, in January 2025, which is now folded into ChatGPT's agent mode.
Our own product, Buttons, sits in this category, and here is the straight talk. Buttons drives your real Mac apps on screen, so there are no APIs to wire up and it reaches apps that have no integration at all. It is Mac-only, priced at $19.99 per month with a 7-day free trial, and it is not in the Mac App Store, so you download it directly from usebuttons.com. Two honest caveats: Buttons uses your machine while a run executes, and every step is supervised with checkpoints that pause before anything is sent, because a mis-keyed value is worse than no automation. If your workflow touches an app with a solid API, a tool like Zapier will often be the better fit.
AI agents vs. workflow automation, and where each wins
Workflow automation follows a fixed script, while an agent decides its own steps as it goes. Classic automation, like Zapier or macOS Shortcuts, is fast, cheap, and reliable when an app exposes an API and the steps never change. It breaks the moment a workflow touches an app with no integration, or the screen layout shifts. TechTarget draws the same line: a chatbot or fixed rule shares information, while an AI agent can undertake complex actions across multiple steps without human prompting.
So the choice is practical. Use scripted automation for a stable, API-friendly task you run the same way every time. Reach for an agent when the task spans messy apps, needs judgment, or changes often enough that a fixed macro would keep snapping.
Conclusion
AI agents cover a wide range, from a thermostat running if-then rules to a multi-agent research system spinning up sub-agents. The examples that hold up are the specific ones: Finch in Slack, Fin resolving support chats, Copilot opening pull requests, and screen agents clicking through apps that have no API. Pick the smallest tool that does your job. If that job is a handful of everyday chores on your Mac, a screen-automation agent is worth a look, and you can try Buttons free for 7 days.
Frequently asked questions
What are real examples of AI agents?
Real AI agents already run in production, and the useful way to tell them apart is by what each one actually does:
- Anthropic's Research feature coordinates several sub-agents that search in parallel, then combines their findings, per Anthropic's engineering blog.
- Delivery Hero's pipeline reads product data and writes standardized catalog titles without a human in the loop.
- Intercom's Fin resolves customer conversations end to end across chat, email, and voice.
- Screen-automation agents like Buttons click and type through the apps open on your screen, even ones with no API.
Each does a narrow job with real tools, which is what makes it an agent rather than a chatbot.
What are the 5 types of AI agents?
The five types of AI agents are simple reflex, model-based reflex, goal-based, utility-based, and learning agents, as IBM lists them. The practical way to read the list is as a ladder of what each can handle: reflex agents react to the moment, model-based agents remember state, goal-based and utility-based agents plan and weigh trade-offs, and learning agents get better with feedback. Pick the simplest type that fits your task, since a fixed rule beats an over-engineered planner when the job never changes.
Is ChatGPT an AI agent?
Not by default. Standard ChatGPT is a chatbot built on a language model, so it responds when prompted but does not act on its own. It becomes agent-like when it is given tools, and OpenAI's ChatGPT agent mode does qualify, since it can browse, fill forms, and complete multi-step tasks in a virtual computer. The plain rule: no tools and no autonomy means chatbot, tools plus a loop means agent.
Is Copilot an AI agent?
It depends on which Copilot you mean. Microsoft frames the Copilot interface as an AI assistant, while agents are specialized AI tools built to handle specific processes, calling the copilot the interface and the agents the apps. So the general Copilot chat is assistant-like, but Copilot agents built in Copilot Studio can retrieve data, take actions, and run business processes, which makes them true agents.
What is an example of an AI agent for personal use?
A good personal example is a screen-automation agent that clears repetitive chores on your own computer. Buttons is one: you describe a task in plain words, like triaging an inbox or filing receipts, and it drives your Mac apps to do it, pausing at checkpoints before anything is sent. It runs on macOS 14 and later, costs $19.99 per month, and downloads directly from usebuttons.com rather than the Mac App Store.
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