Generative AI has been a buzzword for the past two years now. But lately, you might have noticed another term popping up—AI Agents. All major AI companies, including Google, OpenAI, Anthropic, Meta, and even open-source models, have started moving in this direction. Many think 2025 will be the year of AI agents.
So, what exactly are these AI agents? And why is everyone suddenly talking about them? Let’s break it down step-by-step.
Table of Contents
What Are AI Agents?
Imagine an AI model that doesn’t just reply with text but takes action on your behalf. These actions can be as simple as booking flight tickets or replying to an email, or as complex as managing an entire customer support system or even automating a factory.
In simple terms, they automate tasks like Google Assistant or Alexa—but far more intelligently. For example, if you ask an AI agent to “Send an email to John summarizing my recent meeting”:
- It searches your notes or even transcribes meeting recordings to extract key points.
- It generates a summary.
- It personalizes the tone based on previous emails to John.
- Finally, it sends an email—possibly after a quick verification from you.
In contrast, Google Assistant or Alexa would require you to provide the subject and body of the email, leaving much of the task in your hands.
There are multiple types of AI Agents:
| Type of AI Agent | What It Does | Example |
|---|---|---|
| Simple Reflex Agents | React instantly based on fixed rules. | Automatic doors open when motion is detected. |
| Model-Based Reflex Agents | Use memory to handle situations with missing information. | Thermostats adjust temperature based on trends. |
| Goal-Based Agents | Plan actions to reach specific goals. | GPS calculates the fastest route to a location. |
| Utility-Based Agents | Pick the best action based on preferences and priorities. | Self-driving cars optimize routes for safety. |
| Learning Agents | Learn and improve from experience over time. | Email filters get better at spotting spam. |
| Multi-Agent Systems (MAS) | Work together with other agents to complete tasks. | Robots collaborate to assemble products. |
| Hierarchical Agents | Use different levels of control, with higher levels managing strategies. | Factory robots follow supervisors’ instructions. |
However, at the core, all AI agents automate tasks by making decisions, thinking through logic, learning from past interactions, and performing multiple steps to achieve a goal.
Sometimes, multiple AI agents even work together to solve larger tasks. For example, if you ask it to complete an entire web development project, one AI agent designs the page, another writes the content, a third programs the project, and another tests the code and output. Why? When AI agents specialize in specific tasks, they perform with greater precision. So it makes sense to combine multiple AI agents who will complete the task by talking to each other while also delivering better results.
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How Do AI Agents Work?
AI agents work by observing, planning, acting, and learning—similar to how humans handle tasks. Let’s break this process down with an example. Imagine you want an AI agent to organize your messy photo collection. You give it one simple instruction—sort photos by date and create folders for vacations, birthdays, and family events.
Step 1: Observe
The AI agent scans your photos, reads timestamps, and may use image recognition to detect faces or objects. It doesn’t just follow a pre-written rule; it analyzes patterns and identifies what’s important. Basically, what chatbots are doing these days, but here agent proceeds to complete the task.
Step 2: Plan
Once the AI gathers data during the observation step, it moves on to planning how to organize the photos effectively. It decides how to group photos—perhaps by location first, then by date, and then by themes like beaches or birthday cakes. At this stage, it doesn’t ask you to confirm every step; instead, it builds its own workflow to achieve the goal.
Step 3: Act
Once the plan is ready, it executes with actions. It creates folders, renames files, and organizes everything. If it detects duplicates, it may delete them or ask for confirmation. Missing dates? It might even make educated guesses based on patterns like faces or locations. AI Agents can work with real apps or gadgets either through APIs or simulating and clicks and taps just like humans.
Step 4: Learn
Finally, the AI learns from its own experience. If it places a beach photo under birthdays and you correct it, the AI remembers that preference for next time. The more it works, the better it gets at understanding your needs and how you think and work.
This cycle—observe, plan, act, and learn—makes AI agents smarter and more useful the more you use them. While I have explained here with a simple example, the same pattern will be used for various other functions.
Real-World Use Cases for AI Agents
- You can use AI agents to perform basic actions like the examples shown above. You can send emails or sort files. Google’s Project Mariner, OpenAI’s Operator, and Anthropic’s Computer Use do exactly that. However, currently, all these projects are in the beta phase.
- Running an online store? Instead of hiring people to handle customer support, you deploy an AI agent. It answers questions, tracks orders, and even processes refunds by connecting with payment systems. If a query is too complex or out of its permitted limits, it hands it off to a human—but only when needed.
- Acts as a software engineer. It can write code, debug programs, test software, and even deploy projects without human input. It can handle complex programming tasks from start to finish. Services like Devin are already doing that. eBay has internally developed a framework that utilizes multiple large language models for coding and marketing tasks.
- It can research on your behalf. Come up with everything it has to search, frame questions, and search deeper based on the info it gathered. It checks for all related subtopics and generates a report with all the collected info. Google Gemini’s new Deep Research feature is exactly that. Even their new Gemini 2.0 model is an agentic model (agentic models can not only talk to humans but also other AI agents dynamically).
- In healthcare, AI agents assist doctors by analyzing medical scans, identifying abnormalities, and even suggesting treatment plans. They process massive amounts of data faster than any human, helping doctors focus on critical cases.
Why Is Everyone Talking About AI Agents?
AI agents are making headlines these days because they can automate tasks and act similarly to human assistants. Examples include running businesses, managing warehouses or factories, automating smart homes, helping in healthcare, etc. It’s like J.A.R.V.I.S from IronMan.
On one hand, while the technology seems interesting, it can raise privacy and ethical issues. These AI Agents can access your private info on your devices or will they replace your jobs?
