AI agents are programs that can take a big, high-level goal and actually go out and accomplish it on its own. It doesn’t just answer a question; it performs a task from start to finish.
The field is moving incredibly fast, but a few have really stood out to me as being functional and impressive. It’s worth noting that many of the most powerful ones are currently found in paid tiers of services, as they require a lot of computing power.
ChatGPT Advanced Data Analysis (formerly Code Interpreter)
This is probably the easiest one for most people to access if you have a ChatGPT Plus subscription. While OpenAI is working on more advanced agent-like features, the Advanced Data Analysis tool is a primitive form of an agent. You can upload files—like a PDF, a spreadsheet, or a set of images—and tell it to perform a task.
For example, I uploaded a CSV file of my personal finance exports and said, “Categorize my spending for the last three months, identify the top three categories, and create a simple bar chart to visualize it.” It wrote its own Python code in the background, ran it, analyzed my data, and produced the chart and a summary. It performed a series of actions (coding, calculating, chart-making) to achieve a goal I set. It’s a limited but incredibly powerful glimpse into the concept.
Google’s Gemini in Workspace (Especially Gemini Advanced)
Google is baking agent-like capabilities directly into its products, and it’s one of the most practical implementations I’ve seen. In Gmail, Docs, and Sheets, Gemini can now take action.
You can highlight a long email thread in Gmail and tell Gemini, “Summarize the key action items and draft a response confirming I’ll handle points one and three.” It will read the entire thread, create the summary, and write the email. In Google Sheets, you can ask it to “Find and highlight all the cells where the projected revenue is more than 10% below the actual revenue.” It will then go through your spreadsheet and do exactly that. It’s not just answering; it’s manipulating the software based on your command.
Microsoft’s Copilot (Especially with Copilot Studio)
Microsoft is taking a similar approach to Google, but deeply integrating its Copilot AI into the Windows operating system and the entire Microsoft 365 suite. The real agent-like power comes when businesses use Copilot Studio to create custom agents.
A company could build an agent that has access to its internal HR policies, project management software, and email system. An employee could then tell the agent, “I need to schedule a complex project kickoff meeting. Find a time when the entire core team is free, book Conference Room B, create a project plan template in SharePoint, and send an invitation to the team with the agenda attached.” The agent would have the permissions and knowledge to execute all of those steps across different applications.
Devin (by Cognition AI)
This one is a glimpse into the future and is more specialized, but it’s too impressive not to mention. Devin is marketed as an AI software engineer. You give it a prompt like, “Build me a simple website that has a contact form and connects to a database,” and it will literally get to work.
It will write the code, set up the files, run the code to test it, debug errors it finds, and then deploy the final product. People have given it real jobs from freelance programming websites, and it has completed them start-to-finish. It’s a pure example of an agent: a goal goes in, and a finished product comes out, with the AI handling the entire complex workflow in between.
The Reality Check
As amazing as this all sounds, it’s not a perfect technology yet. These agents can still be slow and expensive to run because they are doing so much step-by-step thinking. They can also get stuck or make mistakes in their reasoning, just like a human might. You still need to be the manager and review their work before it goes out the door.
But the shift in concept is what’s important. We’re moving from tools that we command directly to partners we can delegate to using AI automation. It’s the difference between having a powerful calculator and having a junior analyst who can take a question, run the numbers, and come back with a report.
I find myself getting genuinely excited to see what new task I can offload next. It feels less like using a piece of software and more like collaborating with a very fast, very diligent intern who never sleeps. The key is to start experimenting now, on a small scale, to get a feel for this new way of interacting with computers. It’s probably going to become a normal part of how we all work sooner than we think.