Janani Sekar:
Hey everyone. Welcome to the fourth video in this Agentic AI series. This is the video where we're actually going to get into the weeds of the capabilities of AI agents. So I'm very excited. Let's dive right in. We've talked a lot about what transforms language models into agents. A lot of that is the ability to do multi-step tasks, also the ability to interact with external tools, to access real-time information, to search the internet. So let's prime ourselves with another example of a workflow with and without an agent. Suppose that we wanted to know unemployment last month and we asked an LLM that didn't have access to the internet. So think about this as GPT from say two years ago where it knew some information, but it couldn't cite its sources. We wouldn't know if that information was up to date or not or where it was coming from.

In the best case scenario, you ask a model, what was unemployment last month? And it says, "I don't know." The worst situation is when it tells you information that sounds plausible very confidently, but that information is just false. And that is what we call a hallucination. And that can be a pretty dangerous thing if you aren't aware that your model has hallucinated, you aren't aware that this model say doesn't have information in its knowledge base beyond 2023. With an agent, this problem gets mitigated because today's GPT has access to search and we would consider that to be an agentic capability. What GPT would do now if you asked it for unemployment last month is it would run a web search for current unemployment rate. It would access the Bureau of Labor Statistics website. It would look at what was unemployment as of January 2026, and it will send you that information.

It will return that information to you along with the link and the citation for where it came from so that you can go back and verify that that is true. And this really matters for research because timeliness is important, accuracy is important. Being able to cite our sources obviously all really matters. Here's a quick example of what a workflow for me looked like with and without an agent. I'm mostly writing code. I'm not doing a whole lot of querying for unemployment. But without an agent, if I had to write some code, I would ask a model to say maybe merge two datasets. It would write me some code. I would paste that into my editor. It would error out. I would paste the error into the model. It would try to fix it. I would put it back. New error, we go back and forth until it works.

It's not a bad process. It is a little bit faster than if I had to figure out how to write that code by myself, but honestly, not by a whole lot. With an agent, I can just drag and drop dataset one and dataset two along with a quick request to merge, and pretty quickly I'll get back my merged data. Now, if you wanted to be even better about making sure that this merge went well, you could add some color to this prompt, like we talked about in the previous module that might involve saying things like, "The key that you should merge on is this column," or, "Feel free to drop these rows of data, or if you encounter this error, address it in this way." But even without that, an agent should have the ability to see when it runs into an error and try to figure out the solution from there without being prompted by you, the user.

So we've talked a lot about agents and having access to tools and being able to connect to external applications. What is the whole space of tools that agents can access? The answer is that it is a very, very wide space that I couldn't even really begin to break down in its entirety. But as researchers, the two collections of tools that I wanted to flag for us are data tools, including access to APIs like the Federal Reserve, but also the ability to read CSVs and data files and query databases, and a second group of tools that we will call action tools, which involve things like writing, executing code, accessing files on your computer, running jobs on your compute cluster, generating figures, saving those figures to a directory, maybe even LaTeXing information for you, that type of thing. We've also talked a lot about agents as coding agents or coding tools.

I want to emphasize that agents can do a lot more than just write code. They can write in English, they can build websites and deploy websites. They can help us outline and tasks like presenting a literature review or putting together slides. This is a quick example of a similar workflow to the previous slide, but for developing a personal website, I'm not going to read out this dialogue, but again, feel free to pause and compare and contrast the left and the right side.

We'll finally talk about the architecture of an agent in more detail. This is a figure from module one where maybe it made a little bit less sense, but now that you have the technical jargon to understand what I say when I mean reasoning engine or API, let's tackle the figure on the right. We start with our prompt. This user says to this agent, which has access to the Qualtrics API, I want to survey to see if people are more likely to click on job posting A or job posting B. From there, this agent, which is powered by GPT5 is going to store in its memory the fact that this particular user has information on posting A and posting B. And from there, it is going to plan the structure of the survey to get information on which stimulus people like more. It's going to write those questions and call or make a connection to the Qualtrics API, where that survey is going to get published to your Qualtrics account.

From there, it's going to return a link or maybe some affirmation that that survey question was written to Qualtrics and give you something that is a lot closer to a finished working product. So the core of the agent, remember, is the orchestration engine, that is the LLM, and this is going to handle most of the tasks that the agent does, including planning which order to execute in, what tools it should be calling, if at all, how to interpret the results from that API or from that tool call, and how to handle errors and try again if that doesn't exactly work as planned. All right, the second part of the agent is, of course, the memory, and there are two types of memory that I want to call out here. So the first type of memory is just like humans, short-term memory. This is things that you might've talked to the agent about in that session.

So things like when you query data, make sure you're only giving me data after 2010, and also long-term memory, which are things that you can configure your agent to do. So when I say that my agent, my Claude Code desktop agent, for example, has access to my files on my computer and the documentation for my compute cluster, so this lives in the agent's long-term memory. Anytime I initiate a session with this agent, all of this gets loaded into its memory so that it doesn't have to be repeated by me over and over again. And then finally, remember there are a whole bunch of tools that agents have access to. I'll flag a few. Search we've already talked about, file writing tools we've already talked about, and coding environments themselves are also tools. This is how GPT today sometimes is able to run a simple Python script and tell you the results without you having to copy paste that into an external editor and run it yourself.

Are agents always necessary? What is the value add of an agent? Is every task going to benefit from having an agent? The answer is generally no. Here are a few cases I think where agents can add a lot of value over a standard language model. Anytime timeliness is important, anytime a workflow has multiple steps, anytime we need some sort of external tool, anytime we need to send or receive data, really, we should and can use an agent. But just because we can does not mean that we should. There are also plenty of examples I can think of where fancy agents are not necessary. This is anything that really involves technical questions that we're reasonably confident a model can help us with. You just want to be reminded of the equation for the standard normal CDF. That is something that I'm sure a language model like GPT or Claude can do without having an agentic connection to some math textbook.

For simple one or two lines of code, similar sort of thinking for quick verbal or grammar checks, also really no need for an agent, but I'll leave it to you to discern which of your use cases for which you want to use an agent versus just approach a regular language model.