Janani Sekar:
Okay. Hello everyone, and thanks so much for being here. This is going to be the first video in this series of a total of eight video modules, and this first introduction is going to be a high level overview of AI agents, especially as they pertain to research. And I know that PREDOC.org encompasses a variety of different academic research disciplines, whether you're coming to this from an economics or a business or a social science background, hopefully there is some takeaway that you find relatable and useful. I've tried to keep a lot of what we discuss here as general as possible so that it is going to be useful to you regardless of the type of research you do day-to-day. My name is Janani. I am a pre-doc at the University of Chicago Booth School of Business. I work at the Center for Applied Artificial Intelligence here. And I wanted to shout out to folks at both of those places for their support and helping make this video series possible.

And also, of course, to PREDOC.org for being such a great collection of resources for all of us. So to dive right into what we're going to cover over the next eight video modules, we're going to be discussing agentic AI as it pertains to research, but also how to be careful and mindful as we're using these tools. So we'll be discussing how AI agents are different from the traditional chatbots or language models we already use. When people say AI agent, what does that mean? Is that different from GPT or Claude? How do we leverage or use these agents for research tasks like lit reviews and coding? What are some of the things to avoid when we're using agents? And so as researchers, we care about things like replicability and transparency and reporting the right results. And so it's important to make sure when we're using black box technologies or technologies where we don't know all of their inner workings, we are very clear about where and how we've used them.

And finally, whether you come to this series with extensive programming knowledge or a pretty limited programming background, you should be able to access and use these technologies equally. One of the nice things about being able to turn natural language into coded projects is that really it doesn't matter if you don't have a wealth of programming knowledge, you're going to be able to benefit from AI agents nonetheless. So for some brief motivation on how this series came about and why it's so important that we look at this right now, especially over the last year, we're seeing a lot of literature documenting the labor market changes that are occurring because of AI agents and language models. And we see that these things are driving productivity gains, particularly in computational and mathematical and educational disciplines. So pretty much the exact type of work that all of us do as pre-docs.

And that is very evident in this figure here. This is from the Anthropic Economic Index. This is a report compiled by the folks that make the language model Claude. And the source for this, as well as all of the references that I cite and mentioned throughout this presentation will all be available in the slides, which hopefully are being made available to you, but also they'll be visible in the reference slide at the end of the presentation, which you should be able to see at the end of video eight. Okay. So we've established that a lot of our peers are adopting these tools that our field is moving in this direction. So hopefully you feel and understand the importance of familiarizing yourself with these tools. We said that AI agents might be a little bit different from the language models like GPT and Claude that we're used to using on the day-to-day.

What exactly makes them different and what do folks mean when they say AI agents? Well, this is a nice definition and quote from the Kempner Institute for AI at Harvard, and I think it summarizes really well what AI agents do that typical language models can't, which is effectively that you can think of an agent as a layer on top of a language model that is able to execute complicated workflows that might involve multiple different steps without necessarily having a back and forth conversation with you at every step of the way to make that happen. And one of the key tools that allows agents to do this are APIs or we'll discuss these in more detail, but external connections to other applications or services that might allow an agent to do things like access your calendar, write a PDF, run some code and produce a figure, search the internet.

And these are the types of things that really power them up over language models or at least very simple language models like the GPT and the Claude that we were using two or three years ago. Okay.

So, for a little bit more evidence on what agents can do that language models can't, let's focus on what language models can do that we all know that they can do. Chances are that you've used a language model either to explain code or explain maybe a mathematical or statistical model. You can use language models to translate code. So if you have a replication package written in R, but you want to see that in MATLAB, that's something that you can ask a language model to do. You can ask them to debug code, make sense of errors. You can ask them to make tables and figures for papers. And so these are all things that other pre-docs that I know are using language models to do.

But agents with all of their PowerUps can go even beyond this. And so by being able to do things like search the internet, use external tools, act without step-by-step back and forth instructions, the workflows that we can use agents for change entirely. So one really great example that I always like to talk through is imagine that you wanted a personal website, a pretty helpful thing to have, maybe when you're applying to your PhDs. And if you had to ask a language model to do this, you might say, "I need a personal website." It'll give you some code, you'll look at that code, paste it into an editor. It may or may not work. You'll go back, it'll give you some more code, you go back and forth. With an agent that is optimized for building websites, all you have to say is build me a personal website, maybe give it some details about what you want included on this website, and it'll build it from end to end and in the end, deliver you a link, hopefully with a deployed website that you can click on and view as a user without having to figure out how do I publish this website after testing that the code works by myself.

So it just makes this process a little less tedious.

There are multiple different types of agents. There's going to be an entire video module in this series later discussing the different types of agents, but as one perspective on what the different types of agents out there are, this is a nice breakdown from OpenAI. They break agents down into agents that manipulate data, agents that take actions like accessing calendars or sending emails and texts, and agents that manage other agents. So when you have a research agent, you can think of that as an agent that is delegating tasks to other agents that it is in charge of. So an agent to search for literature, an agent to read the literature, an agent to synthesize through writing into paragraphs, and then report all of that back to the final user. And so this is just one way to think about the different types of agents out there.

Like I said, we'll see others in a little bit, but hopefully this orients you a little bit towards the type of things that these tools can do. Here's an example of the diagram of an agent. Again, we won't think about this too much, but as a user, you're going to pass a prompt in. Some stuff is going to happen and the result is going to come back out. For a concrete example of this, suppose that you want an agent to deploy a survey that you write to Qualtrics. As the user, you might say, "Hey, can you build me a survey to see if people are more likely to click on job posting A or B?" Your agent takes in that question, it processes that information, including doing stuff like storing some information, planning the survey structure, talking to external tools like Qualtrics, and then it's going to get back to you and say, "Hey, I've managed to deploy this survey. Here's a link to it."

And you can see that this workflow is a much more complicated involved workflow than what you might be able to accomplish with a standard language model.