While most people like giving gifts, plenty don’t particularly like shopping for those gifts — drifting through online search results, comparing 4.3 stars to 4.5 stars. But big tech is racing for a future in which artificial intelligence bots can shop for you. Among the buzziest tech this season is a new generation of generative AI agents — systems that can potentially do your shopping, as well as perhaps plan and book your next vacation or schedule a home repair.
Amazon reportedly hopes autonomous shopping is the future of its Rufus shopping assistant. Perplexity, another AI company, released a shopping agent for paying customers in November. And last week, Google announced Mariner, an AI agent prototype.
These agents have been top of mind for Chirag Shah, a University of Washington professor in the Information School, who studies generative AI search and recommendation systems, with a focus on useful, unbiased systems.
UW News spoke with Shah about what AI agents are and what might impede a near future where we simply upload shopping lists and unleash the bots.
What are AI agents and why is there a race at big tech companies right now to create and release them?
Chirag Shah: AI agents have been around for a long time. Essentially, these are computer programs that work autonomously. Users can give them some instructions and they’ll perform tasks for the users. It could be as simple as an agent that turns on lights. Or it could be as complex as an agent that drives your car for you.
Right now, because of generative AI, we’re able to do a lot of the things that the previous generation of agents weren’t able to do. A lot of organizations now see this as the next phase of generative AI, where systems can move beyond just generating information and can use that information for reasoning and for taking action — so they can function kind of like personal assistants.
How is this new generation distinct?
CS: There are tasks that we do that are tedious. So imagine if somebody were to observe you and see how you do certain things, and then replicate that. Then you can delegate the task to them. Now, if you can train generative AI to mimic human behavior in that way, it can help you with things you might do online: finding information, booking things, even shopping.
So are we just going to do our holiday shopping next year with these agents? If that isn’t in the immediate future, what’s standing in the way of it?
CS: Well, some people don’t want to delegate, because they actually get joy out of shopping. But if you find it tedious, it would be nice to have an agent that functions like a personal assistant. We’d say, “OK, I’m trying to buy shoes for my friend. Here’s my budget.”
What’s stopping us from doing that? First off, if you have this AI assistant, would you trust its judgment? Obviously, there are times when you can say, “OK, as long as it fits these criteria of this budget and this size, go for it.” But there are other times you may have more specific needs that you don’t realize until you are actually doing the task yourself. People discover what they like and don’t while they’re shopping, and so we haven’t been able to really mimic that with AI agents yet.
This new generation of browsing agents provides a way forward. The way I would browse and the way I would shop online would be different from yours. So my agent, which is personalized to my taste, could learn those things from me, and could do the kind of shopping that I would do. One of the things we’ll need to see is personalized agents.
Building that trust seems key.
CS: Yes, because there is a cost to making a mistake. Imagine this shopping scenario: You give the budget, you give the parameters and you get some outcome that you’re not happy with. And maybe you’re stuck with the item because the agent bought from a company that doesn’t take returns.
So there are costs to making mistakes, and you’re going to bear the cost, not the agent. You don’t have a lot of choices in terms of correcting this, besides not using the agent anymore.
What would it take for someone to be able to trust it? Users will perhaps start small and see that the agent will actually do the kind of things that would be agreeable to them, and then go from there. Ultimately, we will see these agents playing critical roles in sensitive domains like health care, finance and education. But we are not there yet.
In fact, one of the hardest problems to solve is scheduling. It’s time-consuming, and not everybody enjoys it. So what would it take for you to trust an agent to plan your holiday trip? What if it books the flight with this airline that you hate? What if it gets you an aisle seat when you prefer a window seat? There are so many things to figure out.
There’s no shortcut to trusting these systems. I don’t think anyone’s just going to come up with the most sophisticated system, and the problem is solved. We’ll have to build that recognition, that social awareness, that personal awareness and that trust.
What are some potential downsides to having these agents deployed at scale?
CS: One potential issue is bias. What if the agent has some embedded agenda that I’m not aware of, because this is being supported by, say, Amazon? Amazon is giving me this free agent that I can use for shopping, and it works great on Amazon, but what guarantee do I have that it’s not buying things that maximize Amazon’s profit margin? If I get an agent for free from my bank, how would I know that it’s not optimizing things just for the bank?
We haven’t figured out a lot of these issues that would fall under the responsible AI umbrella. But considering the progress that we have made so far, we will likely start having these kinds of capable agents soon.
For more information, contact Shah at chirags@uw.edu.