When millions of AI agents meet

**Hannah Fry** (0:00)
Welcome back to Google DeepMind, the podcast. Now, not very long ago, an AI assistant essentially meant a large language model. You ask it a question, it gave you an answer, but it couldn't go off and perform tasks on your behalf.
All of that is changing with the advent of AI agents. While Google DeepMind has this long history of developing agents stretching back to reinforcement learning in games, for most of us, they hadn't really arrived. And then we saw open source tools like OpenClaw released into the wild. And at Google, a new generation of agentic tools is here, including GeminiSpark and AntiGravity. But what happens when millions of AI agents are not just working for us, but transacting, negotiating, delegating to each other? Do we end up with a new kind of economy, a new route to AGI? And how on earth do we keep all of that safe?
Well, one of the people trying to answer these questions is Nenad Tomashev, Senior Staff Research Scientist at Google DeepMind. Nenad, thank you so much for joining me.

**Nenad Tomasev** (1:05)
Very happy to be here.

**Hannah Fry** (1:07)
I think we should probably start at the beginning here, because for people who have only played around with large language models, could you describe to us the difference between that experience and acting with an agent?

**Nenad Tomasev** (1:18)
Yeah, no, definitely. I think this is becoming one of the main trends we're seeing this year.
And it's interesting because agents are not a new concept. It's something that we've been looking at in the context of AI for a long time. Even before large language models, we had agents operating in simulated 3D environments, going on collecting items, completing some tasks. This was back in the days we were really prioritizing actioning in the world as a way of manifesting intelligence. Now, similarly nowadays, I guess you could say that the main conceptual difference between just a language model and an agent is that an agent observes a state of the world and performs an action, makes an action in the world, in the environment that it's given, whereas a language model just gives you a continuation, a reply to a prompt, to query. Now, obviously, agents that we use nowadays, they use large language models under the hood, so the two concepts are not completely disambiguated. It still is the large language models formulating the actions. It's just that there is a harness around it made to enact the changes once they have been proposed.

**Hannah Fry** (2:22)
But it has a lot more autonomy to chain decisions together, I guess.

**Nenad Tomasev** (2:26)
Correct. And I guess this is ultimately the motivation, right? Because you could do everything, most things that an agent can do manually, painstakingly, by interacting with the language model very many times, and you guiding the whole process. Whereas an agent instantiates this harness that automates some of that away and gives you less work and just a language model or the agent more autonomy to complete tasks. So if you want something done that takes multiple steps, that agent can make a plan and take actions on all of those steps, obviously requiring approval or human input for those actions that are, let's say, more sensitive or more likely to go wrong.

**Hannah Fry** (3:09)
How is it different, though? I mean, if you're used to interacting with a large language model by now, what would it be like interacting with an agent?

**Nenad Tomasev** (3:16)
In many ways, similar. Your interaction interface is somewhat similar. You're still talking to the agent in a way in which you'll be talking to a language model. There is a language model there.
But because the agent is doing more things for you, you're more in the position of a decision maker to review and approve. And then once you've approved, the agent is going to do various things, purchase tickets, message your friends if you're organizing a party. And meanwhile, you can put something on Netflix, hopefully, and relax a little bit.

**Hannah Fry** (3:48)
The example I was thinking of was if you were, I don't know, planning a wedding, for instance, you'd go into a large language model and it would, like, tell you a list of caterers, give you a suggested list of venues, but actually you would have to do all the emailing yourself. But an agent, I mean, it would be much more useful, really, in that kind of scenario.

**Nenad Tomasev** (4:06)
100%, especially because agents are given access to all of these tools, so you could, you don't have to, you could give an agent access to your Gmail and give it permissions to send out an email.