Google DeepMind Says Gemini Is Evolving From Chatbot To AI Agent via @sejournal, @martinibuster
All Paid Media PPC News Social MediaAdvertising Video Advertising Columns Ask A PPC ExpertNEW PPC Pulse Webinar ChatGPT Ads Or GEO: What To Buy, What To Earn Audit where ChatGPT places your brand, then get the rule for when Ads win, when GEO wins, and when running both beats either one. Digital Marketing All Digital Marketing Analytics & Data Ecommerce Lead Generation Content Marketing Social Platforms Google YouTube Reddit LinkedIn TikTokNEW WordPress Other CMS Platforms SEJ Pro SEJ Pro: compare AI search notes in private Ask the SEJ experts you already read what AI, agents, and algorithm changes mean for your traffic. Private, human to human. $97/mo. SEJ Live Webinars Rundowns PodcastNEW Ebooks All Resources Webinar Turn AI Visibility Data Into an AEO Plan Live Ahrefs workshop: interpret your AI visibility reports, prioritize fixes, and win more citations. Free. Interview with head of Google DeepMind shows how Gemini is transitioning beyond providing answers toward AI that helps users get things done. SEJ STAFF Roger Montti September 2, 2026 ⋅ 6 min read SEJ STAFF Roger Montti Owner - Martinibuster.com at Martinibuster.com Bio Follow 2.6K READS Google published an interview with Google DeepMind SVP and Chief AI Architect Koray Kavukcuoglu. Kavukcuoglu shared that Google is increasingly viewing Gemini as an AI agent, not a chatbot. This is important because CEO Sundar Pichai has expressed that agentic AI is the future of search. Koray Kavukcuoglu shared that Google DeepMind increasingly sees Gemini as an agent, much more than just a chatbot or language model. What inspired this change is coding, that was the gateway to software engineering, tool use, and agentic workflows. Kavukcuoglu said that the goal is not really about creating a model that answers questions better. The focus now is to create something that can take actions on behalf of and alongside a human. “Going from the initial 3.0 launch, my reflection is we learned a lot in terms of understanding what it means to do coding, and not just coding, right? Like what it means to do software engineering, what it means to work with tools or work with the functions that people use every day. Basically turn this whole thing into an agent, from a model to an agent. And that transition, I think we were sort of walking around it, and that was the thing that we wanted to directly tackle. And of course, software engineering is the most critical domain and environment that you want your systems to be successful in because it’s at the root of many, many different things that you can do. During that time, we learned a lot in terms of how to train a model, how to train an agent that can actually code with you. And after that, I think the steps have started becoming faster. And like in any research project, there are many parallel tracks that is going on at the same time too. So what we are trying to do right now is combine all these learnings, of course, like add on top of them, understand what it means to do agentic actions and agentic workflows better, together with new architectural improvements, together with new ideas that we have been working on for a while. Many of these things that we have done in 3.6, 3.7 go back like a year or more, and then those start paying off and you converge them into the model. And it’s great to see the quality impact that we got from that. We were very excited in the run up to 3.7. When we were doing 3.6, of course, we could see what 3.7 could be or what the next step could be. And then it came together and internally we started enjoying that model A lot. So that’s the parallel track effect that you are going, that you are seeing.” Something else he talked about seemed contradictory in that he said revolutionary change is happening but what’s causing it is not revolutionary itself. He said that the basic steps for creating AI hasn’t changed nearly as much as the problems that AI is solving have. What it does is revolutionary but the way it gets there hasn’t undergone a revolutionary change. What changed, he said, is the environment in which AI is operating in which requires models to infer intent, deal with ambiguity and collaborate with humans. Later in the discussion Kilpatrick said that Gemini 3 reached the frontier, but that the frontier subsequently moved toward agentic coding and agent-like capabilities. Kavukcuoglu said still had things to learn about agentic actions and workflows, saying that the work surrounding Gemini 3.5 taught them about how people actually work with agents, and that he now feels more confident that Google has a better understanding of those kinds of interactions. “I think there are two things that we need to keep in mind. One is, by definition, in a very competitive environment, the frontier will always shift. There will be ebbs and flows of different things. And the cadence and the frequency of which lab is producing their most capable model is going to change. That’s #1. But #2 is a fair point. And we talked about that in the context of 3.5. I think we learned a lot about agentic actions and agentic workflows. And being able to bring that to life in a model, that’s the process that we went through. Where I’m feeling right now, I’m feeling very, very, very comfortable and good right now where we are and our capability of understanding what users need when they are working with an agent that is partnering with them on any kind of agentic task and workflow. The interviewer, Logan, then asked Kavukcuoglu to share what is the most important improvement in AI. “…if you could sort of wave your magic wand and sort of get the models to do anything and not need to spend a lot of time and energy, do you have anything on the top of that list that you would sort of want a capability, sort of better behavior on something?” “I think if I had a magic wand, I would just make them more intelligent. I think the models get more intelligent they do everything better, they do everything more intuitively, and I think that would be excellent.” Google DeepMind, and Google as a whole, is transitioning toward more than providing answers, it’s helping users accomplish tasks. All of Google’s products, from Gmail, to Google Sheets, to Maps, all help people accomplish tasks. Kavukcuoglu explained that AI models are conceptually becoming more than just chatbots, they are also becoming more agentic, and that reflects how everything at Google is changing, including Search. Add SEJ as a preferred source on Google Read Full Bio SEJ STAFF Roger Montti Owner - Martinibuster.com at Martinibuster.com I have 25 years hands-on experience in SEO, evolving along with the search engines by keeping up with the latest ... Learn how to connect search, AI, and PPC into one unstoppable strategy. Google's Gemini Decoded: Unveiling The Impact On SEO Looking Ahead To The Future – SGE, Gemini…And AGI? Phoenix: LLMs Revive Decade-Old Visions For Search Join 75,000+ Digital Leaders. Learn how to connect search, AI, and PPC into one unstoppable strategy. Learn how to connect search, AI, and PPC into one unstoppable strategy. In a world ruled by algorithms, SEJ brings timely, relevant information for SEOs, marketers, and entrepreneurs to optimize and grow their businesses -- and careers.
Source: Search Engine Journal
This article has been carefully curated and reformatted for educational and informational purposes. Full credit goes to the original publisher.
📚 Visit more helpful articles on Joab Peters Blog
No comments