In a definitive shift for the future of artificial intelligence, Google DeepMind has officially signaled that its flagship Gemini model is moving beyond the limitations of a traditional chatbot. Koray Kavukcuoglu, Senior Vice President and Chief AI Architect at Google DeepMind, recently outlined a transformative roadmap that repositions Gemini as an "AI agent"—a system designed not merely to provide answers, but to execute complex tasks, navigate software environments, and collaborate alongside human users.
This strategic pivot aligns with the vision of Google CEO Sundar Pichai, who has repeatedly identified "agentic AI" as the cornerstone of the future of search. As the industry moves away from simple prompt-response interactions, Google is banking on the idea that the true value of AI lies in its ability to take action on behalf of the user.
The Core Transition: From Passive Model to Active Partner
For years, the development of Large Language Models (LLMs) focused on the mastery of language—predicting the next token to provide the most accurate or creative textual response. However, Kavukcuoglu suggests that this era of "passive" AI is nearing its end. The new frontier is "agentic AI," where the model serves as an active participant in human workflows.
Coding as the Gateway
The catalyst for this evolution, according to Kavukcuoglu, was the pursuit of high-level coding capabilities. By training models to write software, Google researchers realized that coding is the ultimate "gateway" to broader agentic utility. Software engineering requires more than syntax knowledge; it demands tool usage, logical planning, and the ability to navigate complex digital environments.
"We learned a lot in terms of understanding what it means to do coding, and not just coding," Kavukcuoglu explained. "It’s about software engineering—what it means to work with tools or work with the functions that people use every day." This realization transformed the model’s objective: instead of creating a better question-answer engine, the team began building a system capable of functioning as a digital colleague.
Chronology of Development: The Path to Gemini 3.7
The evolution of Gemini has not been a singular event but a series of incremental, research-heavy milestones. Kavukcuoglu described the progression from the initial Gemini 3.0 launch to the current iterations as a process of continuous, parallel-track learning.
- The Foundational Phase (Gemini 3.0): The initial focus was on establishing baseline competency in coding and logic. This phase provided the empirical data necessary to understand how models interact with external APIs and software environments.
- The Iterative Phase (Gemini 3.5): This period proved pivotal in teaching Google engineers how users actually interact with agents. The feedback loop during this phase shifted the team’s focus toward "agentic workflows"—the sequence of actions required to solve real-world problems.
- The Convergence Phase (Gemini 3.6 & 3.7): Recent iterations represent the culmination of over a year of research. Features deployed in the latest versions are not spontaneous breakthroughs; they are the result of long-term architectural experiments that were finally ready to be integrated into the model.
Kavukcuoglu notes that internally, the team saw the potential for 3.7 while still working on 3.6, illustrating the "parallel track" nature of deep learning research. The result is a system that feels more coherent and intuitive, as it integrates years of architectural refinements.
The Paradox of Innovation: Revolutionary Results, Conventional Methods
A fascinating aspect of Kavukcuoglu’s insights is his assertion that while the output of AI is revolutionary, the methods used to achieve it are largely consistent with established research paradigms. He suggests that the "revolutionary" label applies to the problems AI is now solving, rather than a radical change in the underlying architecture of training.
Why the Process Remains Consistent
Google DeepMind continues to rely on rigorous data curation, large-scale compute, and iterative fine-tuning. Kavukcuoglu argues that the "magic" isn’t necessarily a new invention in the training pipeline, but rather the adaptation of these models to a more demanding, ambiguity-prone environment.
In the early days of LLMs, the environment was static—a prompt was given, and an answer was returned. Today’s agentic environment is dynamic. It requires the model to:
- Infer Intent: Understanding the "why" behind a user’s vague request.
- Manage Ambiguity: Dealing with incomplete information by asking clarifying questions or making logical assumptions.
- Collaborative Execution: Working alongside a human in real-time, often requiring the model to adjust its strategy based on user feedback.
This shift in environment requires a different type of "intelligence" that is less about static knowledge and more about situational awareness and procedural reasoning.
Supporting Data: Confidence in Agentic Workflows
When asked about the competitive nature of the AI industry—where the "frontier" of capability shifts almost weekly—Kavukcuoglu maintained a sense of steady confidence. He acknowledged that labs will always oscillate in their ability to claim the "most capable" title, but he argued that Google’s advantage lies in its deep understanding of user-agent interaction.
The transition from Gemini 3.5 to the latest models has been defined by a refined ability to facilitate these agentic workflows. "I’m feeling very, very comfortable and good right now where we are," he stated, emphasizing that the process of building the model was, in itself, the most valuable learning experience. By observing how users collaborate with agents on complex tasks, Google has effectively moved the needle on what is considered "useful" AI.
The "Magic Wand" Question: The Quest for Intelligence
In a lighter moment of the interview, host Logan asked Kavukcuoglu what he would improve if he had a "magic wand"—a hypothetical scenario to bypass the energy and time costs of research.
His answer was characteristically fundamental: "If I had a magic wand, I would just make them more intelligent."
While this might seem like an oversimplification, Kavukcuoglu’s reasoning is profound. He suggests that intelligence is the "master key." If a model is truly more intelligent, it inherently becomes better at everything—coding, reasoning, collaborating, and handling nuance. Rather than chasing specific "features," the goal of Google DeepMind is the pursuit of a generalized intelligence that behaves more intuitively in every human context.
Implications: The Future of the Google Ecosystem
The transition to agentic AI has massive implications for the entire Google product suite. For decades, Google has been a company built on helping users accomplish tasks—whether that’s drafting an email in Gmail, managing data in Sheets, or finding a destination in Maps.
The Transformation of Search
The most significant impact will likely be felt in Google Search. If the future of search is "agentic," then the search engine of tomorrow will not just provide a list of blue links or a summary paragraph; it will perform the task the user was trying to accomplish. For example, rather than just showing a list of flight options, an agentic search could compare schedules, check personal calendar conflicts, and draft a booking request.
The Human-AI Partnership
This evolution shifts the role of the user from "searcher" to "manager." As AI agents take on the heavy lifting of digital workflows, the user becomes the overseer of the process. This creates a new paradigm of productivity where the limiting factor is no longer technical skill, but the user’s ability to define and direct the AI’s actions.
Final Thoughts
Google DeepMind’s move toward agentic AI is a calculated response to the maturing needs of the digital world. By prioritizing the ability to "take action" over the ability to "provide information," Google is positioning Gemini to be the engine of a new generation of software. Whether this strategy will allow Google to maintain its dominance in a landscape crowded with startups and competitors remains to be seen, but the intent is clear: the future is not in the chat, but in the collaboration.
