Oak Lab by Rich Sutton: AI agents that learn by themselves

The timeline: from Montreal to the Toronto garage

Richard Sutton is not a new name in the artificial intelligence landscape. He is the co-author of the seminal textbook on reinforcement learning, published with Andrew Barto. In 2024, he received the Turing Award, the most prestigious award in computer science. However, instead of retiring into academia, he chose to found a new startup: Oak Lab, based in Toronto.

The news was reported by The Decoder July 13, 2026. Sutton's move comes at a precise moment. In fact, the AI agent market is already crowded with solutions based on static LLMs. In contrast, Oak Lab is aiming for something structurally different: agents that continuously learn from interaction with the environment.

This approach draws on the roots of classical reinforcement learning. At the same time, it projects it into a modern infrastructural context. Therefore, it is not a simple iteration on existing models.

The technical bottleneck: why deep learning «isn't enough»

Sutton has described current deep learning methods as «weak and inefficient.» That's a strong statement. Therefore, it's worth understanding what he means in detail.

Large language models—GPT, Claude, Gemini—learn during the training phase. After deployment, they remain static. They don't update their knowledge based on subsequent interactions, except for periodic fine-tuning mechanisms. Consequently, they require frequent human intervention to stay up-to-date and relevant.

A continuous reinforcement learning-based agent, on the other hand, adapts in real-time. It learns from environmental feedback. Therefore, over time, it improves its performance without the need for new centralized training cycles. This feature is the substantial difference that Oak Lab intends to leverage. To further explore the technical distinction between static LLMs and continuous learning systems, the MIT Technology Review provides an updated overview of the state of research.

Winners and Losers in the New Autonomous Agent Ecosystem

The establishment of Oak Lab rebalances the competitive landscape in the industry. However, not everyone emerges strengthened in the same way.

Who gains positions: companies that are building flexible infrastructures capable of integrating next-generation agents. Furthermore, automation platform vendors that update their APIs to communicate with continuous learning agents will have a structural advantage.

Who risks losing ground: AI marketing solutions based on rigid workflows and non-upgradable pre-trained models. In fact, if the industry moves towards autonomous agents, closed architectures become a limitation. Similarly, marketing teams that today rely on a single vendor risk being tied to already outdated technologies.

According to the analysis of Gartner, by 2027-2028, autonomous AI agents will be integrated into major enterprise platforms. The direction indicated by Sutton accelerates this trajectory.

Reading SHM Studio: What Changes for Operational Marketing

In SHM Studio We are following the evolution of AI agents with specific attention to the operational implications for Italian marketing teams. Therefore, we want to offer a concrete, not just academic, reading.

The first impression concerns the Content personalization. A continuously learning agent can adapt messages, offers, and formats based on actual user behavior. Not on aggregated historical data, but on real-time signals. This changes the underlying logic of digital marketing strategy and the production of the SEO content.

The second impact concerns the campaign management. Today, optimizations on Google Ads e LinkedIn Ads occur on weekly or monthly cycles. With autonomous agents, optimization becomes continuous and adaptive. Consequently, the role of the marketing manager shifts: less manual execution, more strategic oversight.

The third impact concerns the customer intelligence. Agents capable of interacting and learning can build much more accurate predictive models of customer behavior. This strengthens the activities of AI applied to marketing which we already offer to our clients today.

The still-open construction site: limitations and unknowns of Oak Lab

Despite this, it is necessary to maintain a critical perspective. Oak Lab is a newly founded startup. There are no commercial products yet, nor industrially scaled validated use cases.

Continuous reinforcement learning presents non-trivial technical challenges. First of all, learning stability in real-world environments is difficult to guarantee. Furthermore, data privacy management – fundamental in European contexts subject to GDPR – becomes more complex when the agent learns incrementally from user interactions. Therefore, Italian companies will need to carefully evaluate the regulatory implications before adopting these technologies.

Subsequently, it will be necessary to understand how Oak Lab intends to position itself against the major players: OpenAI, Google DeepMind, Anthropic. They are all working on agentic systems. Sutton's difference is the theoretical approach. But the distance between research and enterprise product is often longer than expected.

Next moves: how to prepare today

For marketing and digital managers of Italian companies, the practical question is: what should be done now, before these agents arrive on the market commercially?

First of all, it's worth Map repetitive marketing processes that today require frequent human intervention. These are natural candidates for agentive automation. For example, managing responses on social channels, dynamically updating landing pages, or continuously segmenting leads.

Secondly, it is useful build flexible data infrastructure. An autonomous agent is only effective if it has access to clean, structured, and real-time updated data. Therefore, investing today in solid data architectures—integrated with the website And with CRM systems, it's a choice that will pay off even in the short term.

Third, it's strategic monitor the evolution of existing platforms. Google, Meta, and LinkedIn will integrate agentic capabilities into their advertising platforms. Those already familiar with automated optimization logic will have an advantage in adopting the more advanced versions. Our activities SEO e digital marketing already take this trajectory into account.

Finally, it is advisable form internal teams on the difference between Generative AI and Agentive AI. It's not about technical details. It's about understanding which type of tool is suitable for which type of problem. For this, the SHM Studio Blog will continue to publish operational analyses on these topics.

For those who want to delve deeper into the topic of AI agents from a strategic perspective, the report by McKinsey on the State of AI offers updated data on global enterprise adoption rates. Those who wish to engage with the team SHM Studio You can do this on these topics through our contact channels.