Rich Sutton's Oak Lab: AI Agents That Learn on Their Own

The timeline: from Montreal to a Toronto garage

Richard Sutton is no newcomer to artificial intelligence. He co-authored the standard textbook on reinforcement learning with Andrew Barto. In 2024 he received the Turing Award, the most prestigious honor in computer science. Yet instead of retreating into academia, he chose to found a new startup: Oak Lab, based in Toronto.

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

This approach goes back to the roots of classic reinforcement learning. At the same time, it brings it into a modern infrastructure context. So this isn't just another iteration on existing models.

The technical issue: why deep learning "isn't enough"

Sutton has called current deep learning methods "weak and inefficient." That's a bold statement. It's worth understanding what he means in detail.

Large language models such as GPT, Claude and Gemini learn during training. After deployment, they stay static. They don't update their knowledge based on later interactions, apart from periodic fine-tuning. As a result, they need frequent human intervention to stay current and relevant.

An agent based on continual reinforcement learning, by contrast, adapts in real time. It learns from environmental feedback. Over time, it improves its performance without new centralized training cycles. This is the key difference Oak Lab intends to leverage. For a deeper look at the technical distinction between static LLMs and continual-learning systems, MIT Technology Review offers an up-to-date picture of the state of research.

Winners and losers in the new ecosystem of autonomous agents

The founding of Oak Lab reshapes the competitive balance in the industry. But not everyone comes out stronger.

Who gains ground: companies building flexible infrastructure that can integrate next-generation agents. Automation platform vendors that update their APIs to work with continual-learning agents will also gain a structural advantage.

Who risks losing ground: AI marketing solutions built on rigid workflows and pre-trained models that can't be updated. If the industry moves toward autonomous agents, closed architectures become a limitation. Likewise, marketing teams that depend on a single vendor today risk being locked into technologies that are already outdated.

According to Gartner analysis, autonomous AI agents will be built into the major enterprise platforms by 2027-2028. The direction Sutton points to accelerates that trajectory.

SHM Studio's take: what changes for day-to-day marketing

At SHM Studio, we follow the evolution of AI agents with a specific focus on the operational implications for Italian marketing teams. Our aim is to offer a practical take, not just an academic one.

The first impact is on content personalization. An agent that learns continuously can adapt messages, offers and formats to users' actual behavior. Not based on aggregated historical data, but on real-time signals. This changes the logic behind your digital marketing strategy and the production of SEO content.

The second impact is on campaign management. Today, optimizations on Google Ads and LinkedIn Ads happen on weekly or monthly cycles. With autonomous agents, optimization becomes continuous and adaptive. The marketing manager's role shifts: less manual execution, more strategic oversight.

The third impact is on customer intelligence. Agents that can interact and learn can build far more accurate predictive models of customer behavior. This strengthens the AI applied to marketing work we already offer our clients.

The work still in progress: Oak Lab's limits and unknowns

That said, we need to keep a critical perspective. Oak Lab is a brand-new startup. There are no commercial products yet, and no use cases validated at industrial scale.

Continual reinforcement learning poses real technical challenges. First, learning stability in real-world environments is hard to guarantee. Data privacy, which is critical in European contexts subject to the GDPR, also becomes more complex when the agent learns incrementally from user interactions. Italian companies will need to weigh the regulatory implications carefully before adopting these technologies.

Next, we'll need to see how Oak Lab plans to position itself against the big players: OpenAI, Google DeepMind, Anthropic. They're all working on agentic systems. Sutton's edge is his theoretical approach. But the distance between research and an enterprise product is often longer than expected.

Next moves: how to prepare today

For marketing and digital leaders at Italian companies, the practical question is: what should you do now, before these agents reach the market in commercial form?

First, it's worth mapping the repetitive marketing processes that currently require frequent human intervention. These are the natural candidates for agentic automation. Examples include handling replies on social channels, dynamically updating landing pages or continuously segmenting leads.

Second, build flexible data infrastructure. An autonomous agent is only effective if it has access to clean, structured, real-time data. Investing today in solid data architectures, integrated with your website and CRM systems, will pay off even in the short term.

Third, it's strategic to keep an eye on how existing platforms evolve. Google, Meta and LinkedIn will build agentic capabilities into their advertising platforms. If you already understand automated optimization, you'll be better placed to adopt the more advanced versions. Our SEO and digital marketing work already takes this trajectory into account.

Finally, train your internal teams on the difference between generative AI and agentic AI. These aren't just technical details. It's about understanding which type of tool fits which type of problem. That's why the SHM Studio blog will keep publishing practical analysis on these topics.

If you want to explore AI agents from a strategic angle, McKinsey's State of AI report offers up-to-date data on the pace of enterprise adoption worldwide. If you'd like to talk through these topics with the SHM Studio team, you can reach us through our contact channels.