Future of Marketing Briefing: Agentic advertising is closer than you think and further than you hope

Autonomous agent-to-agent trading could reach scale within six to 12 months, but industry standards must keep pace with the technology. Technical capability exists within major open internet advertising infrastructure, while fiduciary requirements create the main hurdle. Autonomous financial decision systems need deeply specific guardrails rather than add-on controls. A distinction exists between structured automated workflows managed through conversational interaction and genuine agentic agency. Real autonomy requires reinforcement learning and advertising-specific guardrail work tied to compliance and decisioning. Marketing decision complexity involves many variables, and scaling changes the environment because actions like buying, targeting, and optimizing alter the conditions the system was trained to predict.

"Autonomous agent-to-agent trading could hit scale within six to 12 months. But there's a catch: the industry's standards need to keep pace with the technology. That's what Jamie Allen, Nvidia's director of AI for sports, ad tech and streaming media told me earlier this week when we caught up. From his vantage point - Nvidia sits inside the infrastructure of the major open internet advertising platforms - the technical capability is there. The bigger hurdle is fiduciary. Any system making autonomous financial decisions needs guardrails that are deeply specific, not just bolted on, and right now most of what's being called "agentic" in advertising doesn't clear that bar."

"Allen was deliberate about that distinction. There's a meaningful difference, he continued, between a well-structured automated workflow managed via conversational interaction and genuine agency - and the industry is conflating the two, much like it did with generative AI before it. Getting to real autonomy requires reinforcement learning and guardrail work that's very specific to advertising's compliance and decisioning requirements. "There is that requirement to make sure that all the checks and balances are being put in place before those things are really highly activated, and that's where that next level of consideration of how much agency do we give becomes very important," said Allen."

"Understanding why those checks are so hard to define in marketing specifically requires understanding two things: the first is decision complexity - most marketing problems operate across a smorgasbord of variables simultaneously, a scale at which both humans and most AI systems become unreliable without the right support beneath. The second is what happens when you deploy at scale: marketing exists to change behavior, which means the moment a system starts acting on its prediction - buying, targeting and optimizing - it shifts the very environment it was trained on. The past stops predicting"

Read at Digiday