Agentic AI and Pay or Consent Models

One of the coolest parts of serving as editor-in-chief at Marketecture is that I get to collaborate with the smartest people in ad tech. I get to be the first to read and provide feedback on some of most useful and timely perspectives regarding the hottest topics in our industry. In other words, I get smarter just by doing this job. And people thank me for moving their commas to the proper places.

This past week, Ad Tech Explained and The Monopoly Report outdid themselves. If you don’t already subscribe to these publications, you should (and we put one click ads below so you can). But regardless, we wanted to show off some of their great work in this week’s Marketecture newsletter.

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First up, there was Trey Titone’s “Agentic Ad Tech Explained,” which is the deep dive everyone in our industry needs right now. I’ve written a lot about agentic AI in recent months, but I’ll confess that I only understood a fraction of its implications before reading Trey’s piece. You should check out the full piece here. It features a ton of original interviews and insights from execs at companies including FreeWheel, Swivel, PubMatic, Dstillery, Newton Research, and Gigi. And, of course, there’s a lot of what Trey does so well, which is breaking down complex topics in simple terms. Like he does in the excerpt below.

Agentic AI is the ability of a system to take independent actions toward goals, operating proactively rather than just reactively and adapting its behavior as needed to achieve them. An AI agent is a tool or system that interacts with users, software, or data to accomplish specific goals autonomously.

Agentic AI is like a person's ability to plan and act independently. An AI agent is the person applying those skills to get work done and achieve specific results.

It also helps to compare this to generative AI. Generative AI is a type of AI that creates new content—like text, images, code, or audio—based on patterns it learned from training data. Generative AI is a capability that agentic AI may use to produce outputs as part of achieving goals. An agent might use generative AI to write a personalized email or summarize a meeting.

The real magic is when all these AI concepts come together. Generative AI text output ushered in the ability to interface with an application via chat, which allows a user to set up agentic use cases via the same natural language input mechanism. It also allows an AI to produce results from those interactions in the form of text, images, or video. This combination of agentic AI plus generative AI is what will usher in a profound shift in the way we interact with technology.

AI agents can reason and figure out the best way to accomplish tasks for a user, but to do so, they need access to ad platforms and systems and an understanding of their available capabilities. That’s why we now have to touch on MCP. This term came up frequently in all my conversations.

Anthropic, the company behind the AI assistant Claude, introduced MCP, or model context protocol, last year in an attempt to standardize how AI assistants connect to other systems. The idea is that a system could host an MCP server that allows AI assistants to understand what tools are available and how they work, along with providing a natural language description of each tool.

Think of it like API documentation for humans today. APIs allow external systems to push or pull information to or from a platform. But to integrate your system into a platform, you have to understand what endpoints to call, what information to provide, and how to authenticate. This information would all be outlined in human-readable API documentation and implemented by engineers.

A chatbot or AI assistant can query an MCP server and ask for everything it can do, alongside natural language descriptions of what the tool does and the required information to accomplish that task.

Without an MCP or a similar standard, any agentic tool would have to build out a bespoke integration for every app it interacts with using legacy APIs. With MCP, this process could be mostly automated and enable any platform to support agentic workflows, allowing agentic solutions to scale rapidly.

Then we have the most recent Monopoly Report, written by our own Alan Chapell. Alan’s quite adept at offering perspectives on advertising policy, privacy, and antitrust matters without boring you to death or making you feel stupid. Last week, he applied this unique talent to the topic of pay or consent models, in which publishers require subscriptions if users are unwilling to provide their consent to cookies, tracking, profiling, etc. More specifically, he questions a recent NOYB report that critiques this model’s use among publishers and wonders why NOYB is weighing in on the topic at all. An excerpt doesn’t do the article justice, so read the full piece here. But here’s my favorite part below.

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NOYB provides a list of ways that publishers can generate revenue on page 16 of the report. They suggest that, among other options, publishers can also earn money through:

I don’t even know where to begin with this list. But if we’re looking to philanthropy, big tech platforms, and government funding as the future of newspaper monetization, then the publishing industry is in even bigger trouble than I thought. It’s also odd that “subscriptions” are top of the list given that the study makes a huge point of saying that subscription models are mostly unfair to consumers.

I’m not here to throw shade on NOYB. They are simply pushing for companies to adhere to the law. I might disagree with them on some of the nuances of the law, but I would hope we can all agree on one thing:

The EU consent model as applied to digital media is horribly broken and doesn’t serve publishers, advertisers, or data subjects.

Maybe if we could all agree that the model is broken, we could start taking steps to fix it.