What is agentic advertising?
Let’s dive into what agentic advertising means in practice, where it’s already showing up in programmatic, and what marketers should focus on right now.
First—what does “agentic” actually mean?
At its core, agentic AI refers to systems that can act independently to achieve defined goals within set guardrails. In advertising, that means tools that can evaluate signals, apply logic, and take action without waiting for manual input.
But here’s what matters: Agentic AI doesn’t replace the programmatic ecosystem. It expands it and creates new ways for every participant to operate with more intelligence and efficiency.
Digital advertising has always been multi-party. We have media owners, brands, agencies, DSPs, SSPs, data partners, and solution providers all working together. Agentic systems introduce new, model-driven participants into that environment.
The opportunity here isn’t to reinvent everything. It’s to modernize how we collaborate.
Where is agentic advertising showing up today?
There are three meaningful areas:
1. Workflow automation: Agentic systems help teams troubleshoot deals, generate reports, manage inventory, and optimize campaigns faster and more intelligently. Instead of navigating multiple dashboards and manually investigating delivery issues, buyers can interact with systems that understand context, surface insights, and recommend next steps. For marketers, this means fewer repetitive tasks, faster intelligence, and more time spent on strategy.
2. Agentic buying: This is where automation extends across the campaign lifecycle, from RFP response to campaign activation to optimization. Based on a marketer’s objectives and policies, agents could be positioned to structure and transact media buys. This space is still evolving, but the trajectory is clear: more automation, less friction, and smarter campaign configuration across platforms.
Over time, this has the potential to change how buying teams operate. Instead of spending time on manual execution, teams will focus on setting objectives, defining guardrails, refining models, and supervising automated systems.
3. Sell-side decisioning: With sell-side decisioning, agentic models can now exist right at the origin of the impression. Instead of routing intelligence downstream, where supply is filtered before it reaches a DSP, decisions can originate at the impression itself, which is achieved when models and data are containerized and computed together in real time.
Instead of sending requests across multiple external systems, models run locally. Much like the benefits of edge computing, this reduces latency, enables real-time intelligence that wasn’t previously possible, and shifts every participant into a shared computing environment. It sets up our ecosystem for the scaled application of inference at impression time. For buyers, this means more relevant bid opportunities, stronger match rates, and better performing campaigns that ultimately drive more outcomes at greater scale.
Agentic advertising requires industry standards
To unlock the full potential of agentic AI, shared standards and frameworks are essential. You’ve likely heard a few new acronyms. Let’s break some of them down.
- The Model Context Protocol, or MCP, is a standardized protocol that lets AI systems connect to external tools and data sources and interact in real time. Think of it as the next evolution of APIs, ensuring different applications can understand each other when accessing tools and data.
- The Ad Context Protocol, or AdCP, applies MCP to ad tech. It allows ad systems to interact with agents automating workflows between buyers and sellers. It’s focused on the second use case we covered earlier for agentic buying, and helps to structure and execute campaigns intelligently. It’s not replacing real-time bidding infrastructure—it’s optimizing how we engage with it.
- The Agentic Real-Time Framework, or ARTF, is an IAB Tech Lab industry standard focused on modernizing real-time auctions. This is focused on the third use case discussed earlier, sell-side decisioning. ARTF enables containerized, agentic logic to operate within OpenRTB environments. That means enrichment, identity, optimization, verification, and more can happen inside the auction environment. It expands what’s possible in RTB by evolving the systems the industry already relies on.
This is an ever-changing environment, and there’s a lot of ongoing discussion about how AdCP, ARTF, and other proposals will evolve.
What’s consistent across efforts is the need for shared standards. Without them, we risk fragmentation, slower innovation, and the return of silos the industry has worked so hard to dismantle. With interoperable standards, human-driven, model-driven, and agentic participants can operate on the same foundation. That’s how we modernize responsibly.
What marketers need to know about agentic advertising today
So what does all of this mean for you? You don’t need to chase every new acronym. Instead, focus on three questions.
1. Where can automation reduce friction in your workflows?
If your teams are spending hours troubleshooting delivery, reconciling reports, or manually adjusting deals, that’s an opportunity.
2. Where can real-time intelligence improve performance?
Ask your partners how they’re applying decisioning closer to the impression. Ask how they’re reducing waste upstream and optimizing for stronger outcomes. The closer intelligence gets to the impression, the stronger the signal—and the greater the performance potential.
3. Are your partners adopting and building on shared standards, and what standards are they betting on?
Innovation without interoperability creates complexity. Innovation built on shared standards creates scale. But, standards only work if everyone across the industry adopts them.
Finally, it’s critical to note that automated systems via agents are early. Nothing implemented by AI should go unchecked. We need human validation and governance over these tools, and we don’t see this changing for the foreseeable future.
The next phase of programmatic
Agentic advertising isn’t a reset of programmatic, and it isn’t just about automation. It’s the next phase of its evolution. It’s about what becomes possible when data, models, and execution converge across the open internet.
And when we all align on shared, industry-wide frameworks instead of operating in fragments, the open internet becomes bigger, faster, more flexible, and has the potential to be far more competitive with walled gardens.
Done right, this next phase builds a stronger, smarter, and more sustainable future.
Learn what the next phase of agentic AI and sell-side decisioning means for the future of programmatic.
