This article is part of an ongoing series by Appliscale’s Head of Engineering, Damian Naglak, exploring the new IAB agentic framework and what these shifts mean for the future of ad tech and cloud infrastructure.
With the official rollout of the IAB Tech Lab agentic AI roadmap, the programmatic advertising ecosystem is undergoing a massive shift. Having followed the initial release and demonstrations closely, what stands out immediately is how pragmatic their approach is.
Instead of forcing the industry to adopt entirely new architectures from scratch, the IAB agentic framework focuses on wrapping existing standards in MCP (Model Context Protocol) and Agent2Agent protocols.
What does this mean for ad tech platforms? It means OpenDirect becomes Agentic Direct, RTB becomes Agentic Bid, the Deals API becomes Agentic Deals, and so on. Ultimately, AI agents get a common language to communicate, while the industry gets to keep the infrastructure that already works.
The IAB Tech Lab is also shipping these updates extremely fast. We are already seeing six standards getting agentic extensions, reference implementations that are demo-ready, and a dedicated agent registry to support the ecosystem.
How does the IAB agentic AI roadmap streamline programmatic workflows?
To understand the practical impact of AI agents in programmatic advertising, let’s take a closer look at a buyer and seller agent use case based on the IAB’s early models.
Imagine an EV launch campaign brief goes into the system. It includes budgets, flight dates, and vague audience descriptions like “eco-conscious tech-forward consumers age 25 – 54.”
From there, the buyer agent takes over the heavy lifting. It automatically:
- Structures the brief and maps the vague audience descriptions directly to IAB taxonomy segments.
- Queries seller agents for pricing and availability.
- Gathers and processes responses from various publishers and DSPs.
- Matches audience coverage across the available inventory.
- Builds a comprehensive execution plan, including PG lines, PMP deals, and performance campaigns.
- Asks a human for final approval, and then executes the plan.
The execution phase directly hits live systems. In practical applications, lines appear in GAM just seconds after approval. PMP deal IDs are seamlessly issued and returned to the buyer agent, and DSP campaigns are instantly created with the attached deals.
The entire flow – from reading a PDF brief to booking live media – takes minutes instead of days. What used to require tedious dashboard navigation, manual deal coordination, and copy-pasting data between disconnected systems simply becomes a single AI agent conversation, with human approval acting as the final decision point.
Why standardization in the IAB agentic framework matters
At Bedrock Platform, my team and I have been actively building agents for supply discovery, campaign optimization, and deal troubleshooting. Because of this hands-on engineering experience, seeing the industry align on the standards within the IAB agentic AI roadmap matters more to me than any single platform’s implementation.
The core question for ad tech was never whether AI agents would enter the programmatic space. The real question was whether they would speak the same language when they got here. Thanks to this new framework, it is clear that they will.
Want to stay updated on the latest in agentic AI, ad tech, and cloud engineering? Follow Appliscale on LinkedIn and connect with me directly at Damian Naglak’s LinkedIn. Stay tuned for the next article in our IAB agentic framework series!
