The pitch for agentic ad buying has been that agents plan, buy, and optimize media, relieving humans from having to do the grunt work. September has shown a more grounded reality: small budgets, tangible results, and plenty of humans still watching closely.
Apostra—which has pivoted from sustainability to agentic—lets AI agents handle tasks across media buying, like discovering, negotiating, and approving deals, to executing them across digital publishers, linear TV, and in-store audio.
Ad buyers can use Apostra to connect with other agents across media buying and selling with agentic connectors for major platforms live on the platform. Marketers can manage campaigns across Meta, Google, Pinterest, Reddit, Spotify, and ChatGPT.
Other adtech players too are assembling similar systems. The Trade Desk is building a central AI conversational interface that will route media buyers towards a growing roster of specialized agents. PubMatic AgenticOS launched in January, while Magnite has introduced a buyer agent and an agentic orchestration layer spanning buy- and sell-side functions. Kargo introduced Project Kera, an AI-backed planning and buying interface in Spring.
But September has seen more of a concerted agentic push beyond just media buying.
Runway, an AI video startup, is also getting into the ad business with Runway Ads, an AI agent that can create variations of existing ads, localizing them for Meta, Google, and TikTok. It then pulls the performance data.
At Dreamforce, Southwest Airlines told me its AI agents will soon handle more complex tasks, including completing a flight ticket purchase.
WPP opened Devon’s Point hub, which uses AI-powered tools to automate parts of the development and production process.
AI agents are beginning to move beyond experimentation and into execution across the advertising workflow. But the technology is still a long way from running advertising on its own.
In the short term: ad buys are ramping up
Apostra has already handled automated media buys for some “very large” brands totaling around $1.2 million over the last three months—admittedly a tiny spend, but a sign that advertisers are putting in real dollars behind agentic buying.
Red-light therapy brand Rouge Care is crediting AI agents with a fivefold return on ad spend, after spending $25,000 on CTV ads and driving over $125,000 in attributable sales. The awareness campaign was facilitated through PubMatic’s AgenticOS in the second quarter of 2026.
Still, no one is doing this at scale. Highly autonomous deployments represent less than 5% of all marketing use cases in 2025, according to Gartner. It predicts that by 2028, only 15% of agentic AI deployments will be highly autonomous.
For now, the question isn’t whether advertisers will put money behind agentic buying, but how far that spending can scale—and what infrastructure will ultimately support it.
In the long term: who is responsible when agents go rogue?
Apostra uses AdCP, a protocol developed by a group of adtech companies, including Scope3, that provides a common language for agents to interact. It’s among a handful of protocols within the ad industry, including IAB Tech Lab’s AAMP (Agentic Advertising Management Protocols) and Amazon’s Model Context Protocol.
The rise of multiple protocols could create fragmentation issues, making it harder to scale agentic advertising. If buyers and publishers end up backing different standards, the systems will need a way to communicate, or risk creating another layer of interoperability problems for an industry that is already notoriously fragmented.
Apostra—which pitches itself as a neutral meeting point for agents working with brands and publishers—doesn’t take a cut of media spend, as ad networks might. Instead, it’s positioning itself as a software-as-a-service, with a fee that doesn’t increase with the amount of ad dollars flowing through it. That could give advertisers a reason to favor a neutral coordination layer over platforms like Amazon or Meta, according to Andrew Frank, vp, distinguished analyst at Gartner.
But even if the plumbing works, there are hard questions about who is responsible for the decisions agents make.
“The problem is agents are supposed to make strategic decisions about budget allocations,” said Frank. “If agents reallocate, say $10 million from TV to creator inventory, and performance drops, who’s accountable? Agentic buying introduces this whole new decision layer whose recommendations may not be fully transparent.”
While AI might reveal some of its reasoning behind a decision, there’s always a chance that that reasoning may not work out. For brands, that could keep budgets in the experimental bucket.
Finally, handing over control to the robots will be particularly difficult in premium advertising, where large deals are still heavily relationship-driven and where publishers and buyers have spent years building their own infrastructure, Frank said.
