Going into 2026, all eyes are on agentic AI media buying, and for good reason. With the latest advances in this space, our industry is poised to finally deliver the level of transparency and control advertisers have been chasing since the early days of programmatic advertising. 

At a basic level, agentic buying replaces manual decision-making with goal-driven software agents that act independently on behalf of buyers and sellers, all without needing to log in to a different UI or point solution. Traditional programmatic buying relies on humans configuring rules inside DSP interfaces, reacting to reports after the fact, and worse, problem-solving delivery or performance issues without all of the data needed.  

Agentic buying flips that model. Instead of managing levers and dashboards, advertisers define outcomes with guardrails and then let agents continuously evaluate every available option in real time. These agents can ask more questions, process more signals, and enforce stricter requirements than any human team could. This turns media buying from a reactive workflow into an automated, decision-driven system designed to optimize for transparency and results from the start. 

This doesn’t have to be another black-box optimization tool we have come to expect in the programmatic landscape. Agentic buying, if done right, should allow us to break that mold with full transparency.  

For years, advertisers have accepted that the supply chain is too complex, too fragmented, or too distributed for full clarity. But with agents acting on behalf of both the buy side and the sell side, it becomes possible to process every layer of data in real time. AI agents can transact using a significantly higher number of inputs, optimizing the path to outcomes without relying on legacy interfaces or an ever‑growing roster of intermediaries. 

What once felt like searching for a needle in a haystack of hidden fees is about to become automatic, directing more spend toward working media. If a founding principle of the United States was around no taxation without representation, then buyers should be able to require that any company adding a fee in the bidstream should have to prove the value of that fee.

This shift is a highly practical one. Simply put, agents can do what humans and previous machine‑learning applications could not: process the full log‑level truth of every impression, at scale and at speed, while enforcing transparency as a condition of spend. In doing so, they can spot inefficiencies buried deep in the supply chain that previously required teams of analysts and custom code to reveal.

From complexity to clarity 

Several major advertisers have already begun pairing demand-side platform (DSP) and supply-side platform (SSP) log-level data to understand where their money actually goes. 

When combined, those data sets reveal small but meaningful tolls as spend passes through data partners, data management platforms (DMPs), customer data platforms (CDPs), identity providers, clean rooms, and curators. Some partners prove essential, while others simply extract value. Once such inefficiencies are exposed, they’re impossible to ignore.

Historically, marketers could tighten their tech stack or rely on supply path optimization (SPO) to trim bloat, but transparency was still partial. Agentic buying changes that by turning transparency into an enforceable condition of spend. If a transaction doesn’t meet the agent’s required data standards, it simply moves on – no negotiation, no exceptions. That kind of automated rigor represents a new discipline for the industry.

The necessary pieces falling into place

Only recently have the technical and market conditions aligned for this shift. Modern agent frameworks have become sufficiently advanced to parse massive log streams in milliseconds. Supported by the proper infrastructure, they can look at both sides of the transaction without latency, and they can make decisions based on complete, real-time information.

Advertisers, in turn, want clarity on how many hops occur, what fees are taken, and whether each partner provided measurable value along the way.

In addition, the industry now has a blueprint for coordinated action. The made-for-advertising (MFA) crackdown demonstrated that when enough participants align on shared outcomes, efficiency can be enforced at scale. That same playbook will apply here; once agents expose which partners consistently add value and which consistently introduce drag, the system will adjust accordingly.

Agentic buying in action

At its core, agentic buying introduces a new layer of real-time negotiation between automated systems rather than human operators. On each impression, buy-side and sell-side agents exchange requirements and metadata tied to provenance, identity, context, fees, or performance history. 

This produces a cleaner, more predictable path to inventory, reducing reliance on heavyweight DSP interfaces that were built for human workflows rather than automated ones.

As transactions move through the system, fees, data markups, and overlapping services can be exposed automatically. If two partners perform the same function, the agent can recognize the redundancy and deprioritize one. If a data partner is charging more than the value it delivers, the agent can skip that path. If a curation layer adds cost without contributing to outcome lift, it can be surfaced immediately. There are hundreds of examples just like this that happen every day. 

With full visibility into the path to inventory, low-quality or redundant supply has little room to hide. Agentic buying ultimately turns transparency into a performance engine, transforming it from a reporting exercise into an ongoing quality-control mechanism. 

In “Gladiator,” Marcus Aurelius's dying wish was for Rome to be a republic again, ending the corruption. Programmatic was built originally to be a more efficient buying platform. Many of us believe it can be that again.

A new buyer playbook

Agentic buying’s adoption curve will take time, but frameworks like Ad Context Protocol (AdCP) are already paving the way. The result will be clearer economics, stronger supply paths, higher-quality reach, and a durable lift in trust and performance. Under this model, transparency won’t be something advertisers have to fight for. It will become the natural state of programmatic, driving even more effective outcomes for the advertiser.

Programmatic’s next chapter isn’t about more data; it’s about what we do with it. In 2026, agentic buying will redefine the rules of programmatic, where every data layer is interrogated in real time, and transparency becomes the engine of performance.