For sophisticated performance marketers, running campaigns across multiple DSPs has long been a practical way to understand what is working, compare results, and preserve the option to shift budgets toward stronger performance. But even the most advanced advertisers tend to hit an operational ceiling. In most cases, they might run across two or three DSPs, not five, six, or more. The limitation is less about strategic ambition and more about time.

Every DSP has a unique set of inventory, data, workflows, and best practices. Expanding into another buying platform creates additional work, even when the advertiser sees clear value in broader comparison and testing.

That is why the next phase of agentic media buying could be so significant.

Agentic systems have not yet transformed media buying. The industry is still early in this process. But over the next 6 to 12 months, as agentic experiences mature and become more mainstream, their impact could extend well beyond automation. They could meaningfully lower the operational cost of running campaigns across more platforms, opening the door to a new model of always-on experimentation. 

DSP Agents Will Reduce Platform-Level Friction

In the near future, every DSP will roll out a similar set of agentic capabilities. These will likely include agents for campaign setup, optimization, troubleshooting, and insights.

That matters because much of the work that makes cross-platform experimentation difficult today is highly operational. A media buyer must know how to configure a campaign correctly in each DSP, understand each platform’s recommended practices, monitor delivery, interpret performance, and know when to intervene. Agentic tools can begin to absorb more of that complexity.

Setting up agents will help translate campaign goals into the right configuration. Optimization agents will recommend changes based on performance trends. Troubleshooting agents will identify why a campaign is underdelivering and recommend fixes. Insights agents will summarize what is happening and outline the actions that should follow.

None of this eliminates the need for human oversight, but it does reduce the cost of working in each environment. As that cost falls, the logic of limiting experimentation to only a few platforms begins to weaken. 

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The Bigger Shift May Come From Agency-Built Agentic Operating Systems

A second transformative change in this equation is likely to happen above the DSP layer. Agencies and some large brands have wanted to build versions of a “meta-DSP” for decades. The concept has always been appealing: a single operating layer that enables a team to plan, activate, manage, and evaluate campaigns across multiple DSPs. The challenge has been execution. Traditional APIs made this difficult, expensive, and rigid. The technical complexity often outweighed the practical benefit.

Agentic systems could change that equation. In a more mature agentic environment, an agency could upload a media brief into its own agentic operating system. That system could then interact with DSP-level agents to set up campaigns across multiple platforms. It could coordinate setup, monitor performance, surface insights, and recommend budget shifts based on what is actually working.

 That would not simply make media buying faster. It would make broader experimentation operationally feasible.

 The agency’s agent could become the connective tissue across DSPs. DSP agents would handle tasks within each platform, while the agency’s agent would coordinate across them.

Always-On Experimentation Becomes a Strategic Advantage

The first conversation around agentic systems is usually about efficiency. That is understandable. Automating setup and optimization saves time. It reduces manual work. It helps teams move faster.

But the more important opportunity is learning. When the cost of running across more DSPs declines, advertisers can stop treating experimentation as a periodic exercise. They can make it part of the core operating model. Instead of asking whether there is enough time to test another platform, they can continuously evaluate where performance is strongest and where the budget should move.

Today, many brands and agencies select their core DSPs based on past performance, perceived value, or simple familiarity with how a given platform works. Those factors can become stale. A DSP that performed well for one campaign, audience, or objective might not be the strongest option for the next. Yet because each platform requires its own expertise and workflows, advertisers often keep returning to the same two or three environments rather than continually testing the broader field. Always-on experimentation changes that dynamic. As agentic systems reduce the effort required to activate and evaluate campaigns across platforms, marketers will be able to “taste and see” which DSPs are best suited to each objective, rather than relying on inherited assumptions about where performance is likely to come from.

This creates a very different competitive dynamic. Advertisers that invest in agentic operating systems will be able to compare more options and adjust with greater confidence. They will not be locked into a small set of platforms simply because those are the ones their teams have the capacity to manage. Over time, this could make experimentation less of a campaign tactic and more of an organizational capability. 

Also Read: Are Brands Losing Credibility in the AI Era?

The Role of Marketers Will Become More Strategic 

This future does not imply that media buyers disappear from the process. In fact, their strategic role becomes more important. Agentic systems can reduce executional burden, but they still need direction. Marketers will need to define objectives, determine what to test, evaluate whether recommendations make business sense, and ensure experimentation aligns with brand and performance goals. They will also need to decide how much control they want to centralize within their own agentic operating systems and how much they are willing to delegate to individual platforms. 

As DSPs introduce their own agents and agencies begin building agentic operating systems that can coordinate across platforms, always-on experimentation will become practical at scale. That means the next advantage in media buying will be less about doing the same things faster and more about discovering better ways of doing them in the first place.

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