The rise of agentic advertising has sparked excitement among publishers who envision a future where intelligent software handles the tedious work of planning, packaging, forecasting, and executing campaigns. The promise is clear: automation could streamline operations, reduce manual overhead, and forge tighter connections between buyers and sellers. Yet beneath this optimism lies a familiar concern – will the new incentives driving these AI agents simply mirror the patterns that led to an overload of intermediaries in the programmatic era? As we stand at this inflection point, it is crucial to examine whether we are truly solving inefficiency or merely repackaging it in a more sophisticated guise.
Looking back at the programmatic boom, the average publisher ended up integrating with more than twenty supply‑side platforms, each added with the expectation of tapping fresh demand. In reality, many of these SSPs ended up bidding on the same inventory from the same buyers, creating duplication, operational overhead, and pressure on margins. The underlying issue was not that each platform lacked value; rather, publishers optimized for marginal gains at the level of each individual integration while losing sight of the cumulative cost and complexity of the whole stack. This historical lesson offers a stark warning for anyone assuming that more agents automatically translate to better outcomes.
Publishers pursued dozens of SSPs not by accident but because buyers rewarded the behavior, submitting bids across multiple pathways to increase their chances of winning impressions. Each additional integration, even if modest, contributed incremental revenue that justified the effort in the eyes of the seller. The same dynamic could easily reappear in an agentic world: if a buyer’s AI evaluates numerous sales agents and allocates spend based on perceived performance, publishers may feel compelled to spin up a multitude of specialized agents to capture every possible slice of demand, recreating the very fragmentation they hoped to eliminate.
Agentic selling is already beginning to follow a similar trajectory. Developers are crafting AI agents that specialize in distinct functions – some focus on creative matching and sponsorship storytelling, others on PMP structuring, yield optimization, or native ad execution. Each agent serves a genuine purpose, but when deployed in isolation and multiplied across the ecosystem, they risk reintroducing the same layers of duplication that plagued the SSP landscape. The central question becomes whether we are building a lean, coordinated system or simply assembling a new constellation of intermediaries that each claim to add incremental value.
The incentives encoded into buyer agents will heavily influence how supply is selected. If an agent is judged on campaign performance metrics such as ROAS, efficiency, or platform‑level economics, it will naturally gravitate toward the supply paths that best satisfy those criteria, regardless of whether those paths represent the absolute best inventory for the marketer’s goals. The design of the agent – who defines its objectives, which data it can see, and which supply routes it is permitted to explore – becomes a critical lever that can unintentionally favor certain sellers over others, reproducing the bias that has long troubled the industry.
Consider a typical buyer prompt: “Find high‑quality web and OLV inventory for a World Cup campaign targeting primary household shoppers who are also soccer fans. Optimize for ROAS with a $100K budget.” When this instruction reaches the marketplace, various sales agents respond with tailored audience‑inventory packages that they believe match the brief. The buyer agent then scores each response based on the performance signals reported by those agents and allocates budget accordingly. In theory, any sales agent could win the allocation, but in practice the buyer’s own ecosystem may steer spend toward agents it controls or has deep data integrations with, leaving third‑party options at a structural disadvantage.
This tendency to favor internally controlled supply paths revives an existing form of bias: dollars flow to the most incentivized source rather than the highest‑performing one. When buyers’ AI models are tuned to maximize their own platform economics, they may undervalue external agents that offer superior audience quality or creative fit but lack the same level of integration or data sharing. Publishers, sensing this skew, may respond by proliferating their own agents – one for each niche, each channel, each buyer platform – hoping to capture every possible advantage, thereby reconstructing the intermediary maze under a new technological banner.
Not all sales agents are interchangeable. Some excel at extending distribution to hard‑to‑reach audiences, while others specialize in how inventory is packaged, priced, and presented in private marketplaces or native formats. A custom content agent might dive deep into editorial alignment and sponsorship concepts, whereas a programmatic‑focused agent could concentrate on yield management, floor pricing, and scalable execution across exchanges. Recognizing these distinct competencies is essential; the goal should be to assemble a complement of agents that together cover the full spectrum of selling needs without unnecessary overlap.
The long‑term opportunity lies in orchestrating these specialized agents into a cohesive system that automates the heavy lifting of packaging, planning, forecasting, and optimization while elevating human sellers to focus on strategy, relationship building, and creative collaboration. When agents are designed to hand off clear, standardized outputs to one another – much like modules in a well‑engineered software pipeline – the overall process can become both efficient and transparent. This approach mitigates the risk of duplicate effort and ensures that each agent’s contribution is additive rather than redundant.
Publishers seeking to navigate this terrain should adopt a disciplined framework: build multiple sales agents only when distinct ad products or buying motions genuinely require different expertise. There is no need to maintain a separate agent for every SSP or every minor variation in a deal type, just as today a publisher does not employ a unique salesperson solely to launch PMPs on one platform and another solely for a different platform. Consolidation around clear functional boundaries reduces complexity, improves reporting clarity, and preserves leverage in negotiations with buyer‑side agents.
On the buyer side, agencies and brands would be wise to develop their own buyer agents that are tuned to their specific campaign objectives and data assets, thereby avoiding reliance on duplicative supply paths that can inflate costs and obscure performance insights. By owning the decision‑logic layer, marketers can enforce consistency in how inventory is evaluated and ensure that spend flows toward the highest‑value opportunities rather than the most conveniently integrated ones.
The true test for agentic advertising will not be whether the technology can automate media transactions – that capability is already within reach – but whether the industry can resist the temptation to recreate the same layers of duplication, bias, and intermediation that the automation was meant to erase. Success will depend on transparent incentive design, open standards for agent‑to‑agent communication, and a collective commitment to clarity over short‑term incremental gains. If stakeholders heed the lessons of the programmatic past and apply them thoughtfully to the agentic future, the promise of a more efficient, direct, and profitable advertising marketplace can finally be realized.