Agentic AI has quickly become one of the most overused terms in advertising technology. Every platform now claims to offer “agents,” “autonomous buying,” or “AI-driven orchestration.” Much of that is marketing. But beneath the noise, a real shift is underway.
Programmatic advertising is moving—from AI as a co-pilot to AI as an operator. Not universally. Not all at once. And not without constraints. The change is incremental, practical, and far more limited than the hype suggests.
This isn’t a vision of the future. It’s an update on where agentic AI is actually being deployed today across the buy side and sell side—and where it clearly isn’t.
From Insights to Execution
Historically, AI in ad-tech focused on analysis: dashboards, alerts, recommendations, and optimization suggestions. Humans still executed the work.
Agentic AI changes the scope of responsibility. An agent doesn’t just analyze—it can plan, act, observe outcomes, and adjust. In practice, this means fewer dashboards and more automated execution.
The real question isn’t whether agents are now available to execute these tasks. It’s where they’re trusted.
A Practical Framework: Where Agentic AI Operates Today
The easiest way to separate reality from hype is to look at layers of control. Most production systems operate in one or two layers—not across the full stack.
1. Workflow Agents: Reducing Operational Overhead
This is the most mature and widely deployed category.
Workflow agents translate human intent into platform actions:
● campaign setup and trafficking
● inventory discovery and proposals
● pacing adjustments and troubleshooting
● reporting and reconciliation
Initiatives like AdCP are explicitly designed to standardize these workflows so agents can operate across platforms without rebuilding the ecosystem.
These agents don’t decide what to buy. They decide how to execute faster, with fewer errors and less manual effort. It’s not glamorous—but at scale, it materially changes how teams operate.
2. Decisioning Agents: Curation, Yield, and Constraints
This is where agentic systems become more defensible.
Decisioning agents operate within defined domains:
● supply curation and SPO
● inventory quality and suitability
● sustainability or compliance constraints
● yield optimization under explicit rules
Companies like SWYM.ai and Scope3 exemplify this approach. Their systems continuously evaluate signals, enforce constraints, and adapt decisions based on observed outcomes.
What differentiates these agents from traditional rules engines is closed-loop learning. They don’t rely on static tables or periodic human updates; they adjust continuously based on performance feedback.
A growing pattern on the sell side is the emergence of data-science-driven agents that continuously tune machine-learning models governing yield decisions—such as dynamic price flooring—across thousands of sites in real time. These systems operate as closed-loop agents, adjusting model parameters continuously to optimize toward explicit revenue or efficiency outcomes.
In parallel, some publishers are beginning to deploy internal LLM-based agents trained on proprietary monetization data to surface insights that would be impractical for human analysts to detect—connecting signals across auctions, demand behavior, and historical performance to recommend concrete, actionable changes.
The common thread is focus: these agents succeed because they own a specific decision point and operate within boundaries that can be audited and explained.
3. Transacting Agents: Buyer–Seller Automation
This is the most discussed—and least mature—area.
Here, the promise is agents on the buy side and sell side negotiating, activating, and fulfilling premium media buys with minimal human involvement. Early pilots are emerging through AdCP-enabled workflows involving SSPs, agencies, and curators.
These tests are real, but they are narrow, supervised, and intentionally constrained. Their significance isn’t autonomy—it’s proof that agent-to-agent transactions can run on existing programmatic rails.
This phase is best understood as infrastructure validation, not mass adoption.
4. Governance: The Quiet Prerequisite
If an agent can execute spend, governance becomes non-negotiable.
That means:
● permissions and access control
● auditability and logging
● standardized objects for deals and constraints
● clear accountability when outcomes go wrong
This is why standards bodies like IAB Tech Lab play such a central role in the agentic conversation. Without shared governance, autonomy quickly becomes liability.
Most real agentic systems today are constrained by design—not because the AI is weak, but because trust matters.
What Agentic AI Is Not Doing (Yet)
Despite the rhetoric, agentic AI is not:
● replacing DSP bidding logic end-to-end
● inventing strategy without human goals
● autonomously reallocating large brand budgets
● solving signal loss or measurement challenges
Many “agentic” launches are better described as AI-assisted automation. That doesn’t make them irrelevant—but buyers should be clear about whether they’re buying efficiency or genuine decisioning advantage.
The Real Value
The most effective agentic systems today share three traits:
- Narrow scope – they own a specific workflow or decision
- Strong feedback loops – they learn from outcomes
- Human-defined intent – strategy comes first, agents execute
In practice, this often means agents operating quietly in the background—tuning models, interpreting complex auction behavior, and surfacing a small number of high-confidence actions for operators to review or approve.
In programmatic advertising, leverage usually comes from removing friction rather than inventing intelligence. Agentic AI delivers value when it reduces operational drag, enforces rules consistently, and reacts faster than humans can.
What to Watch Next
Over the next year, agentic AI will be judged on three questions:
● Does it deliver measurable incrementality, not just efficiency?
● Who controls the decisioning logic—platforms, agencies, or independent systems?
● Can governance scale without killing flexibility?
If agentic AI succeeds, it won’t look revolutionary. It will look boring: fewer emails, fewer dashboards, fewer manual fixes.
And that’s exactly how real progress usually shows up.
By Vijay Kumar, CEO of Mile, an AI Powered programmatic revenue optimization platform for publishers, that maximizes yield from every impression by refining floors, shaping traffic, and enriching bid requests using publishers’ unique auction data.
