For many media buyers, AI can feel like a black box. Something powerful, but hard to understand and even harder to trust. That hesitation is understandable. The advertising industry is shifting from impression-by-impression trading toward agentic decision-making, where agentic systems plan, execute, and optimize media in real time. As those systems begin to touch real budgets, real brands, and real outcomes, trust is no longer just a perception issue. It becomes part of the infrastructure.
The reality is far more practical than the hype suggests. AI in advertising is made up of several well-defined capabilities that already support planning, optimization, and execution today. When these systems are applied thoughtfully, with humans setting the goals and guardrails, they can reduce manual work and help teams focus on strategy rather than mechanics.
At this early stage in the technology, clarity matters. As agentic systems move from experimentation into real market deployment, advertisers need more than powerful algorithms. They need systems they can understand, govern, and trust.
AI in Advertising Is a Set of Distinct Capabilities
One of the biggest challenges in conversations about AI is that the term gets used as shorthand for many different technologies. In practice, advertising platforms rely on multiple AI systems, each designed to solve a specific type of challenge:
- Large Language Models (LLMs) specialize in understanding and translating plain language. They are used to turn natural language prompts from briefs, goals, constraints, and questions into structured inputs that machines can act on. Their value is accessibility and speed, allowing buyers to express intent in plain language and reducing friction between humans and complex systems.
- Generative AI produces content or scenarios based on the intent translated from LLMs. In ad tech, this can include generating creative variations, messaging options, media plans, or forecasts. These systems expand the range of possibilities teams can consider, accelerating iteration and testing without requiring manual production for every variation.
- Machine learning (ML) models performance and optimizes toward defined objectives, and has long been foundational to programmatic advertising. ML models improve performance consistency, adjusting bids, forecasts and delivery based on what’s working.
- Agentic systems build on this foundation by coordinating decisions across complex workflows. Agents plan actions, execute them, and learn continuously through feedback loops, coordinating multiple decisions over time, rather than optimizing in isolation.
In practical terms: LLMs translate intent, Generative AI explores possibilities, machine learning predicts and optimizes decisions, and agentic systems coordinate execution. Each capability plays a distinct role. The real challenge, and opportunity, comes from coordinating them responsibly.
What “Agentic” Really Means
Agentic advertising is often misunderstood as “human out of the loop” or “fully autonomous.” In reality, it’s better thought of as an operating model.
In real advertising environments, autonomy must be permissioned. Humans define objectives, guardrails, and risk tolerance, while agentic systems plan, execute, and optimize continuously within those boundaries.
Advertisers decide how much authority to delegate, which controls remain fixed, and how risk is managed. Some may begin with tightly scoped use cases and explicit approvals, expanding autonomy over time as trust is built through performance. Others may move more quickly toward hands-off execution once objectives and constraints are clearly defined.
This isn’t a limitation of the technology. It’s how trust is built.
The result is faster decision-making, greater consistency, and a level of operational scale that would be difficult to manage manually, without buyers losing control.
What This Looks Like in Practice
These distinctions aren’t theoretical.
When PubMatic deployed its first fully agentic campaign with Butler/Till, Geloso Beverage Group was a real advertiser on the other end, with real products to sell, clear performance goals, and real constraints.
As a regional brand operating in a restricted category, Geloso wouldn’t typically have access to advanced agentic capabilities. Historically, those tools have been gated by the largest players in the industry.
The goal wasn’t to showcase novelty or to remove humans from the process. It was to apply agentic execution where it could create real value, saving time, reducing manual effort with automated workflows, and optimizing toward measurable efficiency gains.
That responsibility was taken seriously.
Humans defined objectives and boundaries. Agentic systems handled execution and optimization. The system was designed to work smarter and faster, not louder.
Humans Don’t Disappear, They Move Upstream
As more operational decisions move into software, human roles become more strategic, not less relevant.
People remain essential for defining goals, shaping strategy, exercising judgment, and creating differentiation. Agentic systems take on executional complexity so teams can focus on planning, creativity, and scenario thinking.
The most effective outcomes come from clear collaboration between humans and machines, not from eliminating one or the other.
A Practical, Optimistic Path Forward
Agentic advertising is still early, and that’s a strength.
The industry has an opportunity to define how these capabilities work together: grounded in real infrastructure, applied thoughtfully at the application level, and executed responsibly where transactions occur.
Progress won’t be measured by how aggressively autonomy is claimed. It will be measured by how effectively these systems improve performance, expand access to advanced capabilities, and deliver real value in real markets.
AgenticOS provides the operating system that makes it all work — safely, at scale. To learn more visit go.pubmatic.com/agenticOS.
