From Automation to Autonomy: How Agentic AI Is Rewriting the Rules of Adtech

What if your media platform could not only follow instructions, but actually understand intent, reason through nuance, and take autonomous action – all without sacrificing brand safety or human oversight?

That’s not science fiction, it’s agentic AI – and it’s here to fundamentally reshape the adtech industry.

For years, artificial intelligence in advertising has been synonymous with automation: machines crunching numbers, optimizing bids, or scoring creatives. Agentic AI, however, takes this to a new level. It brings contextual reasoning, multi-step problem solving, and the ability to adapt dynamically to evolving inputs. In other words, we’re moving from AI as just a tool to AI as a true collaborator.

We’re still in the early days of agentic AI, but forward-thinking marketers can get the wheels turning today. Here’s what you need to know now about this impending shift in the business.

From Predictive Models to Autonomous Partners

AI in advertising isn’t new. In fact, many of the innovations we take for granted today – like real-time bidding, creative optimization, and predictive targeting – are the result of traditional AI systems built to perform specific tasks with impressive precision. These purpose-trained models have been essential in helping marketers make sense of vast amounts of data and drive better outcomes.

Agentic AI doesn’t replace those systems. Rather, it complements and extends them by combining language-based reasoning, contextual understanding, and the ability to autonomously navigate workflows. It can interpret campaign briefs, adapt to shifting goals, and execute multi-step tasks with minimal input – all while staying aligned with your brand’s strategy.

This evolution isn’t about discarding what came before – it’s about unlocking the next level of intelligence and autonomy to help marketers work smarter, not harder.

It’s also arriving amid a wave of industry-wide transformation. According to the IAB’s State of Data 2025 Report, 70% of agencies, brands, and publishers are not yet fully integrating AI across media planning, activation, and analysis – but over half expect to reach full integration by 2026.

Why It Matters: Solving the Fragmentation Crisis

The advertising ecosystem is notoriously fragmented. A typical brand today runs campaigns across search, social, DSPs, CTV, retail media networks, and more. Each channel has its own data silos, measurement protocols, and optimization logic. The result is that marketers are overwhelmed, outcomes are inconsistent, and up to 56% of programmatic ad spend never even reaches the consumer – according to the latest ANA Programmatic Media and Supply Chain Transparency Study.

Agentic AI helps address this fragmentation – not by replacing people, but by connecting the dots in more intelligent ways. Agents can unify workflows across platforms, provide real-time feedback loops, and recommend optimizations that factor in brand nuance, creative insights, and cross-channel performance.

Marketers stay in full control; agentic AI simply helps them reach better decisions, faster and with greater context and intelligence.

Best Practices for a Human-Guided, Agent-Powered Future

To unlock the full potential of agentic AI in advertising, brands must strike the right balance between automation and oversight. For instance, a retail brand might use an agentic system to not only identify underperforming audience segments mid-flight, but also dynamically test new creative variations and reallocate budget in real time – without waiting for human intervention.

Here are some principles to keep in mind:

• Start with strategy, not software: Agentic AI thrives when it has context. Begin with clearly defined KPIs, audience frameworks, and creative goals. Let your strategy guide the AI – not the other way around.
• Keep humans in the loop: Even the smartest agent needs a partner. Design workflows that empower your teams to review, approve, and refine outputs. Human oversight ensures alignment with brand values and avoids “black box” decision-making.
• Prioritize interoperability: Agentic AI performs best in open, composable environments where it can tap into diverse data and tools. Seek out solutions that integrate easily with your existing stack and give you flexibility to evolve. According to the IAB, nearly 61% of marketers cite fragmented tech ecosystems as a top barrier to AI adoption. This underscores the need for composable, agent-friendly environments.
• Design for explainability: Agents that explain their rationale build trust. Choose platforms that offer transparency into how decisions are made, what data is used, and what outcomes are being optimized.
• Embrace continuous learning: Just like your team, agentic systems get smarter over time. Incorporate feedback loops – both human and machine-based – to improve outcomes and align with evolving campaign needs.

Where Agentic AI Innovation Meets Real-World Results

At Infillion, we’re building for a future where advertisers are empowered by systems that are flexible, intelligent, and respectful of the consumer. Agentic AI aligns with that vision – offering a way to scale human creativity, unlock operational efficiency, and create truly personalized, bespoke experiences.

As composable platforms become the new standard, agentic workflows won’t just be a nice-to-have – they’ll be the glue holding everything together.

The momentum is real: According to the IAB, more than 80% of media companies not yet at full-scale AI adoption expect to get there by 2026 – showing that this isn’t just hype, it’s happening.

In an industry marked by complexity, speed, and constant change, the brands that win won’t necessarily be the ones with the biggest budgets – rather, they’ll be the ones with the most intelligent systems.

Want to see where agentic AI is headed? Get in touch with us to learn how Infillion is helping brands navigate what’s next.