We’ve passed the point where AI is just a productivity hack. The agentic era isn’t about saving time—it’s about surrendering control. And that’s not some philosophical scare tactic—it’s a functional shift already in electronic motion.

Agentic AI systems don’t just respond to prompts—they evaluate, argue, and evolve without waiting for human direction. They aren’t smart assistants. They’re autonomous collaborators with jobs, accountability, and a growing portfolio of campaign wins.

At Naama Manova Twito’s company, each AI agent is assigned a unique specialty and goal orientation. One might be focused on micro-audience segmentation, another on channel performance prediction. These agents aren't just creating outputs—they’re actively interrogating each other’s assumptions. When one proposes a media buy, another might flag inefficiencies or emotional mismatches based on real-time audience sentiment shifts.

Naama puts it plainly: “Our agents can measure their own impact… and adjust on the fly.” She’s not just talking about optimization within a dashboard. This is intra-agent conflict resolution. They self-regulate, self-correct, and even debate over creative direction, budget allocation, and target expansion strategies. One AI recommends targeting early-morning commuters with upbeat messaging, another counters with a historical performance dip for that slot—and the system adjusts accordingly. That’s not automation. That’s governance.

But while these agents execute with machine speed, the human job hasn’t disappeared—it’s just been distilled to its most irreplaceable elements. As Naama says, “The marketer becomes more of a reviewer than a creator.” Your role? Instinct-tuning. Cultural arbitration. Making sure your brand doesn’t accidentally sound like a manic raccoon on TikTok just because the model saw a spike in engagement on Gen Z meme pages.

In parallel, Zack Rosenberg’s team has made it their mission to erase the hallucinations of adtech’s video strategy. They analyzed the market’s dependency on transcript-based video targeting and discovered that most campaigns are flying blind. Not metaphorically—literally blind. Tags lie. Transcripts mislead. Metadata decays faster than old yogurt on an Arizona summer day So Zack’s solution? Build an AI system that watches video like a sentient intern who never blinks.

This means scene-level comprehension: the ability to register lighting changes, tonal shifts, emotional arcs, and behavioral signals within a given video. Oh, remember he said “We found that video was miscategorized upwards of 88.98% of the time.” That’s not an error rate. That’s a systemic failure. His team’s model bypasses third-party assumptions and instead creates live, frame-by-frame understanding of what’s actually happening onscreen—and more importantly, what it means for the viewer.

They also found that the content itself changes faster than media buyers realize—sometimes every six hours on high-volume platforms. “There’s typically no correlation at all between the page content and the video,” Zack explains. “And the third method everyone relies on—transcripts—can be equally useless. We had a video where the only word spoken was ‘whoa.’ Is that a car crash? A goal scored? A gift being given?” That’s not targeting. That’s fortune-telling with a blindfold on.

So Zack’s answer is what he calls “moment-level intelligence”—where AI registers not just the scene but its emotional and narrative implication, and aligns ads accordingly. This isn’t contextual targeting. It’s narrative targeting. And it’s what allows brands to step into the story instead of awkwardly interrupting it.

That feels like real AI.

But perhaps the most radical shift comes from the work Richard Howe is doing. His AI doesn’t care about who the user is in any traditional sense. It doesn’t store names. It doesn’t need third-party cookies. It’s trained to recognize motivation, not identity. That means ditching 25-year-old taxonomies built on static demographics and building a model that feels out human intent in the moment.

“Reactive machines put Corvette ads on Corvette pages. That’s not thinking. That’s just reflex,” Richard says. His system reasons: what is someone actually trying to solve when they engage with content? Are they researching sleep aids for themselves—or for their snoring dog? “There’s no database field for ‘sleeps with loud animals,’ but AI can read the entire internet and find that connection.”

This approach—what Richard sometimes refers to as theory-of-mind modeling—doesn’t just ask what someone is doing. It tries to understand why. It’s a mental model, not a mechanical one. And in doing so, it discards the ad industry's biggest addiction: identity proxies. As he puts it: “You’re still fixated on identity and structured databases. You can’t even imagine a better way.”

Here’s the paradox at the heart of all this: AI is finally good enough to do the busywork, but also smart enough to question the blueprint. And that changes what marketing is. You’re not the director anymore. You’re the editor. The voice of judgment in a world of infinite options.

We’ve crossed from reactive ads to reasoning machines. And if you’re still trying to run your campaigns like it’s 2014, here’s your gentle reminder: your competition now includes a cluster of agents who never sleep, never settle, and never forget the metrics.

Next time they challenge your media plan, don’t take it personally. They read the entire internet while you were sleeping.