"Agentic" AI Is a Bit of a Catch-All Right Now (and That's OK)

The word "agentic" is getting used to describe a pretty wide range of things. Fully autonomous systems that plan and execute campaigns without human input. Chatbots that answer platform questions. Diagnostic tools that flag delivery issues and suggest fixes. All of it gets filed under "agentic AI."

Here’s how to think about it: most of what the industry is calling “agentic” today is actually assistive. The AI helps you understand what's happening, points you toward a likely cause, and recommends what to do. You still make the call. That's not a limitation - that's actually the point!

Assistive AI is its own category

We love a useful analogy, so here’s one: all squares are rectangles, but not all rectangles are squares. The same is true here. All assistive AI is agentic, but not all agentic AI is assistive. They're related, but they're not the same thing.

Assistive AI is what happens when a system makes a skilled person faster and better informed without replacing their judgment. It surfaces the right information at the right time, explains what it found, and hands the decision back to the human. Done well, it's genuinely powerful. A trader who gets a clear diagnosis of a delivery issue, with a recommended fix, in the time it used to take to even locate the problem, is a more effective trader. That's the value of assistive AI.

And at Yahoo DSP, this is where we've focused our energy. We want to remove the repetitive, time-consuming work so people can focus on the parts of their job that actually require judgment. The AI handles the diagnostic labor while the human handles the call.

Our philosophy: transparency drives trust

We've been deliberate about how we think about this, and it basically comes down to one thing: AI agents shouldn’t be a black box. When the system surfaces a recommendation, you should be able to see why. A human should be able to see and confirm changes in a campaign.

We don't view this as a constraint we're working around, but rather how we built this by design. Trust isn't assumed. It gets established through consistent, explainable behavior, and we think that foundation is what makes the technology worth expanding over time. You can't just declare trust. You have to demonstrate it, repeatedly, before it makes sense to hand more over to the system.

Our troubleshooting agent reflects this. You describe an issue, it identifies the likely cause and surfaces a fix, you decide whether to apply it. Simple, with a human in the loop the whole time.

It's also why we built the Yahoo DSP Agent Network the way we did. Instead of trying to own every capability natively, we've made it possible for partner-built agents across targeting, measurement, creative, and activation to plug into DSP workflows. More choice, same principle: the advertiser stays in control of how and when those tools are used.

The balance matters more than the label

There are use cases where more autonomous action makes sense, where the decision is well-defined, the stakes are manageable, and the system has a clear track record. Those cases exist and will grow. But there will always be decisions where human context, client relationships, or strategic nuance matter in ways an agent shouldn't own. Assistive and (literal) agentic serve different needs, and a good platform handles both.

The word "agentic" will keep stretching as the category matures, and that's fine. The more useful question for marketers is not whether something is technically agentic, but whether it's transparent, useful, and built to give you more control rather than less.

That's what we're building toward. The label is secondary.