Over the past several weeks, I’ve started digging into the many agentic AI announcements that have emerged across the recruiting tech space this spring. My first deep dive focused on GoodTime’s launch of Orchestra—a grounded example of how intelligent agents are being packaged, positioned, and deployed in practice.

But with so many vendors introducing agentic features, assistant interfaces, and orchestration frameworks, it’s worth pausing to examine what’s really happening at a market level.

This post steps back from individual launches to look at the broader narrative: where it’s aligned, where it’s evolving, and where the gaps remain between aspiration and application.

Agentic AI Is Having a Moment—Now What?

This spring brought a surge of announcements positioning agentic AI as the next frontier in talent and HR tech. Vendors are casting AI not just as a tool, but as the core of a modern operation—especially in talent acquisition.

Across the board, a consistent narrative has emerged:

• AI agents “owning workflows” like sourcing, outreach, interview scheduling, or candidate engagement
• Replacing point solutions with interconnected agents that make decisions, learn over time, and act independently across the hiring process

It’s a vision of intelligent orchestration: agents not just executing tasks, but coordinating across systems, surfacing insight, and in some cases, initiating action. In this framing, AI is no longer just embedded in features—it is the feature.

But while the language is new, much of the functionality is not. Many of the so-called agents being introduced this year are built on existing automation and recommendation engines, wrapped in a more user-friendly (and headline-ready) interface. Some are operational now; others are still roadmap-level.

That’s not necessarily a bad thing. As with any inflection point, vendors need time to evolve from vision to execution. But for practitioners, especially those responsible for evaluating or implementing these capabilities, it’s important to understand what agentic AI actually looks like today—not just how it’s being pitched.

This moment is energizing. But it also raises important questions:

• Which agentic capabilities are delivering value now?
• What does governance, control, or override actually look like?
• How well do these agents perform across systems of record like the ATS, CRM, or content libraries?
• And how prepared are TA teams—strategically and operationally—to use them well?

What we’re seeing is the beginning of a new chapter in AI-powered TA. But it’s only the beginning.

Pattern Recognition: We’ve Seen This Movie Before

The current push toward agentic AI feels new—but the dynamics underneath it aren’t. If you’ve been in this space long enough, it’s hard not to draw comparisons to previous hype cycles.

I was recently briefing with the team at Radancy about their own upcoming agentic AI announcements, and was reminded that we’ve seen this trend before.

In discussing Radancy’s approach to where and how to apply AI, Jahkedda Akbar, Senior Vice President of Innovation, Radancy Labs reminded me that we’ve seen this exact movie before.

Ten years ago, “there’s an app for everything” wasn’t just a marketing slogan—it was a design philosophy. Vendors spun out standalone apps for every sliver of the employee experience, from onboarding to engagement to micro-learning. Not because each problem needed its own container, but because the tech made it easy to build one.

What followed was a wave of tool proliferation, integration pain, and what many teams now refer to as stack fatigue. For buyers, the lesson was clear: Just because you can productize a capability doesn’t mean you should. And just because something is framed as a feature—or in today’s case, as an “agent”—doesn’t mean it drives value on its own.

Agentic AI is showing signs of heading down a similar path. As vendors rush to introduce named agents, assistant layers, and orchestration hubs, the market runs the risk of recreating the same bloat it’s spent the last five years trying to untangle—only now, with AI stitched in.

Of course, that doesn’t mean the agentic shift should be dismissed. There’s real potential here. But it does mean we should approach this moment with a little pattern recognition—and a healthy level of discernment.

There’s a difference between adding intelligence and just renaming automation. Not every suggestion is an agent. Not every trigger is orchestration. If we don’t draw those lines now, the value of agentic AI will erode before it has a chance to mature.

A Call for Discipline: Innovation Should Serve Outcomes

The momentum around agentic AI is undeniable. But as with any rapid shift in tech narratives, momentum without discipline is risky. This is a moment that calls for clarity—not just from vendors, but from buyers, builders, and anyone tasked with making this technology operational.

We should be asking material questions about the material challenges we face:

• Does this improve a recruiter’s ability to deliver quality hires?
• Does it reduce friction—or just change where it shows up
• Does it enhance the candidate experience—or complicate it?
• Is it driving measurable progress—or just repackaging incremental updates under a new label?

What’s needed now is intentionality—in design, in deployment, and in messaging. It’s easy to chase the agentic trend; it’s harder to align it with real-world complexity in enterprise hiring.

On that note, I have to go back to Radancy… and my new BFF Jahkedda. She and her colleague Matt Lamphear, Executive Vice President, Digital, were walking me through their new offerings when she said something that really struck me.

Rather than framing agentic AI as a transformational overhaul, Jahkedda emphasized a more grounded approach—one that prioritizes value delivery over novelty, and centers the user (recruiter and candidate) in the process:

 “Everyone’s rushing to agentic as the solution—and it does have a lot of potential—but we are being super thoughtful about how, when, and where we deploy these capabilities… and where we continue to leverage other AI and automation capabilities as it makes sense. But ultimately we’re going to focus on delivering value to our customers and their stakeholders.”

That kind of focus—the willingness to prioritize outcomes over optics—is what I believe will ultimately separate vendors that generate long-term value from those riding the trend cycle.

Because the real opportunity with agentic AI isn’t to rename what’s already in motion. It’s to solve for the very real gaps that exist between teams, tools, and workflows.

That takes product discipline. It takes UX design that respects both autonomy and oversight. And it takes resisting the temptation to make “the agent” the story, rather than the enablement it’s meant to provide.

Ultimately, here’s my take: Agentic AI will only succeed if vendors stop treating it like a feature and start treating it like a design principle. Buyers aren’t asking for bots. They’re asking for better outcomes, with less lift. Everything else is optional.

The Age of Agentic Is Here—Now Comes the Work

The energy around agentic AI is real—and so is the shift it represents. We’ve entered a new chapter in talent acquisition technology, one where intelligent agents are being positioned not as supporting features, but as active participants in the hiring process.

But enthusiasm isn’t execution. The architecture behind many of these announcements is still early. The interfaces are still evolving. And the operational impact remains, in most cases, unproven.

That doesn’t mean this isn’t important. It just means we’re at the beginning—not the breakthrough.

As I wrote in my last piece: this moment is a turning point, not a finish line. And as we move into the summer, I’ll continue unpacking the agentic AI movement with a series of vendor-specific analyses—looking closely at who’s building what, what’s actually live, and where TA leaders should be paying attention.

The agentic era is here. And there’s no better time to separate what’s messaging from what’s measurable.