Everyone is talking about agentic AI. And that's part of the problem.

Over the last couple of years, the term has gone the way of every other buzzword in tech — slapped onto products and platforms regardless of whether it actually applies. Marketing departments are busy, as Adi Kuruganti, Chief AI and Development Officer at Automation Anywhere, put it when we sat down to record the latest TechSpective podcast episode. And when marketing departments get busy, clarity tends to suffer.

Automation Anywhere has been in the automation space for over a decade. They helped create the Robotic Process Automation category, so Adi has a longer view on this than most. He knows what automation looked like before the AI wave hit, and he has a pretty specific definition of what an agent actually is — one that rules out a lot of what's currently being marketed as agentic AI.

That distinction has real consequences. When you're automating routine, low-stakes tasks, some ambiguity is tolerable. But when you're talking about healthcare workflows, financial processes, or anything touching sensitive customer data, the difference between a rules-based automation and a probabilistic AI agent matters. Getting that wrong isn't just a technical problem. It can be a compliance problem, a liability problem, or worse.

We also get into accountability. When an AI agent takes an action — reads a document, makes a decision, updates a record — who's responsible for that outcome? It's a question a lot of organizations are still working through, and the answer is more nuanced than it first appears. Adi has a clear perspective on this, shaped by what Automation Anywhere sees across its customer base of more than 5,000 enterprises.

Data privacy comes up, too. Giving an AI agent access to the context it needs to actually be useful means sharing information with it. But in regulated industries, that creates real constraints. How do you give an agent enough to work with without exposing data it shouldn't touch? It's a real problem for a lot of enterprises right now, and we talk through how organizations are navigating it.

And then there's the question of trust — specifically, how much autonomy you give an agent before a human needs to review what it's doing. The answer isn't as straightforward as "always have a human check the work." Adi makes a point here that I think a lot of people in the AI SOC space would recognize immediately.

If you've been following the agentic AI conversation and wondering how much of it is real versus noise, this episode is worth your time. Adi doesn't oversell where the technology is. He's direct about what still needs to mature before agentic process automation can scale the way people expect it to. And he knows the difference between a real shift and a rebranding exercise.

The TechSpective podcast is available on all major podcast platforms. You can also watch the full episode on YouTube.