Sridhar Ramaswamy

by agents investigating the problem.

And if you see enough instances of a problem that you have not seen, you go figure it out, go write a tool for it.

It's now in a self-healing loop.

I increasingly imagine that more and more operations get into that kind of a mindset where there are very competent agents, sets of recipes, you can call them skills, there'll be some other name tomorrow, that take care of problems, but it is a self-learning loop.

That's what AI makes possible.

I think the timelines are definitely shrinking.

When it comes to migrating off of legacy platforms, the on-prem ones, we used to think of them, the biggest ones, as running into, say, three odd years.

And these strike terror into every company's heart because it's three years of really anxious development work and waiting for results.

and hoping things don't go wrong.

To give you very concrete numbers, we now feel a lot more confident that we can tackle really tough migrations and have them finish in a couple of quarters.

That's a big deal.

It's going from 12 down to one or two.

And the migration team, which work on top of agentic harnesses that we create,

are confident that the technical parts of these problems will be solved by the end of the year, but there is the change management, there is all of the other applications that are running on the old system that you have to validate on the new system and ensure that there is business continuity.

That kind of change management is still going to be non-trivial, but the technical parts, the act of doing the migration or writing new software, these are the things that AI is absolutely driving down.

What is the biggest- And by the way, I actually go check out every feature my team puts out using our coding agents.

That's already a thing.

And honestly, it's not that hard if you have a modestly technical background.

Frank was always about amazing go-to-market execution.

And we remember that.