Sunalini Sankhavaram

speaker

315 appearances 1 recordings 1 series first heard Jul 2026 last heard 17 Jul

Sunalini Sankhavaram’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.

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Heavy Networking · HN834: HPE Self-Driving Networks: The Next Evolution of Network Automation (Sponsored) · 17 Jul 2026 podcast

I thought through all the data, just like a human would.

It comes up with an evidence of everything it found.

It comes up with a recommendation of how to fix and a summary of what the overall root cause was, right?

And now how we do that is where I will hand over to Jose to walk us through, well, how does Marvus know all this, right?

In a reactive mode.

So there's a slow driving mode where agents already help us find issues and fix them.

And there's a reactive mode, which is where the rest of the world is.

in trying to figure out a problem.

But Jose, tell me more about or tell the team more about how we find these problems and how does agentick help.

And I think that's the key layer of differentiation, right?

So from what Agentik brought brought to the technology industry was this long nonlinear, non-deterministic way of thinking, right?

Where it could look at multiple models, multiple skills on how to trouble to the problem, multiple data sets, and sort of put it all together with the reasoning that it has.

Whereas classic AI helps us understand proactively what could be impacting a network, but it's only that

domain expert, but it gives you the efficacy.

So a combination of classic AI with with very high efficacy models, plus it all stitch together with the agentic layer, which is non-deterministic, gives you this accelerated

Achievement of what we call a self-driving network.

Because now I've I'm not starting from scratch.

I'm not just putting an LM on an MCP server and tools where the LM is doing everything.

The LM now has the benefit of learning from the classic AI models as triggers and then putting it all together with this non-linear, not you know, non-deterministic programming that it has as its key hallmark and giving you a recommendation, giving you a summary, which again goes back into my

say hey for that root cause I can fix it, you want me to go fix it.

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