Erhan Giral
speaker
389 appearances
1 recordings
1 series
first heard Aug 2026
last heard 3 Aug
Erhan Giral’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Aug 2026 with 1.
Appearances
Because sometimes technically challenging problems are just that, no one cares about, maybe it's a QA environment, maybe it's a staging environment that's being modified at the moment.
Essentially, from an economist point of view, we always ask, okay, we have a problem, but is this an impactful problem?
Should we mobilize human beings?
Should we spend more resources in mitigating this issue?
Our system constantly makes these decisions in the background by looking at real-time status of systems.
while also constantly comparing the state to its past self.
And we also make plans of action based on plans of actions that were executed in the past by human beings or by bots.
That's right.
So some parts of what I just described that essentially taking that gigabytes of gigabytes of monitoring data and then reducing and then essentially pushing that noise away.
That's typically we employ proprietary technologies to do that because there's so much data to process.
We couldn't really hope to expose all of that to the generative models all the time.
So we take all that sparse data and run through various machine learning and statistical analyzers first to basically find the needles in the haystack, if you will.
But once those needles are found, they are collated, combined, correlated, causally analyzed,
And then the LLM, you know, the LLM is given a pretty comprehensive, causally described, like a patient chart, like a set of x-rays of the system.
And then we ask the LLM, okay, I mean, we ask various questions to LLM, but most importantly, okay,
We ask the question why and what needs to be done, and that level is completely agentic, just like you said.
And there we use reasoning models, just like you said, maybe with a twist, because we want our models to reason just like one of the employees of our customers.
Because like, for instance, if you go to an open AI model or a generic off-the-shelf model, they'll always give you plausible responses, accurate responses based on the documentation, based on what they know about the space.
But it's often the case that when you go to a big organization, the rules of engagement around these systems are very different.
Meaning you can say, oh, go to this file and make these edits and everything should be fine.
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