What happens when a non-deterministic AI system is asked to touch production telemetry or generate changes for an SRE pipeline? The cost of being “close enough” can be lost data, downtime, or a security incident.
Cribl’s Nikhil Mungel joins Cory to break down what it takes to build AI that sysadmins can actually trust. The conversation digs into harness engineering and the practical guardrails that turn probabilistic models into repeatable, verifiable outcomes. They cover why breaking work into small chunks matters, how validation and testing become the real leverage point for AI-native development, and what “code factories” mean for review, CI, and platform reliability when teams can generate a thousand PRs an hour.
Platform engineers will also hear a pragmatic take on the future of the job. The focus shifts away from typing code and toward building systems for verification, simulation, and safe deployment at scale, plus clearer ways to decide what needs human scrutiny and what can ship automatically.
Guest: Nikhil Mungel, Head of AI R&D at Cribl
Nikhil Mungel is the Head of AI R&D at Cribl, where he's building LLM-powered systems for IT and Security data transformation and analysis. Before Cribl, he spent over a decade developing distributed systems across the observability and consumer social tech landscape. He lives in San Francisco with his wife and two kids. His current focus is applying AI to make complex infrastructure more intuitive and explainable.
Links to interesting things from this episode:
