In this talk, Redis and Wild Moose share how they're approaching AI SRE as something that can be designed, automated, and continuously improved. We'll explore how to build AI SRE systems that teams can actually trust in production by applying the same standards used for observability systems: fast, testable, configurable, and transparent.
Using a real-world example from Redis, we'll show how debugging agents turn tribal knowledge into structured investigation workflows and automate critical parts of the operational flow—creating a reliability system that improves with every incident. We'll also share why treating agents like production code, with regression testing and validation, is essential for long-term scale. You'll walk away with an understanding of the cultural shift needed to fully leverage AI for incident management.
Yasmin Dunsky is the Co-Founder & CEO of Wild Moose (YC W23), an AI-first SRE platform that helps engineering teams investigate production incidents in under a minute by turning tribal debugging knowledge into reliable, testable AI agents. Prior to founding Wild Moose, she led and scaled one of Israel’s largest nonprofits for computer science education, partnering with Google and Microsoft to build programs that reached thousands of students nationwide. She holds an MBA from Stanford and previously worked as a software engineer at Google.
Avner Yaacov is Director of Cloud Operations at Redis, where he leads global SRE, Cloud Operations, and Software Engineering teams responsible for the reliability, scalability, and operability of Redis Cloud. He manages the largest multi-cloud deployment of Redis, overseeing complex production environments across cloud providers and regions. With over a decade of experience in DevOps and SRE roles, Avner has built and operated large-scale distributed systems across cloud and high-performance environments. Over the past year, he has led Redis' shift toward AI-driven operations, focusing on moving from reactive operations to AI-assisted troubleshooting and automated remediation, while establishing clear standards for how automation is written, tested, rolled out, and trusted in production.
