Learn how Coinbase made investigations 72% faster using AI
In-person meetup at Resolve AI SF HQ | Duration: 30 minutes | Originally delivered: January 29, 2026
Inefficient engineering workflows impact revenue and costs every day.
To name just a few: It takes 3 - 6 months for new engineers to onboard, changes take too long to deploy and trigger incidents, and customers report issues before engineering teams discover them. Workflow speed bumps like these also negatively impact developer velocity, wellbeing, collaboration, and effectiveness.
As a result, incident resolution takes a lot of time, people, and effort.
Zscaler wanted to change this, so they evaluated AI SREs and AI for prod solutions and then selected, implemented, and trained Resolve AI.
In this tech talk, Duncan Winn, VP of Engineering and SRE Lead at Zscaler, highlights Zscaler's use case, evaluation and implementation strategy, and outcomes using Resolve AI, including reducing investigation time by 75%.
Varun Krovvidi
Product Marketing Manager
Varun is a product marketer at Resolve AI. As an engineer turned marketer, he is passionate about making complex technology accessible by blending his technical fluency and storytelling. Most recently, he was at Google, bringing the story of multi-agent systems and products like Agent2Agent protocol to market
Hear AI strategies and approaches from engineering leaders at FinServ companies including Affirm, MSCI, and SoFi.
Learn how to evaluate an AI SRE to ensure they run in your unique production environments. This guide explains the five agentic pillars, six key evaluation dimensions, and enterprise readiness criteria that separate production-ready AI SRE from experiments.
100 Engineering software engineering executives joined Resolve AI and other luminary leaders to discuss the accelerated evolution of agentic AI in software engineering from coding to managing production systems.
