Build or buy? See where eng teams are landing
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%.
Join our engineering leads for "Behind the Build", a webinar series deep-dive into how we built agents that run software.
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
The question isn't whether AI belongs in production anymore. Here's what engineers at AWS Summit NYC 2026 told us about how agents run your software, what guardrails they want, and how the pricing should work.
A frontier model can produce a thousand coherent answers. Most enterprise work needs exactly one correct one, and closing that gap is not a bigger model. It is the agent architecture around it. Here are the six layers that turn open-ended capability into a defined outcome, and why production incidents are the hardest test of whether they work.
Watch how Resolve AI investigates a service timeout from application logs through Kubernetes pods down to failing memory modules in a UCS blade - building a complete causation chain in 3 minutes. See the stark contrast between traditional multi-team incident response (4 teams, multiple tools, hours of coordination) and AI-native investigation that maps dependencies from app code to storage infrastructure without organizational handoffs. Learn why engineering silos slow incident response and how AI agents can reason across the entire production stack as one connected system.
