Cortex co-founder and CTO Ganesh Datta sits down with Dr. Martin Nettling, Senior Director of Engineering and Head of QA at Tealium, to explore the distinction between trusting people and having confidence in tools, and why that difference matters as AI becomes part of every engineering workflow.

Martin and Ganesh discuss the augmented author model, where engineers take full responsibility for AI-generated outputs rather than treating the tool as the author. They get into what breaks when AI writes your Jira tickets or pull requests without a human validating the thinking, why the ability to trace any decision from organizational strategy to production logs is a chain teams can't let break, and how organizations can build genuine confidence in automation without automating away the exercises that actually make teams learn.

Cortex co-founder and CTO Ganesh Datta talks with Aleks Rudzitis, Principal Engineer at AWS, about how the structure and theater of ops reviews can turn reliability into a shared organizational value.

Aleks explains how error budgets make reliability standards concrete rather than aspirational, why async reports can't replace the psychological weight of a live meeting with leadership, and how AI has made it feasible to backfill tests and even formal verification proofs that were previously too labor-intensive to justify. He also shares what it takes to get an ops review program off the ground in organizations that don't already have the habit.

Cortex co-founder and CTO Ganesh Datta sits down with Shawn Burke, Distinguished Engineer at Cortex, to explore what separates an operational excellence review that drives real engineering behavior from one that produces great conversation and nothing else. Shawn draws on experience from SoFi, Uber, and Microsoft to explain why these reviews so often fail—and how to build a process that actually sticks.

Shawn and Ganesh walk through why senior leadership presence is non-negotiable for the process to matter, and why fully automated reporting is the difference between a meeting that sustains and one that dies. They cover golden signals, SLOs, and how error budgets help teams balance feature work against reliability investment. They also discuss how AI coding assistants are introducing new operational risks around code review quality and flaky tests, and why AI agents may eventually act as a chief of staff—surfacing the why behind the metrics so teams can act on patterns instead of just observing them.