In this episode of Request Response, I sit down with Scott Dietzen, CEO of Augment Code and former CEO of Pure Storage.

We dive into why context selection has become more critical than prompt engineering, and how his team solved the fundamental challenge of giving AI agents just the right amount of codebase context to be effective without being overwhelmed.

If you're working with large codebases, building developer tools, or wondering how AI coding assistance scales beyond startup-sized projects, this episode is a must listen.

Show Notes

[00:00:53] – From machine learning PhD to startup CEO: Scott's journey to Augment Code
[00:02:05] – Anti-vibe coding: tackling enterprise-scale codebases with millions of lines
[00:02:45] – Enterprise customers and graduating from Cursor to Augment
[00:04:38] – The context problem: why LLMs struggle with massive codebases
[00:05:39] – From prompt engineering to context engineering as the new bottleneck
[00:06:35] – MCP adoption and helping ISVs package documentation for AI
[00:07:22] – The risk of getting lazy with API design in an AI world
[00:08:35] – Metaprogramming with agents and the importance of code review
[00:09:22] – AI-generated testing improving coverage and unlocking legacy codebases
[00:10:27] – The $2.5 trillion software failure problem and the promise of AI
[00:11:22] – Backlog zero: the holy grail for developers
[00:12:02] – Lessons from distributed systems: simplicity and reliability in commercial software
[00:12:48] – Engineers as tech leads for agents: human judgment still essential
[00:14:02] – Augment's differentiators: context engine, security, and IDE compatibility