What does AI native look like in practice?
Tom Willis, VP of Software Engineering and Product at Flexion, has been working it out one project at a time. He uses agentic loops to do the heavy lifting and keeps a human in the loop for the decisions that matter.
His biggest test so far is Opportunity Capture, a pipeline built to support Flexion’s business development by finding government and commercial work the team was missing. The volume it now handles is a long way from where it started, but the first version didn’t go to plan, and what he learned from that early mess changed how he thinks about where AI belongs in a workflow.
In this episode, Tom walks Matt Sharp and Holly Fake through the Claude Code loop behind the build and the test-driven development habits that keep it honest. He also shares why he won’t make big productivity claims, and what running LLMs locally in a home lab has taught him about AI cost optimization. Along the way, AI turns up in a few less expected places, from the kitchen to a tabletop RPG.
00:00 Introduction
01:18 What AI native means right now
05:04 The Opportunity Capture project
10:35 Test-driven development with Claude Code
12:13 How agentic loops work
18:03 AI as an amplifier of good practice
22:55 Cooking with AI
29:56 Running LLMs locally in a home lab
33:37 Why constraints build better AI habits
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Learn more about Flexion: https://flexion.us/
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