From Art to Science: Wild Moose and the Future of AI-Powered Debugging

About this episode

The episode features the founding team of Wild Moose discussing their innovative approach to AI-powered production debugging.

In this episode, we sit down with the full founding team of Wild Moose — CEO Yasmin Dunsky, CTO Roei, and VP R&D Tom Tytunovich — to explore how they’re transforming production debugging from an art into a science using AI. The trio shares their unconventional founding story — from meeting across three different cities to living together for three months in a California Airbnb to stress-test both their idea and their relationship. They discuss how they identified production debugging as a massive unsolved problem before ChatGPT even launched, recognizing that while code generation is fundamentally a text problem, debugging is a search problem that demands a completely different approach. We dive deep into Wild Moose’s “microagents” architecture — fast, highly optimized AI agents that replicate the muscle memory of senior engineers to automatically investigate production incidents in under a minute. The team explains why accuracy trumps everything in their space (wrong answers are worse than no answers when you’re debugging at 3 AM), how they navigate the speed-cost-quality triangle, and why they built a test-driven approach to validate agents against past incidents. We also…

People in this episode

Hosts: Kostas, Nitay

Guests: Yasmin Dunsky, Roei, Tom Tytunovich

Topics covered

• AI-powered debugging
• production debugging
• microagents architecture
• software engineering
• machine learning
• incident management

Keywords

• debugging
• AI
• software development
• microagents
• production incidents
• machine learning
• observability

Mentioned in this episode

Organizations: Wild Moose, ChatGPT

Places: California

More episodes of Tech on the Rocks

• Physical AI and the Future of Robotics with Sergey Arkhangelskiy of Positronic · June 5, 2026 · 52 min
• Building the Open Lakehouse for the AI Era with Shubham Baldava from DataZip / OLake · May 21, 2026 · 58 min
• From Session Replays to Autonomous Improvement: Shipping the First AI Product Engineer with Milana · April 24, 2026 · 1h 0m
• From Exabyte Storage to Reactive Backends: Jamie Turner on Building Convex After Dropbox · April 9, 2026 · 59 min
• From Notebooks to Production: Xorq’s lockfile Approach for Reproducible, Portable ML Pipelines · January 29, 2026 · 57 min
• From pandas to Arrow: Wes McKinney on the Future of Data Infrastructure · December 1, 2025 · 1h 22m