You See the Potential. Now Make It Deliver.

For engineering leaders tired of correcting plausible AI output, defending code quality, and reviewing more code than their team can safely ship.

This workshop helps your team use AI to improve delivery flow, not just generate more code.

The pain

• AI works on easy tasks, then falls apart on real work. Your codebase has domain rules, customer exceptions, brittle tests, messy data, and architecture in motion.
• The promised speedup becomes review burden. AI produces plausible output, but senior engineers still have to find the gaps, correct the assumptions, and protect quality.
• More code creates more inventory. If AI only helps you get to “dev complete” faster, it can flood a delivery system that still lacks automated tests, review capacity, or a clear path to production.
• The team lacks a shared way of working. Useful AI practices stay trapped in individual engineers’ heads, so results vary from person to person and week to week.

What you gain

• A practical model for how AI actually works. Move past the idea that AI is magic. Learn how LLMs work, what they are good at, where they are blind, and how to use them deliberately.
• A harness for real engineering work. Learn how to package context, define boundaries, create review lenses, and set up feedback loops that improve output over time.
• Better flow, not just more output. Use AI where it reduces friction across the delivery system, instead of creating code your team cannot safely review or ship.
• Reusable team practices. Leave with patterns your team can share: context packs, specs, review prompts, safety rules, and metrics for deciding whether AI is actually helping.

Format

The public course uses realistic examples that let everyone practice the same techniques together. You will learn the patterns, tradeoffs, and habits that make AI useful in production engineering work.

If you want to apply this directly to your own codebase, we can come in-house and run a private workshop around your real problems, workflows, and constraints.

About us

We are practitioners, not AI influencers. Our team has decades of experience in production software, BDD, testing, DevOps, SRE, release engineering, modernization, coaching, and agentic software delivery.

• Matt Wynne co-founded Cucumber and co-authored The Cucumber Book.
• Aldric Giacomoni has been building software through agents since 2023 and authored Stone, an agentic harness framework.
• David Laing brings deep DevOps, SRE, and release engineering experience from Pivotal, VMware, Shopify, and Mechanical Orchard.
• Zach Marcin has designed and taught agentic training for high-quality modernization work.
• Jeremy Lightsmith has 25 years in tech and spent three years at Mechanical Orchard helping teams use LLMs to modernize mainframes for Fortune 500 clients.

We are comfortable with skeptical senior teams, complex systems, and the gap between AI hype and production reality.