production-ai
Production AI
Your AI works in the demo. Production is a different animal.
I help engineering teams put AI into production safely — and stabilize the systems that are already breaking there. This section covers the five disciplines that work consists of.
The gap between "works" and "production"
A coding agent that writes great code on a laptop is not the same thing as fifty engineers running agents against your monorepo. A chatbot that answers questions in a demo is not the same thing as that chatbot holding customer data, calling your internal APIs, and failing at 3 a.m. while nobody is watching.
Most AI failures in production are not model failures. They are operational failures: no review gate on agent-written code, no observability into what agents did, no rollback, no cost ceiling, no permission boundary. The model was fine. The system around it was missing.
The five disciplines
- Readiness audits — find out what agents will break before they get production access.
- Stabilization — the engagement shape for AI that is already live and already breaking.
- Agent observability — see what agents did, what they spent, and what they broke before customers tell you.
- Deployment guardrails — a deploy process engineered so that normal human (and agent) error cannot take production down.
- AgentOps — the operating discipline that holds all of it together once agents are a permanent part of your system.
I run this on my own systems
This is not a methodology I read about. Every code change on my own engineering rig — including the pages you are reading — goes through a cross-model AI code-review gate, with a different model family reviewing every PR and a human retaining sole merge authority. One audited week of that workflow (September 21–28, 2026) covered 180 merged PRs across 16 repositories, 967 gate reviews, and 6.57 billion tokens, with a 96.5% prompt-cache hit rate holding cost down.
Figures below come from one audited production week (September 21–28, 2026) across my own GitHub account — merged-PR counts, gate logs, and token accounting, published weekly on the Turbo Rig stats page. They are measurements of my own workflow, not industry averages.
The full numbers and how they were measured: the Turbo Rig case study.
Where to start
If nothing is live yet, start with the readiness audit. If something is live and hurting, start with stabilization. If you want the short version of either, the consulting page lists the engagement shapes and deliverables.
Talk it through with someone who runs this stack on his own systems every day.