Antoine
Craske
Central thesis
Redesigning technology organizations for the agentic era.
AI does not simply make engineers faster. It changes the production capacity—and the constraints—of the entire technology system.
Flagship idea
Engineering production systemsThe defining question
What happens when AI agents become part of the engineering workforce?
This is not a story about assisted coding. It is a redesign of the engineering production system: its capacity, constraints, controls and economics.
Read the flagship thinkingSignature framework
The AI Engineering
Production System
Greater production capacity does not remove constraints. It relocates them—toward verification, architecture, governance, context and the organisation’s ability to absorb change.
Operating evidence
Constraint migration in practiceAI is the latest acceleration of an old pattern.
For 15+ years, I’ve worked on increasing the production capacity of technology organisations — from DevOps and CI/CD to Quality Engineering, cloud-native platforms, APIs, event-driven architecture and data transformation.
Each time, removing one constraint exposed another.
At La Redoute, this meant moving from large quarterly releases and firefighting to daily deployment, eventually reaching 40–80 deployments per day and less than 10 minutes from commit to production.
The lesson was never that we had eliminated the bottleneck. The bottleneck had moved.
AI agents introduce a much larger increase in engineering production capacity. My research explores where the constraint moves next — and how organisations need to redesign their engineering production systems to absorb it.
Intellectual territories
Six connected fieldsAI Engineering Transformation
The redesign of engineering production systems as agents join the workforce.
02Systemic CTO
Technology leadership understood through systems, constraints and decision flows.
03Enterprise Architecture
Architecture as an active decision system for complex organisations.
04Data & AI
The architectures, context and governance required to turn models into change.
05Technology Operating Models
How structure, incentives and flow shape the capacity to deliver technology.
06Quality Engineering
Verification, feedback and governance as the system that sets the pace of innovation.
Selected publications
View the archiveAI Engineering Transformation
The Absorption Gap: Why Your AI Adoption Metrics Are Lying to Your Board
AI is increasing engineering supply faster than organisations can absorb it. The real job of the CTO is no longer making engineers faster. It is redesigning the system that now has to keep up with them.
Perspective
Leadership · Architecture · TransformationTechnology is a system of decisions, constraints and consequences.
Antoine Craske is a senior technology executive, enterprise architect and systems thinker working across AI and data, engineering transformation, quality engineering and technology operating models.
His work connects technological possibility with the organisational systems required to make it real.
Read the biography06 / Work with Antoine