loveholidays has been scaling AI-assisted development, with code committed with AI assistants now accounting for nearly 70% of our output.

In the same period, our code health has improved every quarter and deployments to production are up over 20% year-on-year. The challenge for our engineers was to maintain and improve quality while increasing productivity through AI-based innovation.

A year into our journey, we are finding that our existing engineering principles hold but the practices need adapting. This session will explore what it means for a codebase, team, and function to be "AI-Ready", structured around three areas of practice: guardrails, process, and knowledge. We'll examine how each area has evolved under agentic development, and the tension between established approaches and the adaptations we see emerging.

We will share the patterns our teams are using to make AI-assisted development work at scale: code health guardrails embedded in the agentic loop, build pipeline optimisations that prevent CI/CD from becoming the bottleneck, and approaches to encoding organisational knowledge into agent skills and data products that reduce guesswork and rework.