An AI agent writes good code when it knows what “good” means in your codebase. Usually, it does not. It cannot see the utility functions you already have, understand your architectural conventions, or recognize the patterns your team expects. As a result, it often recreates existing logic, introduces inconsistencies, and produces code that feels disconnected from the rest of the system. And because each session starts with little or no memory, yesterday’s correction is often forgotten today. That is not the model being bad. It is the model working without the context and guidance that a new colleague receives on day one. In this talk, I show how to combine quality gates and engineering practices into a harness around AI-assisted development, so that problems are caught by automated checks instead of by users. We build the harness step by step, examine where each practice helps and where it breaks down, and explore the trade-offs involved. Finally, we look at how to measure whether these techniques actually improve outcomes, using data and evidence rather than impressions.
Ario is a Lead Software Engineer and Tech Innovator with deep expertise in platform engineering, distributed systems, and cloud-native architectures. Specialist in Java, microservices, and middleware (Kafka, Spring, Kubernetes), with a focus on data engineering and AI integration. Proven track record of architecting scalable enterprise systems while leading cross-functional teams with empathy, agility, and continuous mentorship.
