From Stateless to Stateful: Building Code Review AI That Learns From Team Decisions

Most AI development tools reset after every interaction, losing context about team preferences and coding standards. This article explores how adding a persistent memory layer—where the system tracks which feedback developers accept or reject—transforms a generic code reviewer into an adaptive teammate. By storing decisions in plain language and retrieving relevant patterns during each review, the agent evolves to match actual team norms rather than generic rules, demonstrating why LLMs benefit more from interpretable context than structured schemas.