Why AI Coding Agents Lose Critical Context Between Sessions and How to Fix It

Modern AI coding agents like Claude Code and Cursor can now autonomously read repositories, plan tasks, and ship multi-file code changes, dramatically boosting developer productivity. However, each agent session builds two layers of state: the persistent work product and the ephemeral understanding of codebase conventions, which vanishes when the session ends. This loss forces every new session, developer, or CI process to re-derive project conventions from scratch, creating costly redundancy and inconsistency across handoffs. The core argument is that agents are consumers of context, not its source, meaning any convention or pattern the team wants reliably reproduced must be explicitly documented inside the repository itself. To work effectively across sessions, agents need persistent, repo-level documentation covering the tech stack, file conventions, approved UI components, and standardized recipes for recurring tasks like webhook handlers or database migrations.