Developer Fixes AI Coding Agent That Remembered Chats But Forgot How to Do the Work

A developer building CliGate, a local control plane for AI coding assistants, discovered that maintaining session continuity did not prevent agents from inefficiently relearning the same workflow details on repeated tasks. The root problem was that agents retained conversation history but lacked structured memory of what actually made a previous run succeed, such as known dead ends, environment quirks, and user preferences. To fix this, the developer replaced raw execution logs with a compact, file-based memory layer storing procedures, facts, directives, and references from past runs. The system now recalls the previous best approach first, verifies each step, and updates memory after success rather than replaying steps blindly. Separating standing user preferences from ordinary conversation history further made the assistant more predictable without requiring it to rediscover the same rules mid-task.