Developer builds ATMAR framework to preserve context across multi-agent coding sessions

A developer working with AI coding agents repeatedly faced the problem of lost context when long-running features spanned multiple sessions or tools. To address this, they began storing structured feature trackers in the project root, capturing what was completed, pending, parked, and already decided. They also documented recurring behavioral rules to prevent agents from repeating the same mistakes across sessions. Over time, this evolved into a workflow called ATMAR — Agentic Tracker Memory And Retrieval — combining tracker memory, behavioral rules, and retrieval-based context. The core insight is that continuity should live in the project itself, not in the chat thread or the agent.