Coding Agents Don't Need Longer History — They Need Intent Continuity

AI coding agents often fail on long-running projects because they forget or miss critical requirements established in earlier conversations. Simply increasing the context window or using basic retrieval is insufficient, as the core problem is determining which historical rules are still valid and applicable. A proposed solution, termed 'intent continuity,' involves a pipeline that extracts, retrieves, and most importantly, verifies past requirements to see if they have been superseded. This verification-focused approach, implemented in pure Python without reliance on LLMs or vector databases, proved significantly more effective in benchmarks. The system successfully applied all historical constraints, demonstrating a robust method for maintaining project integrity over time.