Developers lose critical context and understanding when collaborating with AI agents on complex system design

Software architects and senior developers struggle to maintain a shared mental model when working with AI coding agents, leading to misaligned implementations, wasted rework, and 'slop' code that doesn't match the intended design. Current tools (Claude Code, Codex) lack bidirectional traceability between high-level design decisions, visual specifications, and actual code implementation, forcing developers to manually reconstruct context and validate agent work. This context loss is especially painful during architectural decisions where tradeoffs only become apparent after implementation, requiring expensive rework cycles.