The Memory Layer That Stops AI Agents From Starting Over

AI coding agents like Cursor and Claude Code excel at code editing and PR management but lack persistent understanding of system context, forcing them to repeatedly re-read codebases and rerun expensive LLM calls. ASIL addresses this fundamental limitation by creating a temporal, causal knowledge graph that integrates code, deployments, incidents, and metrics—enabling agents to reuse verified engineering conclusions and dramatically reduce LLM costs. This infrastructure layer transforms AI agents from context-blind tools into genuinely intelligent systems that understand not just what changed, but why it failed and what evidence supports that conclusion.