Building an Autonomous Data Pipeline Sentinel with Hierarchical Memory

Subtitle: How I Architected a Persistent PR Defense System Using FAISS, SQLite, and Automated Memory Consolidation TL;DR In my recent experiments, I built DataPipeline-Sentinel , a persistent OS for autonomous data pipeline incident management. I utilized a 4-tier Hierarchical Memory System (Context, Semantic, Episodic, Declarative) to enable genuine machine learning from past incidents. By combining FAISS for vector retrieval and SQLite for immutable logging, the agent instantly recalls resolved pipeline errors. I created a nightly Memory Consolidation background job to distill hundreds of raw logs into hard-coded declarative rules. This architecture shifts AI agents from stateless script-kiddies into seasoned, senior-level operators. All code is available in my public repository here . Introduction I observed a recurring nightmare in modern data engineering: pipelines break, engineers diagnose the issue, they apply a fix (like tweaking a Spark schema inference), and then... everyone