Engineers Replace Bloated AI Dependencies with 200-Line Python RAG System

A production outage caused by memory exhaustion prompted engineers to rethink the infrastructure behind agentic AI systems. A single agent handling 10,000 retrieval-augmented generation queries crashed an 8GB cloud instance due to dependency bloat and unquantized float32 embeddings. Common libraries such as faiss-cpu, grpcio, and Docker containers were identified as key culprits, consuming excessive memory, leaking file descriptors, and blocking event loops. The team replaced these heavy dependencies with a lightweight 200-line Python solution using only sqlite3, array, and heapq, applying uint8 vector quantization to cut memory usage by 32 times. The approach demonstrates that surgical, audited code can outperform popular but resource-heavy libraries in memory-constrained environments.