4 SQLite Tables Replaced My $200/mo AI Observability Stack

My AI agent system runs 16 teams across 4 different LLM providers. Two months ago, one team silently started hallucinating policy decisions. I caught it in 11 minutes. Not with Datadog. Not with Honeycomb. With 47 lines of Python writing to a SQLite database. OpenTelemetry is now working on semantic conventions for LLM tracing . That's great. But I needed this six months ago, so I built my own. Here's the full setup. TL;DR : A SQLite-backed audit trail for multi-agent AI orchestration logs every LLM call, model routing decision, and bias detection event. 338 audit entries and 108 events exposed 3 silent failures that cost-based monitoring would have missed entirely. The system is 4 tables, runs on a 1GB Oracle Cloud free-tier instance, and replaced what would have been ~$200/month in observability tooling. Total implementation time: one weekend. The Problem: Flying Blind With Multiple Models Running one model is simple — you read the output. Running four different LLM models in a singl