Hindsight Over a Database

Why Our Team Chose Hindsight Over a Database The obvious solution was a database. A simple SQLite file, a tasks table, a decisions table — done in 20 minutes. Our AI Group Project Manager would read from it on every request and write to it after every interaction. Clean, predictable, debuggable. We didn't do that. Here's why, and what we learned from the choice. The Problem With the Database Approach When we sat down to design the system, the database option felt safe. But the moment we thought through what the agent actually needed to do, the cracks appeared. A database stores rows. What we needed was for the agent to answer questions like: "What's still pending from last week?" "Who's most overloaded right now?" "What decisions are relevant to Keerthana's current task?" These aren't row lookups. They're semantic queries over connected information. A SQL query for "what's relevant to this question?" doesn't exist. You'd have to build a retrieval layer on top of the database anyway — e