Agent memory of past pipeline postmortems catches recurring warehouse bugs faster

A developer built a demo agent that consults stored postmortems from five data pipelines at a fictional logistics company to diagnose a missing-rows issue. The agent recognized a fiscal-week boundary bug that had caused an earlier outage, reaching the diagnosis in four queries instead of six. The stack uses an open-weight model for the agent loop, Hindsight Cloud for memory, and SQLite with Streamlit for the demo. The memory stores incidents as prose with exact identifiers preserved, and failed first responses are recorded so the agent learns dead ends as well as fixes. The author notes that retrieval returns extracted facts, so results are regrouped by incident ID and shown to the model as whole postmortems.