I Built a Persistent Memory API for AI Agents — Here's Why Vector Search Alone Isn't Enough

The Problem Every autonomous agent framework has the same silent failure: memory decay . Your agent works great on day 1. By week 3, it's confidently using stale facts, making decisions based on outdated context, and you don't notice until something expensive breaks. I've been running an autonomous AI agent 24/7 for two months. Here's what I learned about why agent memory fails — and how I fixed it. Why Vector Search Fails for Agent Memory Most agent memory solutions do this: Store facts as embeddings Retrieve by cosine similarity Hope for the best The problem: vector similarity ≠ fact accuracy . A fact can be semantically close to your query and completely wrong. Your API endpoint changed last week, but the old endpoint is still the closest vector match. Your agent confidently calls the dead endpoint, fails, retries, and burns tokens. The Missing Piece: Retrieval Scoring What if every fact had an accuracy score based on execution outcomes ? Agent retrieves a fact → uses it → task succ