AgentVault: AI Sandbox & Leakage Sentinel

The nightmare of giving an AI agent direct access to a database. The nightmare of giving an AI agent direct access to a database

The biggest problem with AI agents is this. You can't debug what the AI is doing, so it's really hard to track down where something went wrong.What I know for sure:1.Stuff that has nothing to do with the current session got mixed in.What guessing:1.There's a minecraft.py file in the tool folder, and that might have triggered some hallucination.2.Maybe data from some other project on the user's local machine got mixed in somehow.3.Or it could be from another user's conversation.Honestly, if I think about how the system actually works, I don't think it's pulling from another user's data. But oth

The multi-agent budget problem you're describing gets even harder when the services are heterogeneous. In a RAG pipeline, a single user query might hit: query analysis (LLM call), embedding generation (different model/pricing), reranking (yet another model), and response generation (LLM call) — each potentially in a different process.Per-call monkey-patching sees each call in isolation. What I ended up doing was a trace-based approach: every request gets a trace ID, each service appends cost spans asynchronously, and a separate enrichment step aggregates the total. The hard part was ded