I built Canopy, an open source pre-execution governance layer for agentic AI systems. The core problem it addresses: there is currently no deterministic, auditable decision point between what an LLM agent decides to do and what it actually executes in production. The library evaluates every tool call through four independent layers before execution and writes a tamper-evident hash-chain audit entry on every decision. It has adapters for LangChain, CrewAI, AutoGen and OpenAI Agents SDK. Sharing here because the agentic governance gap is well documented in recent research and I think this community would have useful technical feedback on the approach, particularly around the limitations of pattern-based policy evaluation versus semantic understanding. Repo: https://github.com/Mavericksantander/Canopy submitted by /u/Josetomaverick
Originally posted by u/Josetomaverick on r/ArtificialInteligence