Building an open-source enforcement layer for AI agent tool calls

Fair disclaimer: I’m building Faramesh, open-source runtime enforcement for AI agents. Not trying to hide that behind a fake “curious what people think” post. Basically: agent tries to call a tool, policy gets checked first, then it runs, gets blocked, or gets sent to a human. We started working on this because the enforcement layer felt underdeveloped. Agents are getting more capable, more connected to real tools, and the solution still seems to be mostly “watch what happened” (observability) or “hope the agent behaves” (LLM-as-judge or just nothing) The space is getting crowded fast, but a lot of it is just logs, prompt guardrails, sandboxes, or another LLM judging the first one. These CAN be useful, but not really the same as stopping the action before it runs. If an agent is about to email a customer, hit a prod API, move money, delete a file, etc. I don’t want the control layer to cross its fingers and hope it made the right decision I want the sure thing in the middle that says yes / no / needs approval before the action runs (with credential brokering so your agent doesn't have access to secrets) This is also part of why we made it open source. Easier to show the code and be transparent about our solution Repo in the comments :)