Cerbere-AG -- The layer that ensures security, observability, and control, which sits between the AI agent and the real world

Cerbere-AG is a three-layer security guardrail for AI agents: Regex, ML, and an LLM Judge.

AI agents are unpredictable. They improvise and act with full autonomy. I built Cerbere-AG (named after the three-headed dog, Cerberus) so that autonomy doesn’t come at the cost of control.

Why three layers? Our benchmarks show it: → Regex alone lets more than 30% of attacks through. → ML pushes recall up to 100% in some of our tests, but raises false positives. → The LLM Judge resolves the ambiguous cases ML can’t classify. It also looks at context.

Cerbere-AG follows tool calls, checks whether they’re legitimate, and then allows them, blocks them, or flags them for real-time human approval. Agents keep their autonomy, and humans keep control. Everything is audited too.

The collector gathers spans and traces into an audit trail that is independent from the agent and supports compliance. And like any tool, it has limits. That’s why observability matters: security isn’t only about blocking attacks, it’s about understanding what happened, what was allowed, what was blocked, and why.

Open source and free for now. Try it and give me your honest feedback 👇 https://github.com/chrismsmr-celcom/cerbere-AG https://app.cerbereag.site