Most AI agent security conversations are about preventing bad outputs.
That is the wrong problem.
The real problem is not what an agent says. It is what an agent does.
There is a meaningful difference between static guardrails and runtime control.
Static guardrails sit at the model layer. They shape how a model responds. They are useful for content policy, tone, and output filtering. But they were built for a world where AI generates text, not one where AI takes actions.
Runtime control is different. It sits between the agent and the outside world, at the moment of execution. It governs what tools the agent can call, what parameters are allowed, which actions require human approval, and what gets logged.
When an agent can send emails, query databases, trigger webhooks, write to files, or interact with MCP servers, static guardrails are not enough.
You need a layer that enforces policy at the point of action.
That is what we are building at Enforra.
Not a content filter. A control layer.
The agents are getting more capable. The actions are getting higher stakes. The infrastructure to govern those actions needs to exist before something goes wrong, not after.
If you are building AI agents that touch real systems, the question is not whether you need runtime control. It is whether you have it yet.
website: https://www.enforra.com/
GitHup: https://github.com/enforra/enforra
Top comments (0)