There is a concept gap in the current AI agent stack.
Most teams apply safety at the model layer: system prompts, output filters, content policies. These work fine when the agent is generating text. They break down when the agent is executing.
The problem space looks different at runtime:
Your agent calls a tool. Which tools are allowed? With what parameters? Under what conditions?
Your agent wants to send a message, write to a database, trigger an API, or interact with an MCP server. Who decided those actions were permitted? How is that policy defined? How is it enforced? How is it audited?
Right now, most teams handle this with a mix of prompt instructions, hard-coded checks, and hope. That is not governance. That is technical debt with a timer on it.
Runtime control is a policy enforcement layer that sits between the agent and its tools. It evaluates each action against a defined policy before execution, routes sensitive actions for human approval, and logs everything.
It is closer to an authorization layer than a safety filter.
This is what Enforra is building. Not another wrapper around a language model. An infrastructure primitive for the agentic stack.
If you are working on agent frameworks, MCP tooling, or production agentic systems and want to think through this, I am happy to talk.
Website: https://www.enforra.com/
GitHup: https://github.com/enforra/enforra
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