Brex builds AI agent control layer at network level, not in rules

The article identifies a structural gap in how AI agents are currently managed. OpenClaw and similar agentic frameworks have become widely adopted, but they operate without proven safeguards at enterprise scale. The challenge is that agents need genuine credentials—API keys, OAuth tokens, service accounts—to be useful, yet traditional guardrails (permissions embedded in the agent's code or baked into the model) cannot effectively limit what agents do once those credentials are in hand.

Brex's insight is architectural: the network layer becomes the enforcement point. Every request an agent makes traverses the network, creating an observation and control opportunity that is independent of the agent's code or training. By placing a proxy at this chokepoint and having it evaluate each request against policy rules using an LLM-as-a-judge, Brex sidesteps the limitations of SDK-level permissions and model-level guardrails. Franceschi's message to IT leaders is that this shift—from application-layer controls to network-layer controls—should become the standard practice for agent governance.