An AI coding agent that writes Terraform modules for a GCP project has just been added to a nightly CI pipeline. The agent pulls a service‑account key from a secret store, generates a plan, and runs gcloud commands against Cloud SQL, GKE, and Cloud Storage without any human eyes on the output.
Because the model can invent resources or misinterpret intent, a single stray command could create an open firewall, delete a production database, or expose private data. Organizations therefore demand approval workflows that force a person to review the exact API call before it reaches GCP, while still preserving the speed of automation.
A common pattern is to place a gate between the AI process and the cloud APIs. The gate inspects each request, matches it against policy, and either forwards it, holds it for review, or rejects it. By centralising this logic the environment gains a single point of control for every operation.
The first responsibility is setup. Engineers provision a service account in GCP with the minimal set of permissions required for the AI agent’s legitimate jobs. That account is then linked to an OIDC identity provider such as Okta or Google Workspace. The identity provider authenticates the AI process, issues a short‑lived token, and the gate validates the token before any traffic is allowed.
Once identity is verified, the request travels through the data path. hoop.dev sits on the network edge, intercepting every API call destined for GCP. Because the gateway controls the wire‑protocol, it is the only place where enforcement can be applied. The request never reaches Cloud SQL, GKE, or any other service until hoop.dev has approved it.
Enforcement outcomes are produced exclusively by hoop.dev. When a request matches a policy that requires human sign‑off, hoop.dev pauses the flow and creates an approval ticket that includes the full command, the originating identity, and contextual metadata. An authorized reviewer can approve or deny the request from the hoop.dev console. While the request is pending, hoop.dev records the session, masks any secret fields in the payload, and logs the decision. Once approved, hoop.dev forwards the request to the target GCP service; if denied, it blocks the call and returns a clear error to the AI process.
Because hoop.dev records each session, organizations gain a complete audit trail that can be replayed for forensic analysis. The inline masking feature ensures that credentials or personally identifiable information never appear in logs or on the screen of the reviewer. All of these outcomes exist only because the gateway sits in the data path; the underlying GCP services remain unaware of the additional controls.