Shadow AI for Planner-Executor Agents

What good looks like when shadow AI is under control

Imagine a fleet of planner‑executor agents that receive guidance from an AI model, but every suggestion is visible, auditable, and bounded by policy. Engineers can trace why a particular plan was chosen, regulators can verify that no hidden data leakage occurred, and the system can automatically block risky actions before they reach the executor. In this ideal state, shadow AI becomes a transparent partner rather than a black‑box influence.

Why shadow AI is a hidden risk for planner‑executor agents

Planner‑executor architectures often treat the AI component as a helper that produces a plan, which the executor then carries out. In many deployments the plan is passed as a raw string or JSON payload directly to the executor without any intermediate inspection. This creates three problems:

• Unobservable influence: The AI may embed confidential data or privileged commands that never surface in logs.
• Unrestricted execution: The executor runs whatever it receives, even if the plan violates least‑privilege principles.
• No real‑time guardrails: There is no point where a policy engine can approve, mask, or reject a step before it touches production resources.

Teams typically rely on the planner’s identity (a service account) and the executor’s IAM role as the only controls. Those setup pieces decide who can call the planner, but they do not enforce what the planner’s output can do once it reaches the executor.

The missing enforcement layer

The precondition for a secure system is that every request from a planner‑executor pair travels through a data path where policy can be applied. Without that path, the request reaches the target directly, leaving the organization without audit trails, without inline masking of sensitive fields, and without the ability to require a human approval for high‑risk actions. In other words, the setup (identity, least‑privilege grants) is necessary but never sufficient.

What is needed is a gateway that sits between the planner’s output and the executor’s input. This gateway must be able to:

• Record each session so that auditors can replay exactly what the executor received.
• Mask or redact confidential values that the AI may have generated.
• Enforce just‑in‑time approvals for commands that cross a risk threshold.
• Block disallowed operations before they are sent to the executor.

Only a data‑path component can guarantee those enforcement outcomes.

How hoop.dev closes the gap

hoop.dev is a layer‑7 gateway that sits on the network edge, proxying connections to infrastructure resources. When a planner‑executor workflow is routed through hoop.dev, the gateway becomes the only place where the plan is inspected. Because hoop.dev controls the data path, it can apply the enforcement outcomes listed above.

Specifically, hoop.dev records each session, creating a replayable log that can be reviewed during investigations. It can mask fields that match patterns for PII or secrets, ensuring that downstream systems never see raw sensitive data. For high‑risk commands, hoop.dev can pause the flow and trigger a just‑in‑time approval workflow, letting a human decide whether to proceed. If a command violates a policy rule, hoop.dev blocks it before it reaches the executor.

All of these capabilities rely on the fact that hoop.dev is the only point where traffic between the planner and executor is visible. The surrounding identity system (OIDC, SAML, service accounts) still decides who may start a session, but the actual enforcement happens inside hoop.dev.

Deploying hoop.dev is straightforward: the gateway runs as a container or Kubernetes pod, and a lightweight agent lives near the executor. The agent holds the credentials for the target resource, so the planner never sees them. For a step‑by‑step walkthrough, see the getting‑started guide and the broader feature documentation.

FAQ

Is shadow AI eliminated by using hoop.dev?

No. hoop.dev does not remove the AI component; it makes the AI’s output observable and enforceable. The AI still generates plans, but every plan must pass through the gateway where policies can be applied.

Can hoop.dev work with existing planner‑executor pipelines?

Yes. Because hoop.dev proxies standard protocols, you can point your existing planner client to the gateway address and keep the same executor binaries. No code changes are required on the planner or executor side.

What happens to a session that is blocked?

hoop.dev records the attempted command, the reason for the block, and any approval decision. Operators can review the log to understand why the request was denied and adjust policies if needed.

Explore the hoop.dev source on GitHub