Policy as Code for Planner-Executor Agents

Are you wondering how to keep automated planner‑executor agents from drifting into unsafe territory while still enforcing policy as code?

Today many teams hand a single service account token to an AI‑driven planner, then let the same executor run arbitrary commands against databases, Kubernetes clusters, or SSH hosts. The token is often stored in a configuration file or environment variable that every pipeline can read. Because the agent talks directly to the target, there is no central point that can see which query was run, which pod was created, or whether a secret was leaked. Auditors see only the final state of the system, not the step‑by‑step decisions that led there.

The immediate fix many organizations apply is to create a dedicated non‑human identity for the planner‑executor pair and to grant it the minimum set of permissions required for the job. This satisfies the “least‑privilege” checklist, but it does not change the fact that the request still travels straight to the backend service. No gateway records the session, no inline mask can redact sensitive fields, and no just‑in‑time approval workflow can intervene if the planner proposes a risky operation. The request bypasses any enforcement layer, leaving the organization exposed to accidental data exposure, privilege escalation, or malicious misuse.

Why policy as code needs a gateway

Policy as code promises that every decision made by an automated agent is evaluated against a declarative rule set before the action is executed. To make that promise real, the rule engine must sit where it can observe the full request and, if necessary, block or transform it. That observation point is the data path – the network hop that all traffic must cross before reaching the target resource. When the gateway sits in that path, it can enforce the policy set, mask sensitive response fields, and record the entire interaction for later replay.

hoop.dev provides exactly that data‑path enforcement. It runs a Layer 7 gateway next to your infrastructure and requires every planner‑executor connection to pass through it. The gateway validates the incoming request against the policy‑as‑code definitions you have authored. If a rule says “do not allow DROP DATABASE on any PostgreSQL instance,” hoop.dev will intercept the command, reject it, and log the attempt. If a rule requires that any query returning credit‑card numbers be masked, hoop.dev rewrites the response in real time, ensuring that downstream consumers never see raw PAN data.

Because hoop.dev is the only place where the traffic is inspected, all enforcement outcomes originate from it. hoop.dev captures every command and response, creating an audit trail that can be reviewed or replayed. It can trigger a human approval step when a planner proposes a high‑impact change, such as scaling a production Kubernetes deployment beyond a defined threshold. It also ensures that the executor never sees the underlying credential; the gateway holds the secret and presents a short‑lived token to the target on behalf of the agent.

Key enforcement outcomes for planner‑executor agents

• Session recording: hoop.dev captures every command and response, creating an audit trail that can be reviewed or replayed.
• Inline data masking: Sensitive fields identified by policy are redacted before they leave the target, protecting PII and compliance‑related data.
• Just‑in‑time approval: High‑risk actions trigger an approval workflow, allowing a human reviewer to grant or deny the request in real time.
• Command blocking: Policies can explicitly deny dangerous commands, preventing execution at the gateway level.
• Credential isolation: The executor never receives the long‑lived service account token; hoop.dev presents a short‑lived, scoped credential to the backend.

These capabilities turn a loosely governed planner‑executor pair into a controlled, observable component of your automation stack. The enforcement happens where it matters – in the data path – and does not rely on the agent to implement its own checks.

Getting started with policy as code and hoop.dev

To adopt this model, begin by defining your policy rules in a declarative format (YAML, JSON, or a language supported by your CI pipeline). Next, deploy the hoop.dev gateway using the getting started guide. Register the planner‑executor as a non‑human identity, configure the appropriate target connector (for example, PostgreSQL or Kubernetes), and point the agent at the hoop.dev endpoint. The gateway will enforce the policies you have written without requiring changes to the planner’s code.

For deeper insight into how policies are evaluated and how you can extend the rule set, explore the feature documentation. The open‑source repository contains example configurations and test suites that illustrate common policy patterns for automated agents.

FAQ

What is the difference between a policy file and an approval workflow? A policy file defines static rules that are evaluated automatically on every request. An approval workflow is a dynamic step that pauses execution until a human reviewer explicitly grants permission for a specific high‑risk action.

Can I use policy as code for multiple agents simultaneously? Yes. Each agent can be assigned its own identity and policy set, and hoop.dev will enforce the appropriate rules for each connection independently.

How does hoop.dev handle credential rotation? The gateway stores the long‑lived service account secret and automatically rotates short‑lived tokens it presents to the backend, keeping the executor insulated from credential changes.

Ready to see the code in action? Explore the open‑source repository and start building policy‑driven safeguards for your planner‑executor agents today.