When an AI coding agent can invoke AWS APIs with a static credential, the lack of production access control can let a single slip rewrite production databases, spin up costly resources, or expose secrets to the internet. The financial impact of an unchecked deployment can run into millions, and the reputational damage is often irreversible.
Many teams hand the agent a long‑lived IAM key and let it call the AWS CLI or SDK directly. The key is stored in the build container, copied into CI pipelines, and occasionally checked into version control by mistake. There is no visibility into which command triggered a change, no way to stop a destructive operation before it reaches the service, and no audit trail that satisfies a post‑mortem.
What engineers really need is production access control that limits the agent to the exact actions required for a given task, forces a human review for high‑risk operations, and records everything for later analysis. Even with that intent, the request still travels straight to the AWS endpoint, meaning the cloud service sees the raw request without any guardrails, no inline masking of sensitive responses, and no approval step.
hoop.dev solves this gap by acting as a Layer 7 gateway that sits between the AI agent and AWS. The gateway intercepts every API call, applies policy checks, and only then forwards the request to the target service. Because hoop.dev is positioned in the data path, it becomes the sole place where enforcement can happen.
Setup: identity and intent
The first piece of the puzzle is identity. The agent authenticates to hoop.dev using OIDC or SAML, presenting a token that represents a service account or a short‑lived credential. This step decides who is making the request and what groups or roles they belong to, but it does not by itself restrict what the agent can do once the connection is open.
The data path: where enforcement lives
Once the identity is verified, hoop.dev becomes the conduit for every AWS API call. By sitting in the data path, hoop.dev can inspect the request payload, compare it against policy, and either allow, block, or divert it for approval. No other component in the stack has the ability to intervene after the request leaves the agent.
Enforcement outcomes delivered by hoop.dev
- hoop.dev masks sensitive fields in responses, such as secret keys or passwords, before they reach the agent.
- hoop.dev requires just‑in‑time approval for high‑risk actions like deleting a DynamoDB table or modifying IAM policies.
- hoop.dev blocks commands that match a deny list, preventing accidental or malicious destructive operations.
- hoop.dev records each session, capturing the full request and response stream for replay and audit.
- hoop.dev stores the audit trail outside the agent’s runtime, giving security teams a clear record of who did what and when.
Because all of these outcomes are produced by hoop.dev, they exist only while the gateway remains in the path. Remove the gateway and the protections disappear, which confirms that hoop.dev is the source of the security guarantees.
Getting started
To protect AI coding agents on AWS, deploy hoop.dev near your AWS resources, configure an OIDC trust with your identity provider, and define production access control policies that match your risk appetite. The getting‑started guide walks you through the Docker Compose deployment, and the feature documentation explains how to author policies for AWS services.
FAQ
Do I need to change my existing IAM roles?
No. hoop.dev uses its own service credentials to talk to AWS, while the AI agent presents an identity token that the gateway validates. Your existing roles can stay in place; hoop.dev simply adds a policy enforcement layer on top.
Can hoop.dev handle burst traffic from many agents?
Yes. The gateway is designed to scale horizontally, and because it operates at the protocol layer it can proxy thousands of concurrent API calls without becoming a bottleneck.
What happens to logs if the agent is compromised?
hoop.dev continues to record every request and response, so even a compromised agent cannot erase the evidence. The logs are stored outside the agent’s environment, providing an audit trail that can be reviewed for compliance.
Explore the source code and contribute to the project on GitHub.
