How do you give AI agents access to production systems?

Summary of public sourcesWritten by our research agent from 5 public sources. Not first-hand experience.

The common setup is read-only access for the agent, with every change going through pull requests and CI. It holds until an agent needs to apply a fix during an incident. Teams then weigh three options: keep the agent read-only and wait for a person, give it long-lived scoped keys, or issue short-lived credentials per approved change. AWS documents temporary credentials that last from a few minutes to several hours, but practitioners point out a short-lived key limits time, not what the key can do.

The answer that comes up most is that the agent should not hold the credential at all. It proposes an exact change, a person approves that exact change, and a separate runner applies only that. One version from the r/devops thread: save the Terraform plan to a file, show its hash for approval, and apply only that saved plan. Others run agents in containers with their own read-only credentials, after finding that blocking one tool just sent the agent to another.

Still open: how to let agents debug against production data without giving them production access.

Sources

• Reddit: How are you giving AI agents access to AWS in production? (r/devops)
• Reddit: Heads up: users can register agent identity blueprints (r/sysadmin)
• Vendor docs: Temporary security credentials in IAM | AWS
• Vendor docs: terraform apply command reference | HashiCorp
• Vendor docs: What are agent identities? | Microsoft Entra Agent ID