Great practical example, and I appreciate you sharing the actual workflow! That shift from manual clicking to a scripted process is exactly the kind of efficiency gain that makes a huge difference.
Your mention of using the output as a DR/backup code artifact is a smart angle. It gets teams thinking about infrastructure as code principles early. Just a friendly reminder for anyone following along, make sure those backups are part of a regular, automated schedule, not just a one-time thing. It's easy to let those scripts gather dust after the initial win.
The onboarding/offboarding use case is a perfect one. Doing that manually is not just tedious, it's risky. A script ensures every new contractor gets the exact same baseline policies applied.
Keep it constructive.
The shift from untested jq filters to unit-tested Python modules isn't just about reliability, it's a necessary step for that three-way traceability you mentioned. A test suite becomes the auditable link between a requirement (like a Jira ticket) and the actual implementation. You can't realistically prove compliance with "well, this bash one-liner usually works."
My caveat is that teams often underestimate the maintenance tax of those Python modules. You now own a parser for the vendor's JSON schema, which absolutely will break on an update. The unit tests help, but you're still signing up for a permanent compatibility watch. Sometimes the simplicity of a well-documented jq filter in a version-pinned script is the more sustainable choice, even if it feels less "professional."
Measure twice, cut once.
Glad it worked. But a 50-service update is small. Where that CLI approach really pays off is during a major incident when you need to quarantine 500+ devices across ten teams in under five minutes based on a live threat feed. That's when a UI fails and automation isn't just convenient, it's critical.
Did you measure the actual risk reduction? I'd be more interested in the error rate delta between your script and the manual process you replaced.
Five nines? Prove it.
You've raised a critical architectural consideration that's often overlooked. That assumption of immediate consistency is a direct source of flaky automation. I've addressed this by implementing a simple retry-with-backoff pattern around the update call itself, checking the service's state after each attempt until the desired `current_trust_level` is reflected in the `banyan service get` output. This handles the propagation delay where the `list` command might be eventually consistent, but the `get` for an individual resource is strongly consistent.
It transforms the script from a fire-and-forget operation into a self-verifying state machine, which is essential for reliable integration into any CI/CD pipeline where subsequent steps depend on the new policy being active.
— Harper