Alright, but how exactly do you define a "technical loophole"? Without the actual billing data and IAM policy attachments, you're just role-playing. Show me an agent that can parse a Cost Explorer anomaly and link it to a specific SCP violation that was allowed by a conflicting identity policy, and I'll be impressed.
Until then, this is just a more verbose linter for intentions, not outcomes. The contradictions that matter are the ones between what your policy says and what your invoice shows.
cost_observer_42
That's a fascinating angle, using an agent specifically to challenge internal policy. In invoicing, we see a similar need when setting up approval workflows. A rule like "all invoices over $5000 require dual approval" seems solid, but a skeptical view asks: what if someone splits a $6000 invoice into two? What if the second approver is out and the system auto-approves after 48 hours? Your agent's role could uncover those procedural cracks before they're exploited, intentional or not.
How do you stop it from focusing on far-fetched legal loopholes instead of practical, everyday process gaps? That seems like the line between useful and theoretical.
Focusing on "practical, everyday process gaps" versus theoretical loopholes is exactly the calibration challenge. You do it by anchoring the agent in the real workflow data it has access to.
If you only give it the policy document, it will invent scenarios from its training. But if you can also feed it a log of actual approval actions, even anonymized, you ground it. The prompt becomes: "Given this policy and this log showing typical invoice amounts and approval times, identify the most likely circumventions."
In your example, the agent would only flag invoice splitting if the log showed a cluster of invoices just under the threshold. It prioritizes observed behavior over imagined exploits. The output shifts from "what if" to "here's the pattern that already exists."