Interesting direction — this feels like the natural next layer for AI systems once they move from “single model usage” to production-grade agent workflows.
The focus on control boundaries (sanitization, injection defense, tool access limits, audit trails) is especially important because most real-world failures in AI apps today are not model errors, but interaction-level vulnerabilities.
What stands out in your breakdown is the shift from passive logging → active governance:
not just observing behavior, but constraining it in real time not just detecting issues, but preventing unsafe execution paths
In practice, teams usually end up balancing all three: visibility for debugging, boundaries for safety, and policy for consistency — but the hard part is making those layers work without killing system flexibility.
Curious how you see it evolving: do you think most teams will build this in-house, or will control-layer frameworks like this become standard infrastructure?
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