Risk Architecture for AI-Native Engineering Teams: An Organizational Framework for Agentic System Governance
Engineering management research has produced mature frameworks for software risk: ownership by feature, escalation by severity, and assurance by test coverage. These frameworks implicitly assume deterministic behavior, discrete and auditable change events, and clear component-to-owner mappings. Teams that build and operate agentic AI systems violate all three assumptions at once: outputs are probabilistic, systems take autonomous multi-step actions, and the risk surface mutates silently between deployments. Existing AI risk literature addresses this from above (policy frameworks such as the NIST AI RMF and ISO/IEC 42001) or below (threat taxonomies such as OWASP's agentic AI guidance), bu...
| Authors | Laxmipriya Ganesh Iyer |
|---|---|
| Primary category | cs.SE |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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