WHAT’S INSIDE
The Governance Question Changes from What the Model Might Say to What the System Is Authorized to Do
The first generation of enterprise AI governance was built for systems that generate content: a person prompts, a model responds, and a human decides what to do with the output. Agentic AI removes the human from that loop by design; an agent receives an objective, plans a sequence of steps, and executes them using tools, credentials, and system access.
This guide defines what makes agents architecturally different, sets out the control set that agentic deployments require, identifies the failure patterns emerging in early adoption, and presents a five-step roadmap aligned to the ARISE Framework™. In an environment where an agent can execute thousands of actions daily, reliance on periodic review is not a compliance gap; it is an operational liability.
What makes agents different. Four properties that defeat conventional control assumptions: agents act, agents hold identity, agents chain, and agents ingest untrusted input as instruction; prompt injection is a standing property of the architecture, not a defect to be patched once.
The required control set. Seven controls agentic deployments require: per-agent identity and least privilege, bounded tools and environments, human checkpoints at consequence, action-level logging, interruption and rollback, and continuous validation.
Failure patterns in early adoption. The agent inherits a person, logging the conversation rather than the conduct, unbounded scope by convenience, and no owner for the fleet; fleet governance must exist before the fleet does.
Appendix A control checklist. A ten-item pre-deployment checklist across identity and scope, checkpoints and reversibility, and observability and validation; a no on any item in the first two groups should block deployment until resolved.
