Agentic AI governance: infrastructure, risk models, and safety best practices
Agentic AI systems do not just generate answers; they plan and take actions — calling tools, moving data, executing steps toward a goal. Governance changes when the system can act.
Why this matters for the AIGP exam
Agentic AI is the newest material in Domain IV, and the exam tests whether you see why action changes the risk analysis, not just the vocabulary.
The essentials
- Autonomy compounds error. A wrong answer misleads once; a wrong action triggers consequences, and a chain of actions can compound a small early mistake into a large outcome before anyone looks.
- Tool access defines the blast radius. An agent's risk profile is set less by its model than by what it can touch: payments, email, code, production systems. Governance starts from the tool list.
- Least privilege applies to agents exactly as it does to people: the narrowest permissions that let the task succeed, separately scoped credentials, and no standing access to what the current task does not need.
- Human approval gates go where consequences are irreversible — sending, paying, deleting, publishing. The design skill is placing gates at consequence boundaries rather than everywhere or nowhere.
- Observability and a kill switch are non-negotiable: full action logs, the ability to interrupt a run, and a tested path to revoke an agent's access immediately.
What the exam asks
Identify what makes an agentic scenario riskier than a generative one, and pick the control — scoped permissions, an approval gate, logging, a kill switch — that fits the described failure.
Going deeper
This page is the condensed version. The full topic — with the detail above expanded and practice questions attached — is in the app, inside Domain IV. Domain I, a quarter of the course, is free to try first.
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