Agentic AI and AI agents are not the same thing. The terms get used interchangeably, but they describe meaningfully different levels of autonomy — and from a governance standpoint, that difference is crucial.
How they compare
Agentic AI is human-triggered: a person initiates the task, the AI decides how to execute it, and a human reviews the result. Most organizations are already using agentic AI, even if they aren’t calling it that.
AI agents pursue independent goals, trigger themselves on a schedule or event, and operate with minimal real-time human oversight. An AI agent operating on a schedule with access to procurement systems has more in common with an employee than with a chatbot.
"Once we hit the age of agentic AI, all of our historical AI governance tools and tradecraft will break."
Geoff Schaefer, Chief AI Officer, Leidos
Why this distinction matters for governance
Agentic AI and AI agents carry different risk profiles, require different oversight mechanisms, and raise different accountability questions. Four areas deserve particular attention.
Irreversibility. Some agent actions can’t be walked back — a financial transaction that’s settled, content that’s been published, data that’s been permanently deleted. Irreversibility should be treated as a primary factor in risk assessment.
Prompt injection. Agents that process external content are vulnerable to indirect prompt injection, where adversaries embed hidden instructions to hijack the agent’s behavior.
Why this distinction matters for governance
Agentic AI and AI agents carry different risk profiles, require different oversight mechanisms, and raise different accountability questions. Four areas deserve particular attention.
Irreversibility. Some agent actions can’t be walked back — a financial transaction that’s settled, content that’s been published, data that’s been permanently deleted. Irreversibility should be treated as a primary factor in risk assessment.
Prompt injection. Agents that process external content are vulnerable to indirect prompt injection, where adversaries embed hidden instructions to hijack the agent’s behavior.
Three ways agents take action
Not every agent acts the same way. Understanding how an agent reaches into the world is the fastest way to size its risk — and to decide what controls it needs before deployment.
What needs to change in your governance program
Agentic AI governance doesn’t require starting from scratch. Organizations with existing governance programs have a foundation to build on. What’s needed are targeted updates in four areas: defined scope per agent, controls proportional to autonomy, pre-deployment accountability, and active shadow agent management.
Each of these maps to a capability your program likely already has for traditional AI — extended to account for systems that act on their own. The organizations that adapt early will be the ones still in control when autonomous AI becomes the norm rather than the exception.