Sumeet Agrawal, Vice President of Product Management at Salesforce.gettyIf AI's the Wild West, who's the sheriff around here?Over the past few years, GenAI has transformed how work gets done—drafting content, answering questions and accelerating productivity across industries. We're now in a more consequential phase: the rise of agentic AI.Unlike generative systems that assist, agentic AI systems can act. They can interpret goals, break them into steps, interact with APIs and execute workflows with limited human intervention. Put simply, GenAI advises, while agentic AI executes.This shift is operational, and it fundamentally changes how organizations must think about risk, control and governance. This article will focus on AI governance and how to operationalize it in agentic AI.From Content Risk To Action RiskThe term "hallucination," widely used to describe when AI confidently generates incorrect information, became mainstream as AI adoption surged—even being recognized as the Cambridge Dictionary Word of the Year 2023. We've already seen real-world consequences stemming from business users relying on flawed AI-generated summaries, leading to poor decisions such as lawyers submitting AI-generated briefs with fabricated case citations or customer support bots delivering confident but incorrect responses.Research from MIT has shown that AI systems can produce outputs that appear highly convincing but are factually incorrect, making them particularly risky in high-trust environments. So far, these issues have largely been contained to content, but what happens when those same systems begin to act autonomously? A hallucinated answer is an inconvenience. A hallucinated action, such as executing a financial transaction or triggering an enterprise workflow, is a business liability.In the GenAI era, governance focused on accuracy, bias and data leakage. In the agentic era, governance must shift toward:• Authority: What is the AI allowed to do?• Access: Which systems can it interact with?• Accountability: Who owns its decisions?AI Governance: Top-Down Meets Bottom-UpEffective AI governance in the agentic era requires a combination of top-down direction and bottom-up enforcement.Top-down governance sets the strategic intent and accountability by designating executive sponsorship and ownership, establishing governance councils or review boards and defining AI policies, compliance standards and risk tolerance. This ensures alignment with business objectives, regulatory expectations and ethical boundaries.At the same time, bottom-up governance operationalizes these principles within the system itself by embedding controls directly into the architecture, enforcing access, security and execution constraints, and training developers and establishing engineering standards.Together, these approaches create a comprehensive governance model that combines policy with execution. Imagine deploying a fully autonomous self-driving car with a simple instruction: "Pick up clients." Without governance, it may speed through residential areas to optimize time, pick up the wrong individuals and ignore traffic rules. The result is loss of control, liability and broken trust.Now consider a governed system:• Scope: The car knows where it can operate, who it serves and the rules it must follow.• Guardrails: It has built-in controls such as speed limits, braking systems and collision avoidance.• Observability: Its behavior is continuously monitored.• Human-in-the-loop: A human can intervene in high-risk scenarios​.The difference between failure and safe autonomy isn't strictly the "intelligence" of AI agents; it's governance combined with controlled oversight.​Operationalizing Bottom-Up GovernanceTo make governance real in the agentic era, organizations must embed it from the bottom up into the AI life cycle itself.1. Organizations must clearly define boundaries before granting agents autonomy, including a careful understanding of each agent's role, workflows and data access. The organization can then limit the agent's authority based on business criticality. That also means identifying failure scenarios and defining where human approval is required before the agent proceeds.2. Organizations should implement identity and access controls, apply policy enforcement layers and action restrictions, and use sandboxing and centralized gateways for all interactions. Governance is most reliable when it's built in from the beginning and control is enforced by design. This means agents should operate only within explicitly defined permissions.3. Organizations need real-time monitoring, anomaly detection and intervening action to allow teams to catch unexpected behavior as it happens. Full traceability of actions and decisions backed by audit logs, human-in-the-loop escalation and kill switches ensures oversight remains actionable. In autonomous systems, visibility is control.Governance Enables ScaleDespite rapid advancements, enterprise AI adoption is limited by trust. Industry studies, including Salesforce's State of Data & Analytics report, highlight that concerns around data trust, governance and reliability remain top barriers to scaling AI.The pattern is clear: Low-risk use cases scale quickly, while high-trust use cases (finance, legal, healthcare) still face resistance. Confidence in outcomes is driving the gap between experimentation and production, and hallucinations—while only one part of the problem—have made that gap visible.AI is evolving from a tool that assists humans to a system that acts on their behalf. That transition changes the question entirely. It's no longer what AI can generate. It's what we're willing to let AI do and under what controls.Organizations that answer this well will scale AI confidently and responsibly. Those that don't will remain constrained by risk and lack of trust. In the agentic era, how well intelligence is governed will define success.The true potential of autonomous AI is realized at the intersection of data integrity and operational agility, where the integration of AI governance with agentic CRM creates a seamless feedback loop. This architectural synergy ensures that as organizations deploy agentic workflows, they're doing so on a foundation that's inherently governed, secure and contextually aware.​​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?