Over the next year, AI agents will increasingly operate across enterprise systems, pulling signals from CRMs, ERPs, HR tools, and communication platforms to draw conclusions and take actions without step-by-step human oversight. That ability to assemble context is not only what makes agents powerful, but is also what creates a new baseline of governance concerns.
Traditional access control asks “Which users are allowed into which systems?” But agents don’t just log in; they synthesize information across boundaries, infer new knowledge, and act in ways no permission model anticipated.
Recent surveys make this risk clear: 80 percent of organizations report that their AI agents have carried out unintended actions such as accessing unauthorized systems or resources, accessing or sharing sensitive or inappropriate data, and downloading sensitive content.
These incidents are reminders that once agents can engineer context and act autonomously, traditional access controls are no longer enough. What’s missing is a way to observe, audit, and govern their reasoning process. Here’s why.
Traditional role-based access controls were designed for people: define roles, grant permissions, and monitor who enters which systems. That model works when the main risk is unauthorized access. With agents, the challenge is different. What matters is not only where they go but how they assemble information once inside.
Take a finance agent with legitimate access to both payroll and expense data. By combining those datasets, it could surface insights the enterprise never explicitly authorized. If compromised, the same breadth of access could also allow the agent to move laterally into tax filings, vendor accounts, and compliance logs. Microsoft has already reported cases where compromised copilots became stepping stones into wider system infiltration.
The difference is that with agents, governance can’t stop at permissions. It also has to extend into how they reason: what data they combine, which inferences they draw, which ones they report to other agents and humans, and whether those conclusions remain within acceptable boundaries. That requires new forms of monitoring and auditing designed not only to track activity, but to make the reasoning process itself visible and reviewable.
When agents assemble context across systems, they can transform ordinary, non-sensitive inputs into outcomes the enterprise never authorized. This happens in two ways:
Together, these protocols ensure that context remains a source of value without drifting into disclosures or inferences that enterprises cannot control or defend.
Enterprises don’t just need powerful agents, they need agents that can carry equal weight to human coworkers. A new economy is already forming around this shift: companies that can trust agents with meaningful responsibilities will move faster, scale more efficiently, and unlock opportunities their competitors can’t.
That requires more than access; it requires explainability, accountability, and trust in how those agents operate. Those that establish observability, reasoning logs, and enforceable guardrails now will be positioned to lead in this new economy. Those that delay will find themselves reacting to incidents, struggling with compliance gaps, and losing ground to competitors.
Wand is laying the foundation that makes agent governance possible. Our operating system for the hybrid workforce — where humans and AI agents work side by side — is designed to give enterprises visibility, accountability, and control over how agents operate. It does this through four core pillars:
Together, these layers make agent governance tangible: giving companies the means to treat agents as teammates, not tools, and ensuring every action is explainable, verifiable, and aligned with enterprise priorities.
If you’d like to see how Wand can give you visibility and control over your agents, book a demo.