Artificial Intelligence (AI) agents are steadily emerging as a distinct class of digital actor outpacing traditional oversight.
AI agents can autonomously access systems, trigger workflows, and make decisions faster than the organization can detect, leading to growing security, compliance, and reputational risks. They cannot be governed like traditional IT assets or earlier AI models because they act across systems.
Altaz Valani, Principal Advisory Director at Info-Tech Research Group, said many people will have multiple agents working for them, but AI agents cannot be governed the way humans are. This is because AI agents move quicker and lack emotions, conscience, and consequences.
According to Info-Tech Research Group’s new report, AI agents will outnumber human workers globally by 2029. These automated intelligence systems will operate across the enterprise with speed, autonomy, and access that existing AI governance was never built to handle.
The persistent digital actors cannot be governed as humans or software and without governance built for their autonomous behavior, they can go rogue at any time. Treating them like human actors or traditional applications creates blind spots in governance. Define each agent as a persistent digital actor with explicit identity, ownership, and controls, anticipating that they can go rogue at any time.
Sounds something out of a science-fiction thriller, doesn’t it!
Profilerating AI Agents
AI agents are spreading fast across business websites on the internet. Even some digital news media portals have some sort of AI agents interacting with the users, taking in their personal information and storing cookies!
The report states that organisations cannot reliably control agent behavior, especially when agents are externally sourced and evolving rapidly. Instead of changing model behavior, reduce operational space by applying controls based on risk tiering across autonomous actions, system access, and business impact.
A phased-out approach can be taken to establish agentic AI governance authority and guardrails, define the governance model, and look into the operational oversight and accountability.
By doing so, organisations can move from a one-time approval method to ongoing governance that manages risk as agents operate, enables safe experimentation, and gives leaders a clear view of exposure as AI use expands across the organization.
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