AI governance in the agentic era

This article is part of a series on global AI governance law and policy.

Agentic artificial intelligence is an active area of exploration for many organizations. A recent IBM and Morning Consult survey of 1,000 enterprise AI developers found that 99% of respondents said they were exploring or developing AI agents. These agents are goal-driven, and their purpose is to perform tasks and carry out workflows in an autonomous manner.

AI agents tend to focus on specific tasks structured with simple workflows. At the same time, agentic AI tends to involve multiple AI agents that can carry out full end-to-end workflows with significant autonomy and may operate in more complex environments. Basic AI agents are not new; examples include antivirus software and robotic vacuums.

Agentic AI is a newer concept and can enable end-to-end workflows for key organizational processes such as IT incident management or managing customer returns.

Stanford's 2025 AI Index Report highlighted that AI agents already show signs of matching human capabilities for select tasks and can deliver speed and cost efficiency. Over time, agentic AI capabilities will likely continue to improve and adoption to increase. While this has many benefits to organizations and society, there are risks that must be managed.

"AI has accelerated rapidly, evolving beyond [large language models] LLMs and chatbots to assistants and now agents that can drive autonomous decision-making with continuous optimization. Agentic AI holds the potential to transform the way we work, unlocking new levels of productivity and allowing organizations to focus on more strategic priorities. As organizations implement these powerful systems, success will depend on having the right strategies and governance in place to enable them to responsibly scale interconnected agents to solve enterprise-wide challenges," Savio Rodrigues, vice president of Service Partners at IBM.