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The governance landscape has shifted from managing isolated agents to controlling massive fleets facing "agent sprawl," where low-code tools have outpaced centralized tracking capabilities. Organizations must now evolve their infrastructure to handle simultaneous lifecycle management and risk mitigation across hundreds of thousands of autonomous units to prevent token waste and data leaks. The core challenge is no longer deploying individual models but scaling governance platforms that enforce permissions and observability at enterprise scale.

Surse care acoperăTowards Data Science, LessWrong, AI Alignment Forum, SiliconANGLE

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How to Govern AI Agents

Towards Data Science4 octombrie 20268 min de cititArticolul și perspectiva sunt în engleză.

From guarding one agent to steering a fleet The post How to Govern AI Agents appeared first on Towards Data Science.

This time last year, I released a Deeplearning.ai course with Andrew Ng on Governing AI agents with the goal of educating developers on the basics of data protection for effective and responsible agent deployment. The motivation for the course came from IBM's 2025 breach study, which found 97% of the organizations that suffered an AI-related breach lacked proper AI access controls, and 63% had no AI governance policy at all. A year is an eternity in AI development, and we have come a long way from agents with zero governance or grappling with governing a single agent. Teams are now confronting a new governance challenge: agent sprawl (the uncontrolled growth of autonomous AI agents across an organization without centralized tracking, ownership, or governance). According to Gartner, by 2028 the average Fortune 500 enterprise will use over 150,000 AI agents. However, according to the firm, only 13% of organizations believe that they have the right AI agent…

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