Agent · MIT Technology Review
Unlike static, rules-based systems, AI agents can learn, adapt, and optimize processes dynamically
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But unlocking their potential requires redesigning processes around agents rather than bolting them onto fragmented legacy workflows using traditional optimization methods.
Key facts
- According to Stanford’s 2026 AI Index, AI is sprinting, and they're struggling to keep up
- With technology budgets for AI expected to increase more than 70% over the next two years, AI agents, powered by generative AI, are poised to fundamentally transform organizations and achieve results
- Exclusive: Niantic's AI spinout is training a new world model using 30 billion images of urban landmarks crowdsourced from players
- This content was produced by Insights, the custom content arm of MIT Technology Review
Summary
Unlike static, rules-based systems, AI agents can learn, adapt, and optimize processes dynamically. In an agent-first enterprise, AI systems operate processes while humans set goals, define policy constraints, and handle exceptions. “You need to shift the operating model to humans as governors and agents as operators,” says Scott Rodgers, global chief architect and U.S. With technology budgets for AI expected to increase more than 70% over the next two years, AI agents, powered by generative AI, are poised to fundamentally transform organizations and achieve results beyond traditional automation.