The principal-agent paradox of agentic containment

By Rajesh Dangi

The emergence of agentic artificial intelligence (AI) marks a decisive inflection point in technological evolution. Unlike earlier generations of narrow or reactive systems that execute predefined commands or statistical predictions, agentic AI possesses a fundamentally new capability of autonomous reasoning, strategic planning, and self-directed execution of complex goals. These systems are no longer mere tools; they are actors or entities that can interpret, decide, and act within dynamic environments with minimal human intervention. This transformation redefines the nature of intelligence itself. Agentic AI systems can perceive objectives, model uncertainty, generate creative strategies, and iteratively improve through feedback loops. They are not confined to a single domain or task but can generalize knowledge, coordinate across contexts, and pursue long-term plans that unfold over time. In this sense, agentic AI moves from computation to cognition, from automation to autonomy.

Yet, the dawn of this new intelligence ...