In the evolving landscape of enterprise AI, the rise of agentic systems marks a pivotal shift. AI agents are autonomous applications powered by large language models (LLMs) capable of reasoning, memory, and action. They are no longer passive responders to user input; they are active decision-makers influencing business processes in real time. But with autonomy comes risk, and with risk comes the need for structured supervision. In this new paradigm, supervision of tech systems is no longer optional, it is foundational.

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