Governing agentic AI: when guard rails drive growth
Human judgment is an indispensable feature, not a brake, in agentic operations
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PICTURE a junior product manager working on a report on his laptop. Meanwhile, in the background, an autonomous artificial intelligence (AI) agent files expenses, schedules meetings and drafts a client memo. The efficiency is impressive, but the agent sends calendar changes to the wrong distribution list due to a misconfigured permission. This mistake then becomes a reputational issue for its firm.
This is the paradox of agentic AI – where the same autonomy that lifts productivity can also scale errors. As organisations move rapidly from assistive AI tools to agents that plan, act and iterate on their behalf, the core question shifts from “Is the answer right?” to “Who is responsible for what the system just did?”
Now, more than ever, a clear operating model for agentic AI – one that is principles-based, autonomous and accountable with runtime control – is imperative. Smart rules do not slow agentic AI; they accelerate responsible deployment by making accountability and controls explicit. Singapore’s Model AI Governance Framework for Agentic AI offers that clarity to turn caution into forward motion.
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