“Agentic AI” is the phrase of the moment — software that doesn’t just answer questions but takes actions, chains steps together and works towards a goal. It’s genuinely useful, and genuinely over-promised. The trick is telling the two apart.

What agents are good at today

The reliable wins are bounded, repetitive, multi-step tasks with clear rules and room for review: drafting a first response, gathering and summarising information, moving data between systems, or preparing a recommendation for a person to approve. In these settings an agent saves real time without much risk.

Where the hype outruns reality

Agents struggle where judgement, accountability or messy context matter — anything with ambiguous goals, high stakes, or no clean way to check the result. Handing an agent broad autonomy over important decisions is how organisations get fast, confident mistakes. Today’s agents are best treated as capable assistants, not unsupervised staff.

Where to start

Pick one narrow, valuable workflow with a human checkpoint at the end. Define what “good” looks like, let the agent do the legwork, and keep a person accountable for the outcome. Prove the value there, learn how it behaves, then widen the remit. Start small, keep a hand on the wheel, and let trust be earned.