The Future of AI in Government • NAU Public Service Academy
A chatbot responds to your message. An agent pursues your goal: it plans, uses your tools, takes action, and loops until the job is done. That shift, from answering to acting, is what makes AI “agentic.”
The gold ring above the agent is the ReAct loop: reason, act, observe, and repeat, until the goal is met or a person needs to weigh in.
Forecasters put the move from pilots to real operations in 2026, and much of it arrives through software you already buy.
The tools went from answering questions to taking actions, and they are showing up in everyday software.
Agentic features arrive in your systems when Microsoft, Salesforce, or ServiceNow update, not only when you choose to build.
Federal guidance turned pro-innovation in 2025, and states are passing their own AI laws. AI readiness is now a compliance item.
There is real upside and there are real watch-outs. Both are the reason to understand this rather than ignore it.
How this settles is not yet known, and the early evidence points in more than one direction. One economic argument, the Jevons paradox, holds that when a service gets cheaper and faster, demand for it tends to grow, which would shift work toward higher-judgment tasks (framing problems, supervising AI, reviewing output) rather than remove it. The counterpoint: automation displaces workers from tasks and can reduce demand for labor even while it raises productivity, and the offset depends on new tasks appearing for people; in United States data, economists Daron Acemoglu and Pascual Restrepo found that offsetting effect weakened over the last three decades. The early data on AI carries its own caution: Stanford's analysis of payroll records finds hiring pressure on entry-level workers in the most AI-exposed fields, while experienced workers in the same fields have held steady so far. The finding is debated: in a 2026 response to critics, the authors report that under stricter statistical controls the decline starts later, in 2024, that other factors likely contributed early on, and that the pattern has continued to grow. Deskilling is a caution here too: over-reliance can erode the skills people stop practicing. The federal government moved its AI hiring toward demonstrated skills in 2024 (the OPM competency model). Questions for your office rather than predictions: which roles would you redesign around judgment and review, and if AI drafts the routine work, where do junior staff learn?
There is no single right answer. It depends where a tool sits on the spectrum, from a plain chatbot to an autonomous agent, and where a person stays in control. Let's walk it with one real request, handled five ways.
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