What is Agentic AI?

AI isn’t just evolving—it’s growing teeth. Let’s trace the arc from search to action:

• Google Search – You ask. It shows results.
• ChatGPT / GenAI – You ask. It writes or explains.
• Custom GPT – You train it. It answers in your context.
• AI Assistant – You define the task. It helps you complete it.
• Agentic AI – You set the goal. It initiates, executes, and updates—without waiting for your next prompt.

Agentic AI may seem like just another rung on the AI ladder, but don’t be fooled. It differs in two vital ways:

1. It acts without being asked.
2. It doesn’t just respond—it takes action.

Imagine saying:

“We’ve got a tax audit next month. Can you help us prepare?”

A normal GPT might offer a checklist, link guidance material, and define terms.
You still do the work.

Agentic AI gets moving. It can extract relevant data from your ERP and tax engine, scan for anomalies, reconcile positions, draft summaries, issue reminders, and tailor outputs to your jurisdiction—all while you get on with your day.

This isn’t automation. It’s orchestration.

Why It Matters for Tax

Agentic AI represents a profound shift for tax teams—from reactive compliance to proactive strategy. It has the potential to close the gap between what systems could do and what actually gets done.

But here’s the reality check: Agentic AI is only as smart as the data and governance behind it. Like every other technology, it won’t fix broken processes or bad inputs. And it won’t replace skilled professionals, but it can amplify their impact by removing repetitive drag.

To work well, it needs structured data models, connected systems, and smart oversight. Tax leaders must supervise this, but it requires a new kind of leadership.

So What Now?

Agentic AI, especially the kind with autonomous reasoning, is still emerging. Its success depends on timing, readiness, and whether it’s solving a real problem.

Many teams will rightly take a “don’t fix what isn’t broken” stance. That’s valid. But as we’ve seen before—with cloud, data tools, even basic e-filing—those who wait too long risk playing catch-up under pressure.

Others will wait for agentic AI to be embedded in existing tools. Vendors are already moving. For example, UiPath, a leading RPA (robotic process automation) tool provider, recently launched UiPath Maestro—an AI-native evolution of its product suite. ERP vendors are circling the same idea, though most of it is still surface-level—for now.

Most professionals already use GenAI tools (like ChatGPT or Microsoft Copilot) for productivity, but few have crossed into the realm of agents.

But before you build or buy your first agent, understand the process. Start by asking your preferred GPT:

“What would it take to evolve from asking you questions to building an AI agent that acts on my behalf in a tax context? Include steps for setting goals, connecting to systems, and automating workflows.”

Let your GPT map it out. Then decide what role you want AI to play.

⚠️ One last warning: this isn’t plug-and-play. About 30% of agentic AI projects fail outright, and many more deliver less than expected or create new problems entirely.

PS: This is a revised version of an earlier article. Why? — I want to improve it as a knowledge source for the custom GPTs I have built or am building.

Until next time …