The Rise of Agentic AI — and the Coming Chaos of Shadow IT

There’s a new kind of “Shadow IT” forming—and this time, it’s invisible, tireless, and doesn’t wait for approvals.

Welcome to the age of Agentic AI.

When AI agents go rogue, and your org has no plan, disaster ensues.

AI agents aren’t just glorified chatbots. These are autonomous agents that don’t just answer questions—they take action. They file tickets. Query APIs. Schedule meetings. Move money. Create records. They’re being adopted right now in marketing teams, sales ops, HR departments, and yes, even the C-suite—where someone inevitably asks ChatGPT to “automate our business.”

And unlike earlier waves of Shadow IT, these agents aren’t just rogue spreadsheets or unauthorized SaaS logins. They are active, connected, and empowered. Which means they’re also dangerous. Especially when they’re used without the disciplines of product management, software engineering, or even basic systems thinking.

The New Shadow IT Comes Without Discipline, Security, or Guardrails

This is the part we need to say out loud: Agentic AI is going to run headlong into the walls we’ve spent the last two decades building—governance, QA, security reviews, stakeholder alignment—and bust right through them.

Because Agentic AI doesn’t come with:

• Clear requirements

• Role-based permissions

• Usability testing

• Transaction limits

• A product manager asking, “Wait, what’s the problem we’re solving?”

What it does come with is the power to execute on poorly written prompts, half-baked ideas, and vague wishes—instantly, and at scale.

Which leads us to the real concern: these tools don’t just amplify productivity. They amplify dysfunction.

Bad Scenarios: When AI Goes ‘Helpful’

SELECT * FROM Chaos

A salesperson asks their AI agent, “Give me a list of all my clients.” The agent doesn’t have business context or account-level permissions. So it queries the entire customer table—every record, every region, every status—and sends the results to a personal Google Sheet. Including private notes, historical invoices, and restricted accounts.

Now you've got PII sitting outside your firewalls, compliance flags flying, and an overwhelmed sales rep who just wanted a filtered report.

Build Me an Accounting System—Whatever That Means

The CEO has a thought during a Zoom meeting and types into their AI agent: “Set up an accounting system for us.” With no context, no stakeholders, and no scope, the agent connects to a generic finance tool, guesses at chart-of-accounts logic, and enables auto-payments.

Weeks later, Finance logs in to find misclassified expenses, duplicate vendor entries, and half a dozen unpaid tax liabilities. But hey, it’s technically “done.”

The Email That Crashed Monday

An eager intern uses an AI agent to generate a daily performance report. The intern never scoped the data. The AI pulls everything: logs, system metrics, and raw JSON objects. It attaches the results to an auto-email that gets sent to the executive team every morning.

On Monday at 8:00 AM, every VP in the company gets a 22MB CSV file with no context and no explanation. Legal gets nervous. The CTO gets angry. And the intern? Quietly reassigned.

Your AI Just Spent $18K on API Calls

Your vendor switched from seat-based pricing to usage-based. Meanwhile, one of your ops managers set up an AI agent that pings their API every 30 seconds to check for “real-time updates.”

No one reviewed the cost implications. No one added a throttle. One month later, you get a surprise invoice: $18,000 in usage fees for data you already had cached.

Good Scenarios: Where Product Thinking Wins

The Smart Sales Sidekick

This agent is scoped and configured correctly. It has access only to the rep’s accounts, applies filters, limits queries, and summarizes pipeline activity into a digestible Monday morning briefing.

No raw data dumps. No over-permissioned requests. The rep gets actionable insight without creating security risk or performance drag. It’s the right kind of automation: controlled, purposeful, and aligned with the role.

AI with a Finance-Approved Blueprint

Instead of improvising a solution, the product team partners with Finance to capture real needs: role-based access, multi-currency support, invoice matching logic, and audit trails. The agent is deployed to assist with tasks like categorizing expenses or tagging vendors—but only within the limits of pre-defined rules.

It doesn’t own the process. It makes the humans in the loop faster and more consistent.

The Disciplined AI Rollout

In this scenario, AI adoption looks like a real product rollout. The use case is defined, the data is scoped, the agent operates in a sandbox first. It’s reviewed by legal, monitored by IT, and tested by support before going live.

The agent helps triage tickets: tagging duplicates, suggesting responses, escalating issues with contextual summaries. It augments—not replaces—the team. And because product management was involved from the start, the whole thing runs like a well-oiled machine.

The Real Problem Isn’t AI. It’s Artificial Product Management.

Most people don’t know how to define requirements. And AI doesn’t magically fix that. It makes it worse—because now those vague requests become executable commands.

• The CEO who says, “I just want an accounting system” and walks away.

• The salesperson who says, “Give me all clients” without realizing they just triggered a 10-minute database query.

• The manager who sets up an AI agent to “clean up” CRM data without backing anything up first.

This isn’t the AI’s fault. It’s ours. We’ve unleashed agents with no training, no guardrails, and no strategy—because it’s easy.

The result? Not automation. Not transformation. Just entropy, at scale.

Before You Deploy Agentic AI, Ask These Questions

• Is the problem clearly defined?

• Is the data scope constrained and filtered?

• Who owns the outcome—and the consequences?

• Are there API cost implications?

• Has the output been tested or reviewed?

• Is a human in the loop?

If the answer to most of these is “I don’t know,” then your AI isn’t augmenting your business. It’s guessing at it.

Agentic AI won’t replace product management—but it will expose whether you’ve been doing it well.

Because these tools don’t just execute; they accelerate. And if your processes are sloppy, misaligned, or undefined… the AI will amplify all of it.

You can use this technology to automate your strengths. Or let it automate your dysfunction.

The choice is yours. But the agents? They’re already here.