Most AI conversations in organizations today follow the same pattern.
Someone types a question. The AI answers it. The person reads the answer and decides what to do next.
That model is already becoming obsolete.
The next wave of AI — agentic AI — doesn’t wait for your next question. It doesn’t just answer. It acts. It browses the web, reads your files, writes code, sends outputs to other systems, and completes multi-step tasks from start to finish, with minimal human involvement in between.
For small and mid-sized businesses, that’s an extraordinary opportunity. It’s also a governance problem that most organizations aren’t remotely prepared for. And the window to get ahead of it is closing.
What ‘Agentic AI’ Actually Means — and Why It’s Different
Let’s be clear about what we’re talking about, because the word ‘agentic’ gets thrown around a lot right now without much explanation.
Traditional AI — the kind most people are using today — is reactive. You prompt it. It responds. You’re always in the loop between each step.
Agentic AI is different. It operates with a goal, not just a prompt. You tell it what you want to accomplish, and it figures out the steps to get there — making decisions, using tools, and taking actions along the way without stopping to ask for approval at every turn.
A few real examples of what this looks like in practice:
- You ask your AI assistant to research three vendors, compare their pricing, draft a summary email to your team, and schedule a follow-up meeting. It does all of it.
- You ask it to review your contracts folder, flag any agreements expiring in the next 90 days, and draft renewal notices. It does all of it.
- You ask it to monitor your inbox for customer complaints, categorize them by type, and update your CRM. It does all of it — continuously, in the background.
This isn’t science fiction. Tools like Claude from Anthropic are already operating in agentic modes today. And they’re being adopted by businesses right now — often without IT knowing, often without compliance being consulted, and almost always without a governance framework in place.
Why This Is a Different Risk Category Entirely
Here is the critical thing I need you to understand: an AI that answers things is a tool. An AI that does things is an agent. And agents require governance in a way that tools simply don’t.
When a human employee takes an action — sends an email, modifies a record, executes a transaction — there’s a chain of accountability. There’s a person who did it. There’s a log. There’s a manager. There’s a process.
When an agentic AI takes that same action, that chain gets murky fast. Who authorized it? What data did it access along the way? What decisions did it make autonomously? What did it send, where, and to whom? Can you reconstruct the audit trail if needed?
For SMBs operating in regulated industries — healthcare, financial services, legal, retail with cardholder data — these aren’t philosophical questions. They’re compliance requirements. And right now, most organizations can’t answer them.
The specific risks that keep me up at night when it comes to agentic AI in SMB environments:
- Unauthorized data access. An agentic AI, given broad permissions, can read files, emails, and systems it was never explicitly intended to access. Without tight scoping, you may not know what it accessed until after something goes wrong.
- Unintended data exfiltration. If an agent is summarizing documents and sending outputs to external systems — a CRM, an email thread, a third-party platform — sensitive data can leave your environment in ways nobody planned for.
- Action without approval. Unlike a human who might pause before hitting send on something unusual, an AI agent will complete the task it was given. Without clear guardrails, it may take actions your policies would never have permitted.
- Liability without clarity. When an AI agent makes a decision that causes a problem — a contract goes out with wrong terms, a customer gets incorrect information, a file gets deleted — who is responsible? Right now, in most organizations, nobody has answered that question.
- Audit trail gaps. Regulators don’t accept ‘the AI did it’ as an explanation. They want to know who authorized it, what controls were in place, and how you would detect and respond to a failure. If you can’t answer those questions, you have a compliance gap.
The ‘It’s Just Claude’ Misconception
I want to address something I hear often when I raise these concerns with business owners and IT teams.
‘We’re just using Claude to draft emails and answer questions. We’re not doing anything fancy.’
That may be true today. But agentic capabilities aren’t something you opt into with a special enterprise purchase. They’re being rolled into the tools your team already uses — as default features, as integrations, as browser extensions, as plugins inside platforms you’ve been paying for for years.
Claude, for example, already has agentic capabilities available. Microsoft Copilot is being embedded across the entire Office 365 ecosystem with increasing autonomy. Google’s AI is integrated into Workspace in ways that allow it to take actions across Drive, Gmail, Calendar, and Meet.
You don’t have to deploy agentic AI intentionally for it to end up in your environment. In many cases, it’s already there.
The question isn’t whether your organization will encounter agentic AI. It’s whether you’ll have a governance framework in place when you do.
What Good Governance Looks Like for Agentic AI
The good news: you don’t need to boil the ocean. You need to think about four things.
1. Scope and permissions.
Every agentic AI deployment should have clearly defined boundaries. What systems can it access? What actions can it take? What data is off-limits? Treat it like you would a new employee with broad access — you’d define their role before you gave them the keys. Do the same here.
2. Human-in-the-loop checkpoints.
Not every step in an agentic workflow needs human approval. But certain categories of action should always require it — anything that sends external communications, modifies customer records, executes financial transactions, or touches regulated data. Define those checkpoints before you deploy, not after something goes wrong.
3. Audit logging.
If your agentic AI takes an action, you need a record of it. What was the task? What steps did it take? What data did it access? What did it produce or send? This isn’t optional in most regulated industries — it’s a control requirement. Make sure your tooling supports it and that someone owns the log.
4. An AI acceptable use policy that covers agentic behavior.
Most AI policies written in the last two years were written for the reactive model — prompting and responding. They need to be updated for agentic use. That means addressing autonomous action, data handling during multi-step tasks, scope limitations, and what employees are and aren’t permitted to ask AI agents to do on behalf of the company.
The Bottom Line
Most compliance frameworks were written for a world where humans take actions and systems record them.
Agentic AI flips that. Now systems take actions and humans try to figure out what happened afterward.
That’s not a technology problem. That’s a governance problem. And governance problems are solvable — but only if you start working on them before the incident that makes them impossible to ignore.
The organizations that will navigate this well aren’t the ones with the biggest IT budgets or the most sophisticated AI tools. They’re the ones who asked the right questions early:
- What is our AI actually doing inside our environment?
- Who authorized it to do that?
- What would we do if something went wrong?
- Can we prove it?
If you can’t answer those questions today, that’s your starting point. Not panic. Not a six-figure compliance overhaul. Just clarity.
That’s always where good governance begins.
Need help building an AI governance framework for your organization? The We Make Sure team works with SMBs and IT teams to close exactly these kinds of gaps — before they become incidents.