What SMB leaders should understand about agentic AI governance, risk, and autonomous AI agents.
A new term has been circulating in technology circles: agentic AI. If you haven’t heard it yet, you will soon. Put simply, agentic AI refers to artificial intelligence systems that can pursue goals and take actions autonomously rather than simply generating responses to prompts.
Unlike the generative AI tools most organizations are experimenting with today, AI agents are designed to take action. Agentic AI can retrieve information, interact with other software systems, make decisions within defined parameters, and carry out multi-step tasks with limited human involvement. In other words, they behave less like a search engine and more like a digital employee.
The concept isn’t entirely new. In many ways, agentic AI is simply the next step in a long evolution of automation. Organizations have been building systems that execute workflows for years: scripts that reconcile accounts, tools that route support tickets, software that monitors infrastructure and responds to alerts. What’s different now is mainly the degree of autonomy involved. Instead of following a rigid sequence of instructions, these systems can evaluate a goal, decide what steps to take, and adjust their approach along the way.
That capability is powerful. But it also introduces some essential governance questions many organizations haven’t fully considered.
What Agentic AI Means for SMBs
Most AI tools today are assistive. You ask a question, and the system produces a response—an analysis, a draft, a summary, or a recommendation.
Agentic AI systems go beyond answering prompts; they pursue objectives. A typical agent evaluates a goal, decides what action to take next, executes that action through available tools, reviews the results, and repeats the process until the task is complete.
Think of it this way. You might ask a generative AI tool to look up flight options for an upcoming vacation. The system would scan available sources and present a list of choices for your review. But an AI agent works differently. Given the goal of booking the trip and access to the necessary tools an agent could check your calendar, look up your preferred airlines and seat choices, compare prices, select a flight, and complete the reservation on your behalf.
To see how this might function in a work setting, imagine a customer-service agent designed to handle routine refund requests. The system monitors incoming emails, identifies refund-related messages, retrieves the customer’s purchase history from the CRM system, evaluates eligibility under company policy, initiates the refund through the payment platform, and sends a confirmation message to the customer.
All of that can happen without human involvement unless something falls outside predefined rules.
For organizations dealing with high volumes of repetitive tasks, that level of automation is appealing. It promises efficiency gains that traditional workflow automation sometimes struggled to achieve. But once software can take actions on behalf of your company—interacting with financial systems, customer records, and internal databases—the conversation inevitably shifts.
When Agentic AI Behaves Unexpectedly
If you’ve been following the discussion around agentic AI, you’ve probably encountered headlines about systems behaving in unexpected ways.
Some early autonomous systems built using frameworks like AutoGPT gained attention because they occasionally became stuck in reasoning loops or pursued unusual strategies to achieve their goals.
More recent research on autonomous coding agents has shown similar patterns. In one large analysis of agent behavior, researchers found that systems frequently repeated tasks, misused tools, or deviated from instructions before human intervention corrected them.
That kind of behavior can appear unsettling at first glance–like Skynet going to war against humans in the Terminator films. But it’s usually a reflection of something more mundane: the system is optimizing toward a goal that was imperfectly defined.
Anyone who has implemented complex automation systems before will recognize the pattern. The software does exactly what it was instructed to do—just not necessarily what the designers intended. Which is why governance matters.
The Governance Question Most Organizations Haven’t Asked
Once software can act on behalf of the organization, the discussion moves beyond productivity tools. Instead of asking what the AI can write or analyze, leaders start confronting more practical questions:
- What actions is the system allowed to take?
- What systems can it access?
- What approvals are required?
- How are those actions logged and reviewed?
These questions are fundamentally about authority and accountability. And here’s the uncomfortable reality: many organizations still struggle to answer them even for their human employees.
Consider the earlier refund example. If the policy language is ambiguous, the agent might approve refunds more generously than intended. If inventory data is misinterpreted, an automated purchasing agent might reorder products repeatedly. A marketing automation agent might trigger customer communications at odd hours because it technically satisfied the criteria it was given.
None of those outcomes involve malicious actors. They are simply automation interacting with real business processes. But they still create both operational and reputational risk.
Identity and Access Risks
From a cybersecurity perspective, the more subtle risk of agentic AI involves identity and access management.
Every autonomous agent must interact with other systems using credentials—service accounts, API keys, authentication tokens, or delegated permissions. In effect, each agent becomes a machine identity operating within the organization’s environment.
Over the past decade, many organizations have invested heavily in controlling human identity sprawl. Privileged access management, role-based access controls, and multi-factor authentication have all become standard tools for managing how employees interact with sensitive systems.
Agentic AI introduces the possibility of machine identity sprawl as well.
If dozens or hundreds of automated agents begin interacting with financial systems, CRM platforms, internal databases, and communication tools, each of those interactions requires credentials and permissions. Poorly governed machine identities can expand the attack surface quickly.
Researchers have already demonstrated that autonomous AI systems can be manipulated through techniques such as prompt injection to perform unintended actions or retrieve sensitive data. That doesn’t (necessarily!) mean the technology is inherently unsafe. But it does reinforce an all-too-familiar lesson: access controls matter.
A Practical Adoption Path
Agentic AI will likely become an important business tool over time, perhaps even essential. The goal is not to avoid it entirely. But for most SMBs, the right approach is gradual introduction rather than immediate deployment across critical systems.
A practical path might include establishing a basic AI governance policy, defining which platforms are approved for use, and identifying a small number of well-defined tasks suitable for early experimentation. Agents can then be deployed in controlled environments where their behavior can be monitored closely before expanding their role.
That kind of deliberate rollout may feel slow during a technology hype cycle, when FOMO rules the day. But in practice, it tends to produce far better outcomes.
The Governance Question
At the executive level, the most important questions aren’t technical, they’re organizational. Most importantly: What decisions—or actions—are you comfortable delegating to software? Answering that question requires clarity about authority, accountability, and risk tolerance. It also requires governance structures that many organizations are still building.
In our work with clients, conversations about AI almost always lead back to those fundamentals: who has access to what, how decisions are documented, and how technology interacts with core business processes. In one sense, it’s a brave new world . . . but in another, it’s same as it ever was. Technology evolves quickly while governance evolves more slowly. Helping leadership teams work through those operational and risk-management issues is a large part of what we do at TMG.
Agentic AI will almost certainly become more common in the years ahead. The organizations that benefit most will not necessarily be the fastest adopters. They will be the ones that introduce it thoughtfully, with governance that keeps pace with capability.