Agentive AI Is Powerful. It Also Needs Guardrails.
By Grant Andres, Cardinal Point
In our last post, we discussed generative AI and how businesses are increasingly using it to create content, summarize information, and save time. This time, I want to talk about the next step in that conversation: agentive AI.
You are going to hear this term more and more, and for good reason. Agentive AI has the potential to do more than respond to prompts. It can take action, make decisions within a set of rules, and carry out steps toward a goal with less human involvement.
That is exciting. It is also where business leaders must slow down and ask probing questions.
From where I sit, the most important conversation around agentive AI is not just what it can do. It’s about using it responsibly, especially when it comes to protecting your business.
What agentive AI actually means
Most people are familiar with AI tools that generate something after you ask for it. You type in a question, and it gives you a response. You ask for a summary, and it gives you one.
Agentive AI goes a step further.
Instead of simply answering, it can carry out tasks. It can pull information from systems, evaluate options, make choices based on rules, and continue through the process with minimal human input.
For a business, that could look like an AI tool that:
- sorts and routes support tickets
- flags unusual account activity
- gathers information from multiple systems and builds a report
- coordinate input from multiple systems using programmatic API calls and synthesizes workable spreadsheets in a predictable format without any human interaction
- writes software source code using only prompts and deploys working systems to cloud-based infrastructure in Amazon AWS, Microsoft Azure, etc.
- handless parts of an internal workflow automatically
That kind of efficiency gets people’s attention quickly, and I understand why. Business owners are constantly looking for ways to help their teams work smarter.
But when a tool operates within your business, the security conversation changes.
Why security has to come first
The moment AI starts doing more than generating ideas, the stakes get higher.
A system that can take action inside your business should never be treated casually. If it has access to your data, systems, users, or operations, it needs clear boundaries.
That is true whether you are a small business or a larger organization.
I think one of the biggest mistakes companies can make right now is jumping into AI tools because they feel pressure to keep up, without really understanding what those tools can access or what they are allowed to do.
That is where problems start.
The real risk is not the buzzword. It is the access.
I do not think businesses should be afraid of agentive AI. I do think they should be cautious about how much trust they hand over too quickly. Start with a small, surgical approach.
The biggest security risks usually come down to a few simple issues:
- Too much access: If an AI system can reach across email, files, line-of-business apps, financial tools, and customer data, one bad action can create a serious problem.
- Bad information in, bad decisions out: AI is only as reliable as the information and instructions it receives. If the data is wrong, incomplete, or manipulated, the actions it takes can be wrong too. Or, even worse, catastrophic.
- Sensitive data exposure: If an AI tool is connected to systems that store confidential information, businesses need to ensure that data is properly protected.
- Automation without oversight: Just because something can be automated does not mean it should be left alone. Businesses get into trouble when they assume automation removes the need for supervision.
What I tell clients to focus on
When I talk with clients about AI, I am not telling them to avoid it. I am telling them to put the right guardrails in place before they let it touch anything important.
Here are the security measures I believe matter most.
- Start with limited permissions
An AI tool should only have access to the systems and information it truly needs to do its job.
If it only needs to review support requests, it should not also be able to access finance records or make administrative changes in other systems.
This is one of the simplest and most effective ways to reduce risk.
- Keep humans involved in high-risk decisions
There are some actions that should always require human review.
Things like changing security settings, approving payments, accessing highly sensitive records, or deleting large amounts of data should not be left entirely to automation.
AI can assist. It does not need final authority in every situation. Also, before you even start the conversation, make sure your backup and business continuity plan is in place and has been tested to work correctly. A seemingly small issue can loom large in a hurry if something goes sideways and what you thought was your safety net isn’t there when push comes to shove.
- Make sure everything is traceable
If an AI system is doing work inside your business, you need to know what it did, when it did it, and why.
That means logging, monitoring, and maintaining a clear record of actions. If something goes wrong, you need to be able to trace it quickly and respond. For all our AI implementations, we create a distinct user with scoped permissions and treat the agent like an employee. Some of them have their own phone and/or eSIM. The additional licensing costs and setup are a small price to pay to afford you the same level of control you would have with an employee. Terminating access should be as easy as terminating an employee. Think to yourself, how would I pull the plug if I had to? The 80/20 rule applies. Pulling the plug should equate to a 20% work effort that minimally yields terminating access to 80% of the AI agent’s role.
- Test before you trust
I would not recommend turning an AI agent loose in a live business environment without first testing it in a controlled setting.
Businesses need time to understand how a system behaves, where it performs well, where it struggles, and what kind of oversight it needs. That is especially true when integrations and automations are involved.
- Review the vendor, not just the features
Many companies will adopt agentive AI through third-party tools rather than build it internally.
That means you have to look beyond the sales pitch. Ask hard questions.
- What data does this tool access?
- Where is that data stored?
- What permissions does it require?
- What controls exist around risky actions?
- What audit trail is available?
- How is customer information protected?
- Has the vendor had any reported incidents?
Those are basic business questions now. Not advanced technical ones.
- Set policy before adoption scales
A tool starts in one department, people find it useful, and suddenly it spreads faster than leadership can govern.
That is why clear policies matter early. Your team should know which AI tools are approved, how they can be used, which data should never be entered, and when human review is required.
That kind of clarity protects the business and gives employees confidence.
My advice to business leaders right now
My advice is simple: be curious, but do not be careless.
There is real value in agentive AI. It can improve workflows, reduce manual effort, and help teams move faster. I believe that. But I also believe the businesses that benefit most will be the ones that adopt it with discipline.
You do not need to be first. You need to be smart.
That means asking the right questions, setting limits, and ensuring your security posture keeps pace with the technology.
Why it matters at Cardinal Point
At Cardinal Point, we work with businesses that depend on stable systems, protected data, and practical technology decisions. That is why this topic matters so much to me personally.
I do not want clients adopting AI because it is the trend of the moment. I want them to adopt the right solutions, in the right way, with the right protections in place.
Agentive AI can absolutely be part of a strong business strategy. But like any powerful tool, it needs structure, oversight, and good judgment.