I have been working with companies across multiple industries that are building AI-enabled secure platforms. Many of these platforms now use AI agents to replicate current work, take on new tasks, or replace any job that is mostly “typey-typey” administrative work.
I will admit it: I have become a full convert to using AI agents for advanced work. It took some time, so if you have not been using the latest state-of-the-art models for your agents, you are missing out on the future that is already here.
Not just simple tasks. I am talking about work that normally requires years of cybersecurity training, professional certifications, and highly paid specialists. The moment that really opened my eyes was working with OpenClaw, an AI agent platform designed to actually perform tasks across apps and workflows. It was not perfect out of the box. Like any powerful new tool, it took time to install, secure, configure, and shape into something I trusted enough to use. But once it was working the way I intended, it did real work.
At first, I used it for tasks I would normally push off until after core business hours: administrative items, documentation, research, follow-ups, and internal support work. These were not always mission-critical tasks, but they still mattered. Over time, I added
more skills, more workflows, and long-term memory, so the agent had context the next time it started a task.
That is when the light bulb went off.
AI agents are not just chatbots. They are not just tools that answer questions. Properly designed, secured, and monitored, they can become digital workers that help businesses move faster, reduce costs, and take pressure off overworked teams.
But there is a catch. You should never blindly trust them.
AI agents need structure, permissions, supervision, testing, and accountability. In many ways, they are like project teams, employees, contractors, or even your kids. They can do amazing things when given the right direction, but they still need boundaries, monitoring, and correction.
Here are the biggest lessons I have learned, and I have built over 300 agents—some of whom do really complicated workflows, use tools, and even write great reports.
First, every AI agent needs a specific workflow. Do not just build an agent and tell it to “help.” Define what it is supposed to do, when it should do it, what steps it should follow, and what success looks like.
Second, define the knowledge, skills, and attributes the agent needs. In cybersecurity, we do this with human roles all the time. The same concept applies to AI agents. What does the agent need to know? What must it be able to do? What judgment or behavior should it demonstrate? You can even use your open requisitions for this—since agents are, in a way, like humans, they will need to be trained in skills, read and execute instructions, and even have behavioral expectations—please don’t accidentally build the terminator.
Third, know what tools the agent needs and what permissions it should have. This is critical. If an agent needs access to a ticketing system, give it only the access required to complete that mission. If it needs command-line or API access, control it carefully. Do not give broad permissions just because it is convenient. This is just like a new employee—what systems will they need access to, and what permissions will they need to do the assigned tasks?
Fourth, train the agent with the right materials. That may include documents, diagrams, policies, user guides, administration manuals, procedures, and examples of past work. An agent without the right knowledge base is like a new employee with no onboarding.
You may also want to create an AI Agent On-boarding Script, which you give to all of them, like the brief you give a new employee.
Fifth, understand what other agents it may need to coordinate with. In more advanced environments, one agent may not complete the full mission alone. A worker agent may need help from a supervisor agent, a compliance agent, a security agent, or a documentation agent.
Sixth, decide what type of agent you are building. A supervisor agent can assign tasks and monitor worker and guardian agents. A worker agent performs specific tasks. A guardian agent watches for triggers and can take action when something happens, such as a security incident, suspicious access change, policy violation, or threshold being exceeded.
Seventh, choose the right “brain.” Not every agent needs access to the most expensive or powerful large language model. I usually give supervisor agents access to the strongest models because they are the agent babysitters. They need to identify issues, correct problems, and make judgment calls when worker or guardian agents fall short. Many worker and guardian agents can use lower-cost models or even small language models for specific tasks.
Eighth, require full audit logs and mission traces. Every action, decision, tool call, output, and created artifact should be tracked. You need to know what the agent did, why it did it, what system it touched, and whether it followed your security and privacy policies.
Ninth, when possible, give agents API or command-line access instead of forcing them to use inefficient workflows that burn unnecessary tokens. Tokens cost money. Poorly designed agents can quietly spend a lot of it.
Tenth, set, monitor, and enforce token usage and mission priority. Not every task deserves unlimited resources. Some missions are high priority and need more access. Others should stop when they hit a limit.
And finally: test, test, test.
Never assume an AI agent is working correctly just because it sounds confident. Test it. Certify it. Grade it. Attack it. Degrade it. Watch what happens when it loses context, runs into conflicting instructions, or tries to reach outside its approved tools and permissions. My agents have lied, said they were using tools they would never initiate, told me they were not using as many tokens as I was monitoring—so, like a human, just because it sounds good and even looks good, does not mean it is true.
That last part matters because AI agents will be attacked just like traditional systems. Prompt injection, data leakage, unauthorized tool use, model manipulation, and agent-to-agent exploitation are real risks. If your business uses agents, you need to know how they behave under pressure, not just when everything is perfect. You probably scan and pen test your systems for security and regulation requirements—agents should be no different. They are attacked or worse—possibly one day become the attacker—don’t act like this does not happen with humans either—that is why we have insider threat training.
At ZeroTrusted.ai, we built capabilities to make this practical. Our platform allows organizations to register agents, test them, trace their missions, audit their activity, verify their behavior, grade their performance, and generate evidence packages showing what the agents did. We also support adversarial testing and more than 45,000 rubric-based tests so organizations can go beyond basic compliance and understand how agents perform under real-world pressure. We also make it as easy as possible, so you can create real-world, secure agents that undergo rigorous continuous monitoring and testing.
That is the future of AI adoption.
AI agents can work 24/7/365. They can take on repetitive tasks, accelerate expert work, reduce administrative burden, and help organizations scale in ways that were not possible before. But they still spend tokens, and tokens are money. They still make mistakes. They still need rules. They still need supervision. You may feel like you need to babysit employees, managers, or yourself—nothing has changed—other than your new employees are AI Agents.
So yes, AI agents may become part of your workforce.
Just remember: even the best digital workers need to be watched.
And when you correct them, they might even give you a little attitude. This will make you feel right at home.
