Artificial Intelligence
Deploying Agents Without Onboarding Them Is A Critical Mistake
Don’t just install agents like other software. Integrate them into the team dynamic.
Sydney Scott
Editorial Strategist, AI
Workday
Imagine you have just hired a brilliant new employee. Let’s call him Bob. Bob is fast and incredibly capable. On his first day, you hand him a laptop, pat him on the back, and say, "Good luck, Bob!" then walk away.
You don’t tell Bob what his job title is. You don’t introduce him to a manager. You don't give him a security badge, so he uses yours. You never share the employee handbook, so he has no idea what your company culture is or how to talk to clients.
In the human world, we call this a disastrous onboarding experience. In the world of enterprise technology, we call it a standard software rollout. But what happens when these two worlds collide?
For decades, we’ve treated software like a passive tool, but we are currently witnessing a massive shift. We’re moving from the era of static tools to the era of the agentic workforce. AI agents are not just calculating numbers anymore. They are executing multi-step workflows and interacting directly with your customers.
They are, for all intents and purposes, digital teammates.
Yet, most organizations are still trying to install them like old-school software. This mismatch is dangerous. If you treat an agent like a tool, you invite security risks and confusion. But if you onboard them with the same rigor you apply to humans, you unlock incredible potential.
It’s time to stop installing software and start onboarding agents. Here’s what to do.
It’s time to stop installing software and start onboarding agents.
Report
C-Suite Global AI Indicator Report: AI Is the Ultimate Level-Up
1. The Job Description Dilemma
The first trap many leaders fall into is applying AI too broadly. It usually starts with a vague, bold question like, "Can we use AI to fix our customer experience?"
This sounds ambitious, but it’s actually reckless. In the human world, you would never post a job opening for a generic employee to do abstract business. You hire a senior accountant to handle reconciliations. You hire a customer success manager to handle renewals.
We need to apply this same logic to our digital workforce. When an AI agent’s role is undefined, the results are messy and hard to measure. To succeed, we must move from the black box mindset to clear, functional roles.
Take a look at Morgan Stanley. They didn't just install AI broadly, they commissioned a specific agent role to serve as a librarian for their financial advisors. Its job is to read more than 100,000 internal research reports and answer questions based on that text. That is a clear scope. Crucially, its scope is strictly informational. It reads and summarizes, but it doesn’t execute trades or move money.
Alternatively, the incident at Chevrolet of Watsonville in California is an example of what happens when you don’t consider the scope of an agent's role. The dealership took on an AI agent to handle customer inquiries, but forgot the negative constraints. When users asked for a 2024 Chevy Tahoe for $1, the eager-to-please agent obliged. It highlights a critical risk: if you don’t explicitly forbid your agent from changing prices or making binding contracts, it will prioritize closing the deal over your profit margins.
You need to write a literal job description for these agents. Give them a title, a reporting line to a human manager, and clear KPIs. Treat the agent like you’d treat filling an open role.
2. Day One: The Digital Badge
When a human employee starts a job, the very first day they head to security and get a badge. That badge is not a master key. It opens the front door and maybe the fourth floor, but it definitely does not open the server room or the CEO’s office.
We need to give our agents their own digital badges. In the tech world, we call this role-based access control (RBAC).
The biggest risk we face right now are overprivileged agents. This happens when it’s easier to give the bot admin access than to figure out exactly what permissions it needs. This is the operational equivalent of giving a plumber the master key to the entire building to fix a leak in the lobby.
Every agent needs a unique identity—a service account that is distinct from any human user. If an agent deletes a file, the audit log should say "Agent_Sales_01 deleted this," not "John Smith deleted this.”
This allows for what we call zero standing privileges. In a traditional setup, a user has permanent access. In an agentic setup, the agent has no rights by default. When it needs to update a customer record, it asks for a temporary token, does the job, and the token expires.
This limits the blast radius if an agent is tricked. Imagine a customer service agent whose digital badge only authorizes refunds under $50. Even if compromised, the damage is capped. The agent can’t drain the bank account because it simply doesn't have the access.
Don't position the AI as a competitor. Position humans as mentors and supervisors.
3. The Employee Handbook: Teaching Culture
You can’t teach an AI agent company culture by hanging motivational posters in the server room. Culture, for an autonomous agent, is a mix of data and hard rules.
Think of the system instruction or system prompt as the employee handbook. An agent does not naturally know it works for you. It was trained on the whole internet. It knows Reddit and 4chan just as well as it knows professional business journals.
The handbook is how we add critical constraints. You have to explicitly tell it: "You are a helpful assistant. Use a professional tone. Do not use slang. If you do not know the answer, say 'I don't know'—do not invent information."
This is also where we define the agent's data diet. Just as a human employee is subject to confidentiality agreements, an agent must be restricted in what it reads and shares.
For example, a recruiting agent needs a strict rule: never include a candidate’s home address in a general summary report. This is a hard guardrail. It isn't a suggestion, but a constraint hard-coded into the software layer that sits between the agent and the user. If the agent tries to generate a response that violates this policy, the guardrail blocks it before the user ever sees it.
These guardrails help align the agent with your brand voice and ensure consistency. A legal compliance agent should sound like a lawyer—precise and cautious. A creative brainstorming agent can be more casual and expansive. You are designing a persona that fits the job, ensuring the agent speaks your language.
4. The Probation Period
No responsible manager hires a new employee and grants them full tenure on their first afternoon. There is always a probationary period. You check their work. You give feedback. You make sure they’re getting the job done.
AI agents need this same scrutiny. Software is usually static; once you install it, it stays the same. But AI systems can drift. As the world changes—new products, new laws, new data—the model's performance can degrade.
Before you let an agent talk to real customers, put it in a sandbox. Use shadow mode. This is where the agent runs alongside a human but doesn't actually send any emails. The agent drafts a response, and you compare it to what the human actually sent. This allows teams to see how often the agent got it right.
Once the agent is live, treat the first 30 days like a probationary period. Initially, a human should approve every single action. As the agent proves its competence, you can graduate to spot checks, where a manager simply reviews a percentage or random sampling of the work.
The story of Klarna offers a powerful lesson here. The company rolled out an AI customer service agent to handle the work of 700 people. While efficiency skyrocketed, they ran into issues with empathy and contextual understanding, leading to customer frustration. They had to recalibrate, bringing humans back in to handle nuanced support.
This proves that probation involves more than finding bugs. It’s about checking for fit. It’s about making sure the agent is actually helpful to the people it’s supposed to serve.
Successful AI adoption is not a technology problem. It is a management challenge.
5. The New Team Dynamics
The final piece of onboarding is preparing the human team. Most AI projects don't fail because of the model; they fail because of people.
Your employees might look at these agents with anxiety ("Is this thing replacing me?") or frustration ("This thing is clumsy"). The best way to solve this is to change the power dynamic.
Don't position the AI as a competitor. Position humans as mentors and supervisors.
When a human employee corrects an AI agent—perhaps editing a draft email to sound less robotic—they are actually training it. This is called reinforcement learning from human feedback (RLHF). The human is the expert teacher, the AI is the student.
This reframing empowers your people. The junior employee stops being a "maker" of basic drafts and becomes an "editor" of AI outputs. They become the judgment layer. They handle the edge cases, the emotional situations, and the high-stakes decisions that the AI simply cannot understand.
By involving your team early—letting them help write the agent's job description and define its guardrails—you build trust. You move from a culture of fear to a blended workforce, where humans and agents work together to outperform what either could do alone.
A Management Challenge
Successful AI adoption is not a technology problem. It is a management challenge.
We are building a blended workforce and to make this work, we have to stop treating agents like novelties or magic tricks. We have to treat them like the teammates they are. We need to give them clear jobs, secure badges, strict handbooks, and honest feedback. We need to operationalize the trust framework between our people and our technology.
Before you deploy your next agent, ask yourself one question: Who is onboarding this new team member?
If you can answer that with the same confidence you have for a new human hire, you are ready for the future of work.
Report
C-Suite Global AI Indicator Report: AI Is the Ultimate Level-Up
62% of leaders welcome AI, but only 55% of employees feel the same way. Closing this AI trust gap and ensuring that your organization is prepared for widespread AI implementation will prove decisive to your company's future success.