A demonstration can work perfectly and fail the day after entering the organization. People do not know what source the agent consults, they have doubts about the data they can share or interpret the result as a closed decision. Some stop using it; others use it for tasks that were not planned. Meanwhile, the person who knew the setup continues to resolve all the incidents.
The implementation does not end when the technical flow works. It ends when people can use it with criteria, review its behavior, identify the limits and maintain the knowledge that feeds it. That is why training is not a commercial complement: it is an operational part of the system.
You don’t have to become a technical specialist.
Each role needs a different level. A user must know what the agent is for, what data they can enter, how to interpret the result and when to ask for help. A process manager needs to understand metrics, exceptions, permissions, and approval criteria. Technical, legal or security teams may need more in-depth information about integrations, registrations, suppliers and risks.
To form is not to explain all the functions of a tool. It is to prepare each person for the decisions he will have to make.
What an agent's onboarding should include
The first part is the purpose. The team must understand what problem it solves, what outcome is expected, and what lies outside of its mission. The second is knowledge: what sources you use, who updates them and what should happen when two sources contradict each other.
The third part covers the operation. It includes how the task is started, which entries are mandatory, where the result appears, which points need approval and how a correction is recorded. The fourth deals with risks: personal data, confidentiality, rights, errors, biases, communication with third parties and dependence on suppliers.
Finally, climbing must be practiced. People need examples of situations in which the agent should stop, a response should not be used, or the case should be moved to a specialized profile. A manual that only explains the ideal course does not prepare the equipment for actual use.
Learn from the same organization
The most useful sessions work with recognizable processes and documents. Instead of practicing with a fictitious company, the team can review a draft, classify an incident, compare a current source to an obsolete one, or decide whether an action needs approval.
This exercise allows to detect different interpretations before they become errors. It also helps to adjust the Knowledge Foundation: if several people do not understand a rule, perhaps the problem is not training, but an insufficient definition.
Sessions can be face-to-face, online or hybrid, brief for a specific agent or organized by level. The format must respond to the risk, the number of people and the degree of change introduced by the system.
Initial training and continuous learning
One launch session does not resolve all future changes. Sources are updated, tools modify functionalities, new cases appear and people develop shortcuts that can improve or degrade the process. It is necessary to establish a channel to register doubts, incidents and proposals.
Periodic reviews can look at rejected outcomes, automatic approvals, unanticipated uses, and policy changes. When a new person is added or agent permissions are extended, the onboarding must be updated.
The training should also explain how to continue working if the system is not available. An organization should not lose process or access to sources for an external tool to change or stop working.
Literacy in AI and responsibility
The European Artificial Intelligence Regulation incorporates literacy in AI. Its article 4 states that suppliers and deployers should take measures, to the best extent possible, so that staff and other persons operating or using AI systems on their behalf are provided with a sufficient level, taking into account the knowledge, experience and context of use. Official text of the European Regulation of AI ↗.
The European Commission notes that literacy does not require the same level for all people, but measures adapted to the knowledge and context. Information from the Commission on skills and literacy in AI ↗.
This framework does not turn any course into a compliance certification. The organization must determine what measures are appropriate according to the systems it uses, the people affected and the applicable regulations. Documentation of trainings, materials, roles and updates may be part of the evidence of governance, but needs legal review where appropriate.
How do we know if the training has worked?
Attendance is not the only indicator. We can see if people correctly identify cases of escalation, if errors decrease, if they use the right sources and if they know how to explain what the agent can do. We can also measure adoption, review time, and the ability to continue the process when an incident appears.
An effective women's autonomy training without withdrawing responsibility. The team doesn't need to constantly depend on who built the agent, but it also doesn't have to interpret the tool as taking decisions.
Technology, knowledge and people
An agent may have a good setup and a full documentary basis, but continue to fail if people do not share criteria about usage. Similarly, a general training on AI does not replace the onboarding of the particular process.
At Karmina AI Studio we integrate training within the project so that technology, knowledge and people advance at the same pace. We can adapt the sessions to teams, managers, management or specialized areas and work in person or online.
The goal is not for the team to blindly trust the agent. It’s knowing when to trust, what to check, and how to act when the system isn’t enough.