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AI agents for companies

Introduce agents and train teams on their own processes

T-NEX introduces AI agents on a concrete use case in the company and trains the teams along the way: how to use, check and improve the agent. Staff learn AI on their own processes rather than in a course.

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AI agents for AI training for teams
  1. 01What teams learn while the agent is introduced
  2. 02Components for introducing the agent to a team
  3. 03Rules in place before a team works with the agent

Aligned with your processes

In daily work

What teams learn while the agent is introduced

The training is part of the introduction, not a separate course. Your specialist team works with the agent on cases from its own operation, from the first question to maintaining the sources. What your teams start with is decided in the project.

  • Using the agent day to day: Staff work with the questions and tasks of their own area. They learn how to give the agent a task, what answers it returns and where its limits lie.
  • Checking answers: Teams compare each answer with the source shown. A citation alone does not prove that a summary is correct, so a look at the original becomes routine.
  • Recognising cases without an answer: A clear response path is configured for questions without sufficient evidence, such as a note that information is insufficient, a clarifying question or handover to the responsible team. The team learns to spot these cases and pass them on properly.
  • Reporting errors and gaps: Questions without a matching answer expose gaps in the knowledge base. The team clarifies who handles such feedback and how a corrected answer is released.
  • Maintaining knowledge and sources: Content owners learn to create, change and categorise answers and sources, and to re-check linked test questions after a work instruction changes.
  • Judging improvements with test questions: A fixed set of questions shows whether a change makes previously good answers worse. The specialist team assesses correctness, source fit and the limits of an answer.
Overview

Components for introducing the agent to a team

The starting point is an AI pilot. The specialist team names a correct outcome in advance, along with the errors that are not acceptable. In the pilot, errors and limits count as well, not only the most convincing examples, and an early design shows how staff review and reuse the agent's output.

Depending on the use case, the AI chatbot is used. It finds maintained answers and shows the stored source location. AI knowledge management keeps sources and responsibilities together: content owners are named for sources and knowledge areas, and a change to a work instruction can trigger a fresh review of linked knowledge pages and test questions.

A successful pilot is not yet a finished production solution. What is additionally needed for operation is named at the end as the next steps.

Overview

Rules in place before a team works with the agent

Before the agent is connected to any system, it is settled which sources it may use and which staff may see what. An answer must not reveal anything that is not intended for the person asking.

The same applies to practice. We define in advance which data may be used in testing and clarify whether the task needs personal data at all. Also settled beforehand are the release of new or corrected answers, the log contents and their retention.

Overview

More providers for this task in Malaysia

The register of AI providers in Malaysia lists further providers for this task, including providers of company training, workshops and programmes for managers. This page describes only what T-NEX itself offers.

All 14 tasks for which T-NEX develops AI agents are in the overview.

The workflow

How we introduce the solution.

  1. 01

    Choose a use case and roles: We begin with a defined area, such as the most frequent questions to one department, and check whether reliable material and responsible content owners exist. It is also settled who uses the agent, who maintains answers and who decides on expansion.

  2. 02

    Set the rules: Released sources, audiences, the release of new or changed answers, log contents and retention are fixed before any data is connected. For testing, too, it is fixed in advance which data may be used.

  3. 03

    Pilot with a measurable goal: Together with the specialist team we define what a correct answer is and which errors are not acceptable. The team works with the agent in the pilot itself, checks answers against the source and reports errors. Measured are quality, source fit, correction effort and the human intervention actually needed.

  4. 04

    Operation and expansion: Only after the pilot do we extend the source collection or the audience. Questions without an answer expose gaps in the knowledge base and have a recipient. Changed answers go through a release step, and the test questions allow later changes to be assessed again.

FAQ

Questions about the solution

Is this a course with a fixed curriculum?

No. The training takes place on the agent your team actually uses, with your questions, documents and processes. How extensive it is and what form it takes is decided in the project. General AI courses are not the subject of this page.

Who in the team is trained?

Three roles are typical: staff who use the agent, content owners who maintain answers and sources, and those who decide on expansion and audience. Who that is in your company is clarified when the use case is chosen.

What data do the teams practise with?

Only data that has been approved for this purpose. Before testing, we define which data may be used and whether the task needs personal data at all. For a first conversation, a description of the process and the types of data involved is enough; actual customer records or confidential documents are not needed.

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