AI Academy: academies we build with you to qualify your teams in agentic AI

Because agents change the work of the people around a process before they change the process itself. Someone has to choose where an agent is worth it, someone supervises it, someone handles the cases it hands over, and someone builds and runs it. A company that buys agents without qualifying those people ends up with pilots that never reach production, or with agents nobody owns.

Three needs follow:

• Every team that works with agents needs to know what they can and can't do, how to review their work and when to step in.
• The teams that build and run agents need engineering skills that most IT departments don't have yet: agent design, integration, evaluation and observability.
• Everyone who uses AI at work needs a basic level of AI literacy, and Article 4 of the EU AI Act asks every company that deploys AI to take measures to support it.

It means the academy is part of your organisation, run on your processes, and it outlasts any single project. You bring the processes, the people and the goals. We bring the curriculum, the labs, the trainers and mentors, and the certification paths. Together we decide who learns what, through which path, how progress is measured and how the academy keeps going once the first groups have finished.

It isn't a catalogue of off-the-shelf courses, and it isn't a one-day workshop.

• Leadership and managers: where agents create value and where they don't, how to decide on an agent project, and how to lead teams that work with agents.
• Process owners and business teams: working alongside agents every day: reviewing their work, handling the exceptions they escalate and improving the process.
• Operations and IT: running, monitoring and supporting agents and their integrations in production.
• Engineering: designing, building, integrating and evaluating agents, with certification paths on the platforms you use.
• Risk, compliance and legal: human oversight, audit evidence and the obligations of the EU AI Act, GDPR and sector rules.

In 5 steps, each one agreed with you:

1. Map: identify the roles that agents will change, the skills each role needs and the gaps today.
2. Design: define the learning paths by role, the curriculum, the formats, and how progress and certification are recognised.
3. Build: prepare the content, labs and exercises on your own processes, systems and agents.
4. Launch: run the first groups with trainers and mentors, and adjust the paths from the results.
5. Run: support the academy's operation (enrolment, groups, progress tracking, content updates) and add new paths as your use of agents grows.

By doing the work. Exercises and labs use your processes and, where possible, the agents being introduced, in safe environments. Paths combine short modules, practical work, groups with mentors and assessments. Technical paths include recognised certifications: Infosistema's Google CoE certifies its own commercial and technical specialists on the Google AI track, and we bring the same paths to your engineers.

The academy and the agents move together. When Infosistema integrates agents into a process, with Vesster or on Google's agent platform, the academy qualifies the people of that process before the agents go live and again as each agent earns more autonomy. Quick wins in an agent project are built with the people who'll use them, and the academy turns that hands-on learning into a lasting programme.

With measures agreed with you at the design step: participation and completion by path, assessment results, certifications obtained, and how the new skills show up in the processes where agents work. They decide which paths to extend, update or add, and they double as evidence of your AI literacy measures.