Role-Play Coach Agent: AI Skills Practice and Evaluation
Build role-play scenarios where learners practise a real conversation with an AI persona, and get a structured evaluation at the end of each session. The Role-Play Coach Agent (also known as Gi Practice) is part of GFoundry Intelligence.
What it does
The Role-Play Coach Agent lets you add a Role Play to a Learn Content: an interactive simulation where learners practise a conversation (feedback, sales, leadership, customer service) with an AI persona. When the conversation ends, the learner gets an evaluation against the criteria defined for the scenario.
Key capabilities
Scenarios built in a conversation: describe the situation to the Role-Play Coach Agent and it drafts the persona, the stages and the evaluation criteria for you.
A real dialogue, not scripted branches: the AI persona reacts to what the learner writes.
Progress through stages: the scenario is organised in stages, and the learner sees which stage they are in.
Structured evaluation at the end: a score per criterion with feedback, an overall score and a pass or fail result.
Use cases
Sales teams: discovery calls, objection handling, negotiation.
Managers and team leaders: feedback conversations, performance discussions, difficult announcements.
Customer service teams: escalations, complaint handling, de-escalation.
HR and L&D: interview practice, onboarding conversations, compliance scenarios.
How do I build and run a role-play?
A role-play lives inside a Learn Content item, as its Role Play block, and who can take it follows the audience of that Learn Content. The step-by-step guides are in the Learn collection:
Designing a Role Play with the AI Designer: describe the scenario and let the agent build the persona, milestones and evaluation criteria.
Testing and publishing a Role Play: try it as a learner, read the evaluation and publish.
The learner experience: practicing a Role Play: what learners see, how a session ends and how it is scored.
Reviewing Role Play sessions, evaluations and feedback: the Overview and Sessions tabs, and the conversation and evaluation of each session.
For what Role Play is and how it feeds the skills engine, see Role Play: what it is and why it matters.
Privacy and data handling
The Role-Play Coach Agent uses external AI providers: OpenAI to build the scenario in the backoffice, and Google Gemini to play the persona and write the evaluation. The provider receives the scenario, the persona and the turns of the conversation, including everything the learner writes. The learner's name and email are not added to the prompt. Conversations and evaluations are stored in GFoundry. The Data Processing Agreement, including the list of sub-processors, is available on request.
What it does not do
It does not replace human coaching. The agent gives consistent, scalable practice; people handle context, nuance and growth conversations.
It does not evaluate personality. The evaluation scores the criteria of the scenario, not the learner as a person.
It can make mistakes. The evaluation is generated by AI: read it as feedback on a practice session, not as a verdict on the learner. Test the role-play before publishing, and again after editing.
Tips for better practice
Make the scenario specific. "A customer asking for a 30% discount on a renewal" beats "a tough customer call".
Pair with a course. Use the Learning Designer Agent to build the underlying course, then add the role-play as the practice step.
Review and iterate. After a few sessions, read the conversations and refine the criteria based on what learners actually do.