Use Cases
Explore real-world examples of how humans and AI agents work together across learning, work, business, research and wellbeing.
ProblemHuman RoleAgent WorkflowDataSafeguardsOutcomes
Each use case shows the problem being addressed, the people involved, how the agent operates, what information it needs, where human oversight remains essential, and what outcomes can be measured.
Showing 6 use cases in all categories.
Plans study, retrieves approved content, generates practice and helps the student reflect on progress.
Supports lesson design, differentiation and feedback while the educator controls pedagogy and assessment.
Finds trusted internal information, drafts work and routes exceptions to accountable people.
Structures discovery across customers, operations, finance and growth before recommending interventions.
Coordinates market, customer, technology and regulatory research with source tracking.
Connects self-reported goals and consented data to general guidance and qualified professionals.
Agentic systems connect goals, context, reasoning, tools and human decisions into accountable workflows.
A person brings a goal, constraint or decision that needs support.
Approved information, permissions and situation are assembled around that need.
The agent plans, compares options and structures a response from that context.
Defined tools retrieve, draft, coordinate or execute the next step.
People review, approve or escalate before anything consequential happens.
Progress is tracked against the original need, not just activity.
Agentic systems can retrieve information, reason across context and execute defined actions. People remain responsible for decisions, approvals and exceptions.
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People approve consequential actions.
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Agents operate from defined and inspectable information.
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Exceptions move to accountable people instead of being silently handled.
Explore the patterns, workflows and safeguards behind practical AI systems.