Building a National AI Skills Policy Agent with h2oGPTe
- Agentic AI
- Enterprise h2oGPTe
This lesson demonstrates an Enterprise h2oGPTe agent working through a four stage government policy workflow: researching existing AI policies, assessing public impact, calculating economic and social outcomes, and producing a final policy decision report. The example casts the agent as a policy researcher at the Australian Department of Industry Science and Resources, evaluating a proposed national AI skills framework against initiatives in the UK, Canada, and Singapore. After launching the agent, the lesson reviews its tool use and the resulting policy brief, cost benefit analysis, and jurisdictional report.
Key takeaways
- How a four stage policy workflow, research, public impact, impact assessment, and reporting, maps to distinct agent tools
- What the agentic analysis tab shows while an agent runs, including the code it writes and the sources it searches in real time
- Why setting agent accuracy to basic versus maximum trades off report depth against the ten turn completion limit
- The set of outputs one agent run produced: a policy brief, a five year cost benefit analysis, and a jurisdictional report
- How to request follow up improvements from the agent, such as capturing more sources or expanding the economic impact section
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