AI Agent Caveats, Best Practices, and Course Recap
- Agentic AI
- Enterprise h2oGPTe
This closing lesson covers the practical caveats to think through before deploying AI agents in an organization: setting up data integration and system access ahead of time, building guardrails for regulated use cases, keeping subject matter experts in the loop for oversight and feedback, and using red teaming to find and fix an agent's vulnerabilities before it goes live. It then recaps the course: the distinction between agentic AI, which takes autonomous action, and generative AI, which focuses on content creation, plus Enterprise h2oGPTe's use in regulated industries like energy and finance.
Key takeaways
- Why setting up data integration and system access, such as CRM connections, needs engineering support before an agent use case succeeds
- How guardrails keep an AI agent on track, especially in regulated industries where a poor interaction risks real regulatory issues
- What role subject matter experts play in AI agent development: ensuring regulatory alignment and giving feedback that guides the build
- How red teaming an AI agent surfaces its vulnerabilities so the next version can be built stronger
- The distinction the course draws between agentic AI, which takes autonomous action, and generative AI, which focuses on content creation
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