Building Custom Agents with the h2oGPTe Agent Builder
- Agentic AI
- Enterprise h2oGPTe
This lesson covers two ways to build a custom agent in Enterprise h2oGPTe. The first is configuring the super agent directly, using a collection to set its knowledge boundary, settings to control tool access and cost, and a system prompt to define its role and guardrails. The second is the agent builder, which turns a natural language description into a research depth choice and a framework, CrewAI, LangGraph, or the OpenAI SDK, then generates full, runnable Python code with tests and validation. It closes by showing how A2A protocol support lets agents in different frameworks communicate as a discoverable service.
Key takeaways
- How configuring the super agent's collection, settings, and system prompt creates a custom agent without building one from scratch
- What research depth controls in the agent builder: trading off speed against deeper architectural exploration before implementation
- How the agent builder chooses a framework, CrewAI, LangGraph, or the OpenAI SDK, automatically if the user does not specify one
- Why the agent builder runs a build, test, refine loop, validating the generated agent with test queries before finalizing it
- How adding A2A protocol support lets agents built in different frameworks discover and communicate with each other
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