AI consultant John Berryman explains how to build functional AI agents using a dual-loop architecture and a repeatable "outside-in" development method during the Zero to Agent series.
Key Points
- AI agents are defined by a dual-loop structure: an outer loop for user interaction and an inner loop for tool execution.
- The "outside-in" development method prioritizes building traditional software interfaces and data models before integrating AI components.
- Developers can use stubs to simulate AI responses, ensuring system stability before replacing them with minimal agents using Pydantic.
- Effective agents require a limited toolkit, specifically read, write, edit, and shell access, to perform autonomous research and tasks.
- Programming agents in natural language allows subject matter experts to audit reasoning, identify errors, and update instructions without needing engineering support.