Our summary
NO1ennn maps an agent loop into generation, bounded decisions, and exact rules, with Python and JavaScript examples for routing, tool checks, dispatch, completion, and retrieval. Jev receives evidence and currently available options; generative models retain planning and writing, while code controls permissions, spending caps, and side effects.
Start with one frequent decision point and measure cost and time per completed task alongside escalation quality before expanding. A confident completion judgment cannot prove a side effect occurred, and routing economics depend on cache behavior. The guide's prices and project results are attributed claims; check its API examples against current SDK documentation.
Key takeaways
- Separate semantic choices from text generation and exact rules; keep permissions, spending caps, and irreversible actions under code and human control.
- Send evidence and currently available options to each decision, route uncertain cases to review, and verify completed side effects independently of the model.
- Move one frequent fork first, then compare cost and time per completed task alongside escalation quality before moving another.