Part 1 covered the 25 scripts I reach for every week — reliable, production-ready automation building blocks. This part covers the part that takes longer to figure out: how to chain them together when the control flow involves a decision.
Pure script-chaining is easy. Run A, pipe output to B, done. But real automation pipelines hit decision points: should this file be processed or skipped? Is this API response an error, a retry, or a success? Does this content need review or can it proceed?
Historically you handled this with if/elif trees. This works fine when the decision logic is clear and stable. It falls apart when the decision requires judgment — parsing ambiguous output, classifying content that doesn't fit neat categories, handling the long tail of edge cases that if statements don't cover.
The pattern I've landed on: use the scripts in the Cookbook for the deterministic parts of the pipeline (HTTP calls, file I/O, scheduling, retries), and use a small AI prompt for the judgment calls. The two fit together cleanly because neither is trying to do the other's job.
The shape of an AI-in-the-loop pipeline
