Building a Multi-Step AI Pipeline with Automatic Retry Logic

When you chain language model calls into a pipeline, things break in unexpected ways. A step times out, the API returns a 429, or the model outputs malformed JSON that crashes your parser two steps later. Without deliberate retry logic, one bad response kills the whole run — and re-running from scratch wastes time and money. This article walks through building a multi-step LLM pipeline in Python with per-step retry, exponential backoff, and clean error propagation.

What a Multi-Step AI Pipeline Looks Like

A pipeline here means: take raw input → process through N language model calls, each depending on the previous → produce a final structured result. A realistic example:

Extract entities from a user document

Classify each entity by type and risk level