AI Pipeline Orchestration

Declare models, data sources, and tools, then chain them so formats match and failures surface across multi-step agent and RAG pipelines.

An orchestrator specifies how components work together end to end:

  1. Define components — models (via a model gateway), external data, tools, eval hooks.
  2. Chain — compose the path from query to completion (preprocess → retrieve → prompt → generate → score → return or escalate).

It must enforce step-to-step format compatibility and surface component and data mismatches instead of failing silently. Parallelize independent steps (routing and PII scrub) when latency matters. This is distinct from general workflow orchestrators (Airflow-style) and from the agent's internal ReAct / agent control flow.

A basic LLM workflow keeps that chain as a fixed DAG or graph: tasks may call LLMs, but routing between tasks is traditional software. That finite, known communication pattern makes failures easy to isolate and tasks easy to evaluate one at a time — the default before LLM-driven workflow routing. Follow workflow build process and task I/O schema design when constructing the graph.

Tempting to adopt a framework immediately; building without one first often preserves understanding (own the control flow). Evaluate tools on integration/extensibility, support for branching/parallel/error handling, and whether they hide API calls or add latency — criteria for progressive agent architecture maturity.

Sources

  • AI Engineering (Chip Huyen), ch. 10
  • Prompt Engineering for LLMs (Berryman), ch. 9