Workflow Orchestration for AI
Workflow orchestration sequences AI steps alongside regular application steps — some deterministic, some model-driven.
Prerequisites
Overview
A real business workflow rarely consists only of model calls — it mixes deterministic steps (send an email, update a record) with model-driven ones (classify, summarize, decide). Orchestration sequences both kinds reliably in one workflow.
Where It Fits
Trigger Event
Deterministic Step
e.g. fetch a record.
Model-Driven Step
e.g. classify or summarize.
Downstream Action
Key Points
- Mixed determinism
- A workflow needs to handle both predictable steps and model output that can vary or fail differently each run.
- Retry semantics
- Retrying a deterministic step is usually safe; retrying a model-driven step can produce a different result each time, which needs its own handling.
- Durable execution
- A long-running workflow needs to resume correctly after a crash partway through, without re-running already-completed steps.
Interview Question
Why is retrying a failed step in an AI workflow riskier than retrying a normal application step?
A deterministic step — fetching a record, sending a request — produces the same result if retried. A model-driven step can produce a genuinely different output on retry, so blindly retrying might change a decision already partially acted on downstream. Workflow orchestration for AI needs retry logic aware of that difference, not a uniform retry policy.
Explain It in 30 Seconds
Workflow orchestration for AI sequences deterministic steps alongside model-driven ones in the same workflow, and needs retry and durability logic that accounts for model steps producing different results on retry, unlike a typical deterministic step.
Real-World Stack
Technologies commonly used to implement this in production.