Article: Runtime-Agnostic AI Workflows: A Pattern for Production Durability and Fast Eval Iteration
The durability-vs-speed tradeoff killing AI workflows has a pattern. Here's how to build both.
Why it matters
A technical pattern for AI workflow systems that solves a real production problem: persisting for reliability without sacrificing the fast eval loops engineers need. Practitioners building AI apps will recognize this tension immediately.
The key facts
10 to knowArticle addresses production durability vs. iteration speed tradeoff in AI workflows
Pattern enables persistence and distribution for crash/deploy/restart survival
Decouples reliability machinery from fast eval iteration
Published on InfoQ (practitioner-focused engineering platform)
Author: Mateus Moury
Runtime-agnostic approach suggested in title
Runtime-agnostic pattern decouples persistence/distribution from eval speed
Addresses production durability (crash/deploy/restart resilience) vs. development iteration velocity
Published on InfoQ (vendor-neutral architecture/patterns platform)
Solves a known pain point in agentic AI deployment workflows
Go to the source
InfoQ AI/MLinfoq.com
Publisher excerpt: AI workflows have two needs that trade off directly. Running reliably in production requires persisting and distributing every step so it survives crashes, deploys, and restarts. But that same machinery is what makes runs too heavy for the fast, throwaway loop you need to check an LLM's output…