Article: Runtime-Agnostic AI Workflows: A Pattern for Production Durability and Fast Eval Iteration
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 quality. The properties that buy durability are the ones that kill iteration speed. By Mateus Moury
The article discusses a pattern for AI workflows that addresses the challenge of balancing production durability and fast evaluation iteration. AI workflows consist of a sequence of steps, including calls to large language models (LLMs), and need to handle production requirements such as surviving deploys and crashes, retrying idempotently, and scaling horizontally.
Workflow engines have solved these issues for long-running distributed systems, but AI workflows differ because LLM output quality can change with prompt tweaks or model updates, requiring frequent evaluations.
The two requirements conflict: durable runtimes are heavy and persistent, while evaluation loops need to be lightweight and ephemeral, allowing for cheap reruns. Most stacks are built around one type of runtime, leading to a mismatch between durability and evaluation needs. The article describes a pattern from Brex's AI workflow platform, which is written in TypeScript and uses Temporal Cloud for long-running agents and an in-house platform for evaluations.
Durable execution requires persisting step results, while evaluation features benefit from local, in-process, and ephemeral loops.
The mismatch between runtime and evaluation needs arises because mainstream tools couple orchestration to the runtime by design. This coupling makes it difficult to evaluate orchestration logic independently and serves as the foundation for the runtime-agnostic workflow orchestration pattern. The core of this pattern is to write orchestration against an interface that the runtime satisfies, allowing for portable core and adapters for different runtimes.
This approach eliminates the need to reimplement agents in separate evaluation runtimes, reducing the risk of eval-prod skew.
Written by urgent.news from InfoQ's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
