AI workflows have two needs that trade off directly:
Production systems need durable execution to survive crashes, deploys, and restarts. But the same mechanisms slow down the fast feedback loops developers need when iterating on prompts and evaluating LLM outputs.
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.
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