Article: Runtime-Agnostic AI Workflows: A Pattern for Production Durability and Fast Eval Iteration | SpinGraph: Category creation
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Article: Runtime-Agnostic AI Workflows: A Pattern for Production Durability and Fast Eval Iteration
runtime-agnostic, AI workflows, LLM evaluation, production durability
Proposes a new architectural pattern called 'runtime-agnostic AI workflows' to reconcile production reliability with fast LLM output evaluation. Frames durability (persistence, distribution, crash resilience) and iteration speed (lightweight, throwaway runs) as inherently conflicting goals. No code, benchmark, case study, or real-world deployment is described or cited.
Emphasizes conceptual novelty and problem framing while minimizing absence of implementation, testing, or comparative analysis.
A forward-looking architectural insight that anticipates and solves a core friction point in LLM-powered systems.
Runtime-agnostic AI workflows are a new pattern that resolves the trade-off between production durability and fast LLM evaluation.
No reference to existing solutions addressing this trade-off (e.g., caching layers, lightweight eval sandboxes, hybrid orchestration); No mention of tooling constraints, team size, or infrastructure requirements that shape the trade-off
Combines problem salience (a widely felt pain point) with linguistic novelty ('runtime-agnostic') and solution framing ('pattern') to imply design maturity and community utility, even though no artifact, API, or validation is offered — the claim of resolution outruns all evidence.
AI workflows have two needs that trade off directly: production durability and fast LLM output evaluation.
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.