Eliminating latency in AI workflows: How to avoid the durability tradeoff

The text explores the latency challenges in durable execution systems, particularly focusing on how each step in a workflow can introduce delays due to factors like synchronous state persistence, HTTP-based dispatch, and constraint checking. These issues become critical in modern use cases requiring both speed and reliability, such as AI agents and real-time data pipelines. To mitigate latency, it suggests optimizations like decoupling state persistence from execution, using persistent connections over HTTP, and separating constraint management from the queue. The document emphasizes the importance of checkpointing and a push-based dispatch model to significantly reduce orchestration overhead, offering insights into how Inngest implements these solutions. It advocates for evaluating durable execution platforms based on inter-step latency, constraint handling, and dispatch methods, highlighting the need for these optimizations in user-facing applications.

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