The Compounding Latency Crisis of Multi-Step AI Workflows
The typical path for building an AI application starts out incredibly fast. You write a single prompt, hit an LLM API endpoint, and watch a beautifully formatted response stream back onto the screen in under two seconds. The user experience feels crisp, snappy, and responsive.
Then, you try to make the system smarter.
To handle complex, real-world requests, you start chaining operations together. Your frontline router model takes the input, runs a search query against a vector database, passes those retrieved chunks to a secondary reasoning model, calls an external database API to fetch user history, sends that consolidated data to a summarizer, and finally feeds the output to a compliance guardrail model.
You deploy this multi-step pipeline to staging, run your first end-to-end integration test, and watch your jaw drop as you stare at the console log.
The single-turn execution didn't take two seconds. It took forty-five seconds. Under...
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