Session Architecture
How AI pipeline sessions are structured, batched, and sequenced, including model selection per session type, target ordering, and session-to-task granularity. The primary cost driver in high-volume AI pipelines.
How AI pipeline sessions are structured, batched, and sequenced, including model selection per session type, target ordering, and session-to-task granularity. The primary cost driver in high-volume AI pipelines.
Session architecture describes the structural decisions that govern how individual AI sessions are created, scoped, and sequenced in a pipeline: which model handles which task class, how targets are batched, how context is shared across calls, and at what granularity a session begins and ends.
In high-volume research or automation pipelines, session architecture drives cost and quality more than model selection alone. A pipeline with poor session boundaries forces expensive models to handle cheap tasks, or forces cheap models to handle tasks requiring extended reasoning.
The key optimization levers are: target ordering by difficulty (easy-first sweeps reduce mean cost), context caching to amortize shared prefixes across many calls, and model tiering so session complexity determines model assignment rather than blanket configuration.