Loading
Topic
Gateways, orchestration, deterministic boundaries, state, failures, fallbacks, and versioning.
Practice items tagged with AI Application Architecture.
Uses traces and saturation evidence to locate retry amplification, queue growth, exhausted workers, and failing fallback paths.
Diagnoses an AI regression by decomposing request stages, configuration changes, token growth, retries, routing, tools, retrieval, caching, and provider behavior.
Compares managed model APIs with operating open-weight models across control, expertise, privacy, scale, latency, cost, and portability.
Chooses delegation patterns based on who owns the user interaction, context, final answer, permissions, and failure handling.
Chooses between a direct provider relationship and a cloud-managed integration using concrete security, operations, capability, and commercial constraints.
Separates the current model input from authoritative workflow state and selectively retained information across runs.