Systems, agents, retrieval, infrastructure

AI Runtime Architecture and Model Routing

Modern AI products rarely depend on a single model call. They operate as runtime systems that classify intent, assemble context, choose models by latency and capability, enforce schemas, and recover gracefully when a provider or tool fails. This module reframes LLM integration as distributed systems design rather than prompt decoration.

Deep dive summary

What this module actually teaches.

Passage 1Modern AI products rarely depend on a single model call. They operate as runtime systems that classify intent, assemble context, choose models by latency and capability, enforce schemas, and recover gracefully when a provider or tool fails. This module reframes LLM integration as distributed systems design rather than prompt decoration.

Passage 2You will study routing layers that separate policy from execution: small classifiers for request triage, frontier models for complex reasoning, local or distilled models for cheap deterministic work, and fallback paths for degraded service. The emphasis is on request budgets, service boundaries, streaming UX, queue backpressure, and adapter contracts that prevent vendor lock-in.

Passage 3By the end, learners can design an AI gateway that supports model portfolios, versioned prompts, structured outputs, provider health checks, and audit trails. The architecture is intentionally compatible with read-only code examples and sequence diagrams rather than live execution.

Learning operating system

Module prerequisites, concepts, outcomes, and artifacts

Prerequisites

  • TypeScript or Python fluency
  • HTTP APIs
  • basic distributed systems concepts

Key concepts

  • model routing
  • capability-based dispatch
  • latency budgets
  • fallback orchestration
  • schema-first outputs
  • provider abstraction
  • request tracing

Target audience

  • Senior frontend, backend, platform, and full-stack engineers who need to own AI features beyond prototype quality.

Outcomes

  • Design a model gateway with routing, retries, and provider isolation
  • Choose when to use small, frontier, local, or specialized models
  • Define prompt, model, and tool contracts that can be versioned safely

Artifacts

  • AI runtime architecture diagram
  • model routing decision matrix
  • read-only TypeScript adapter example

Code example

Real patterns, ready to copy into your editor.

A code reference card that frames this module's concepts in real syntax. Copy any example to study it or adapt it directly in your own editor.

Copy-ready snippets
curriculum/se-runtime-architecture-model-routing.tsts
1type ModuleBrief = {2  track: string;3  difficulty: string;4  keyConcepts: string[];5  reviewGate: 'human-review-required';6};7 8export const moduleBrief: ModuleBrief = {9  track: 'Software Engineers',10  difficulty: 'Advanced',11  keyConcepts: [12    'model routing',13    'capability-based dispatch',14    'latency budgets',15    'fallback orchestration',16    'schema-first outputs',17  ],18  reviewGate: 'human-review-required',19};