AI Architecture
In simple words
System-level design: model routing, boundaries, failure modes, governance. The summit node.
The fuller explanation
Architecture begins where a single model call stops being the interesting question. The architect's decisions are about boundaries: what belongs to the model, what belongs to deterministic code, and what belongs to a human.
The recurring choices are consistent across products. Which capability goes to which model, and when to route down to a cheaper one. Where retrieval sits relative to generation. How much autonomy an agent gets before a person approves. What happens when the provider is degraded, rate-limited, or deprecates the model you built on. How data flows, who is permitted to see what, and what you can prove after the fact.
The failure mode to design against is not the model being wrong - it will be wrong, routinely. It is a wrong answer travelling through the system unchallenged into something irreversible. Good AI architecture is mostly the deliberate placement of checkpoints where confidence is insufficient for consequence.
Learn these first
Real prerequisites, taken from the map rather than guessed.
- Observability & EvalsTracing, logging and regression suites. Knowing you broke it before a user tells you.
- Cost & LatencyToken economics, model routing and caching. The constraint that decides architecture.
- Data StrategyProvenance, licensing, freshness and permissions. The unglamorous moat.
- Guardrails & SafetyInput validation, output filtering, prompt injection, and where to put the boundary.
Sources
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