Production architecture — Where AI engineering becomes engineering
The gap between a working prototype and a system real users depend on is not model quality. It is latency, failure isolation, cost, caching and observability — ordinary distributed-systems work, applied to a component that is slow, expensive and occasionally wrong.
Track 5 of the AI engineering roadmap by K Laxman — about 6 hours across 1 stage.
A production case study worth reading twice
You can reason about an AI system the way you'd reason about any distributed system.
streaming & stateful inference · full-duplex voice · asynchronous delegation · WebRTC · latency budgets · session affinity · prompt caching · failure isolation · observability
- How we built a realtime system for responsive voice AI in six months — OpenAI (article, free, ~1h, rated 10/10). The single best architecture read on this page. The decision that matters: they isolated the real-time media path from slower tool and reasoning work, so a slow tool can never stall the audio stream. That one idea — keep the fast path free of the slow path — is what turns a mid-level interview answer into a senior one.
Build this — Instrument something you already built. Take the Track 3 agent. Add a latency budget per turn, a cost counter, provider failover, and a trace you can actually read when it misbehaves at 2am.
You're done when: You can draw your system's fast path and slow path as separate lines, and say what happens when the slow one stalls.
Put to work in: Jarvis — per-turn provider failover and streaming · Dialogflow CX voicebot — production conversational system
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