1 series · 9 articles
Multi-agent systems, cost engineering, eval pipelines, scaling strategies — the architectural decisions that make or break an AI product.
Topology, orchestration, memory, eval, cost, latency and reliability — composed into one blueprint for an AI system that survives real users.
Models return malformed output, providers go down, and outputs drift. A reliable AI system expects all three and keeps working anyway.
Inference is slow and bursty. Streaming, parallelism, and the async boundary are what keep an AI product feeling fast under real load.
An AI feature that delights at 100 users can bankrupt you at 100,000. Cost is an architectural constraint, designed in — not discovered on the invoice.
In AI systems, evaluation is not QA you do at the end — it's infrastructure you build first. Without it, every change is a prayer.
The context window is your most expensive, most contested resource. What you put in it — and what you remember between calls — is an architectural decision.