AI Systems Architecture — Mastery4 / 9
Context & Memory Architecture
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.
The context window is finite, expensive, and where the model actually "thinks." Treating it as an infinite scratchpad is the most common architectural mistake in AI systems.
Context is a budget
Every token in context costs money and dilutes attention. More context is not more intelligence — past a point it's context rot: the model gets slower and vaguer as noise crowds out signal. Curate ruthlessly: include what this step needs, nothing more.
Two kinds of memory
- Short-term (working) — the current conversation/task. Manage it with summarization: compact older turns into a tight recap when it grows, keeping the gist and dropping the transcript.
- Long-term (persistent) — facts that outlive a session (user preferences, prior decisions, domain knowledge). Store these externally and retrieve the relevant slice into context per request — RAG applied to memory.
Retrieve, don't accumulate
The scalable pattern isn't "remember everything in context" — it's "store everything outside, retrieve the relevant bit." A vector store or structured DB holds the memory; the agent pulls in only what this turn requires.
Memory feeds the system. Next: how you know any of it actually works — evaluation as infrastructure.
Series — AI Systems Architecture — Mastery
- Part 01Architecting AI Products — First PrinciplesAI systems fail differently from normal software: they're non-deterministic, costly per call, and hard to test. The architecture has to account for all three.
- Part 02Single Agent vs. Multi-Agent — Choosing a TopologyMulti-agent is fashionable and usually premature. Here is how to decide honestly — and why most products should start with one well-equipped agent.
- Part 03Orchestration Patterns — Pipelines, Routers, SwarmsOnce you have multiple steps or agents, how they're wired together decides cost, latency and reliability. Four patterns cover almost everything.
- Part 04Context & Memory Architecture — you are hereThe context window is your most expensive, most contested resource. What you put in it — and what you remember between calls — is an architectural decision.
- Part 05Evaluation Pipelines as InfrastructureIn AI systems, evaluation is not QA you do at the end — it's infrastructure you build first. Without it, every change is a prayer.
- Part 06Cost Engineering — Token Budgets That HoldAn 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.
- Part 07Latency & Throughput at ScaleInference is slow and bursty. Streaming, parallelism, and the async boundary are what keep an AI product feeling fast under real load.
- Part 08Reliability — Retries, Fallbacks, GuardrailsModels return malformed output, providers go down, and outputs drift. A reliable AI system expects all three and keeps working anyway.
- Part 09The Reference Architecture in ProductionTopology, orchestration, memory, eval, cost, latency and reliability — composed into one blueprint for an AI system that survives real users.