Structural teardowns of how production AI agents, retrieval systems, and APIs are actually architected — organized by engineering discipline, not by technology brand.
RAG grounds a model’s response in retrieved documents instead of memorized training data. Here is the retrieval, chunking, and evaluation architecture that makes it accurate on real documents.
Read article ↗Agents call APIs without memory of prior sessions and reason about intent in natural language — which changes what good API and schema design means compared to building for a human developer.
Read article ↗Mapping the full sequence of steps, decision points, and failure paths before connecting any tools — so the resulting system is observable and debuggable, not a brittle chain of triggers.
Read article ↗A real case study documents constraints, architecture decisions, trade-offs, and a measured outcome — proving what was built and why it worked, rather than describing it with adjectives.
Read article ↗An AI agent is a system that plans, calls tools, and acts across multiple steps toward a goal \u2014 not a chatbot with a longer prompt. Here is the structural definition and where the term gets misused.
Read article ↗Splitting into multiple agents adds routing overhead and debugging complexity. It is worth it past a specific, measurable point \u2014 not by default. Here is how to tell which side of that line a project sits on.
Read article ↗Three different ways to get proprietary knowledge into a language model\u2019s output, with different costs, update speeds, and failure modes. A framework for picking the right one instead of defaulting to RAG.
Read article ↗Hallucination is not a model bug to be prompted away \u2014 it is an architecture problem with architecture solutions: grounding, citation-forcing, confidence gating, and structured output validation.
Read article ↗Agents consume APIs differently than human developers writing client code do. Here is how REST and GraphQL actually compare once the caller is a language model choosing endpoints from a schema.
Read article ↗An API an AI agent can safely retry needs idempotency keys and predictable rate-limit behavior designed in from the start \u2014 not bolted on after a duplicate-charge incident.
Read article ↗These three terms get used interchangeably in vendor marketing and mean structurally different things \u2014 rule-based triggers, screen-scraping bots, and reasoning systems each fit different problems.
Read article ↗Most cost estimates fail because they price features instead of unknowns. A framework for estimating scope, complexity, and risk that produces a number you can actually hold a vendor to.
Read article ↗Technical debt is not \u201cmessy code\u201d \u2014 it is a specific, categorizable set of shortcuts with a measurable interest rate. Here is how to find it, quantify it, and decide what to actually fix first.
Read article ↗Interview system design optimizes for a 45-minute whiteboard story. Production system design optimizes for the 3am page eighteen months later. The two disciplines share vocabulary and little else.
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