ArchiDecode

Level : 3 - Architecture as code Banking Case Study and Key References The following case study is a realistic composite , not a description of one specific institution. The platform is a credit-risk system in a bank that computes decisions such as underwriting responses and umbrella credit limits. It uses microservices, Kubernetes, Git-based delivery, CQRS-style separation between command and query paths, event-driven updates, and a service mesh for observability and selecti

Level : 3 - Architecture as code Summary “ Architecture Control Plane ” is best understood as a proposed control-plane layer for architecture itself: a resource-oriented system that stores architectural intent as machine-readable objects, reconciles that intent against observed runtime and delivery reality, compiles intent into validation and enforcement controls, and exposes findings, exceptions, evidence, and remediation workflows through APIs. This is not a product categor

Level : 2 - Architecture as code The Architecture That Lied The system had passed architecture review. The diagrams were clean. The boundaries were well-defined. The governance board approved it. On paper, everything was correct. Six months later, a production issue forced us to turn on distributed tracing. What we found was not architecture. It was improvisation. A request that was expected to flow through three services was now passing through nine .Some of those dependenc

Level : 1 - Architecture as code From Static Diagrams to Enforceable, Verifiable Systems The Architecture That Lied A system passed architecture review. The diagrams were clean.The boundaries were well-defined.The governance board approved it. Six months later, we turned on distributed tracing. What we discovered was not architecture. It was improvisation. A “decoupled” service had 11 hidden synchronous dependencies A supposedly “read-only” service was writing to production d

The shift from “LLM integration” to an enterprise AI platform A request to “integrate an LLM” is rarely a single integration. As soon as an enterprise runs more than one model (or the same model in multiple regions), the hard parts move from application code into architecture : throughput, token budgets, quotas, availability, policy enforcement, observability, and cost attribution. You can see why in provider documentation: OpenAI measures limits across multiple dimensions (

Why point-to-point integrations and batch medallion pipelines don’t scale in regulated environments Executive Summary Enterprises repeatedly face a simple requirement: System A needs data from System B .But at scale, this becomes a structural problem leading to duplicated transformations, tight coupling, inconsistent business definitions, and governance gaps. Traditional approaches usually fall into two extremes: Point-to-point integration (fast but fragile) Medallion-based