Documenting Architecture Decisions
Introduction To Technical Documentation
Welcome to the final lesson of our course! So far, we have built a powerful API, organized specialized AI agents, and ensured our code is secure and fast. However, there is a common saying in software engineering: "Code tells you how; documentation tells you why."
In a production environment, you are rarely working alone. Even if you are, you might forget why you chose a specific database or a specific way of handling logins six months from now. If the "why" isn't recorded, future developers might change your code and accidentally break something important because they didn't understand your original plan.
In this lesson, we will learn how to capture these big decisions using Architecture Decision Records (ADRs). We will also build specialized AI agents to help draft ADRs and audit documentation automatically. By the end of this lesson, you will have a complete documentation workflow for production-ready APIs.
The Anatomy Of An ADR
An Architecture Decision Record (ADR) is a short document that captures a single significant technical choice. We don't write them for every line of code, but we do write them for big choices that affect the whole project.
Let's look at the structure of an effective ADR:
The Status field is important because it tracks the lifecycle of the decision: Proposed → Accepted → Deprecated → Superseded. ADRs are immutable—we never edit them. If we change our mind, we create a new ADR that supersedes the old one.
AI Agents for Documentation
Just as we used specialized agents for implementation and testing, we can create agents for documentation tasks. There are two main types:
ADR Writer Agent: Drafts architecture decision records by analyzing code and context. It can generate the basic structure and standard content, which humans then refine with project-specific details and nuanced trade-offs.
Documentation Auditor Agent: Automatically checks that documentation matches implementation. It compares OpenAPI specs to actual endpoints, verifies README completeness, and identifies missing ADRs.
This division of labor is efficient: AI handles repetitive structure and obvious facts, while humans add judgment, context, and strategic thinking.
