Implementing ADRs in Software Engineering Teams

The problem with keeping code and documentation separate. It tends to diverge over time. So most software documentation should be replaced with highly readable code and tests. However, no matter how readable the code is, it doesn’t give us the context behind why the code exists in the form that it does. To address this software engineering teams should be keeping Architecture Decision Records (ADRs). These are lightweight documents that capture important architectural decisions along with the when, how and why of the decision. ADRs are particularly useful for agile projects where not all the decisions will be made at once and long lifetime projects where a new person joining the team, or someone who hasn’t worked on part of the project recently, will need to understand the rationale and consequences of a decision that has been made in the past. An ADR should record: 1. When the decision was made. 2. The status of the ADR (proposed, accepted, rejected, etc.). 3. The current context and business priorities that relate to the decision. 4. The decision and what will be done because of the decision. 5. The consequences of making the architectural decision. ADRs are best stored in the revision control system alongside the code that they relate to, keeping the context with the code.

In my different leadership roles, I’ve always struggled to get developers to write and maintain relevant documentation. It’s been their bête noire. And when they do write it, it’s usually under managerial pressure, resulting in long SharePoint or Confluence pages that slowly rot, disconnected from the actual architecture and code. In my last mission, I decided to face the elephant in the room and change the approach. Surprisingly, it worked. (Partly thanks to LLMs 😊) Here’s what we did: We moved to collaborative documentation (Microsoft Loop — but it could be Notion, Confluence, whatever fits your team). I encouraged devs to use ChatGPT — not to replace them, but to help them structure, improve, and enrich their writing. We made architecture and design discussions async and doc-first. I introduced a simple rule: 👉 “If it’s not on Loop, it doesn’t exist.” Within a few months, we went from static, outdated SharePoint pages... ...to living docs and Architecture Decision Records (ADRs) — that became a shared knowledge base for the team and the broader company. Of course, AI tools won’t write your docs for you. But they can help your team build a real documentation culture. And you — what’s your experience with documentation? #softwareengineering #engineeringmanagement #documentation #softwaredocumentation #architecture #adr #softwarearchitecture

✍️Architecture Decision Record (ADR): The most underrated skill of a great Solution Architect Most architecture discussions focus on what technology we chose. Very few capture why we chose it. That “why” is exactly where Architecture Decision Records (ADRs) come in. An ADR is a short, structured document that records: • The context of a decision • The decision made • Alternatives considered • Consequences & trade-offs In simple terms: ADR = documented architectural thinking Why ADRs matter more than ever Without ADRs: ❌ Teams repeat the same debates ❌ New architects struggle to understand legacy systems ❌ Decisions become opinion-driven ❌ Knowledge walks out when people leave With ADRs: ✅ Clear decision traceability ✅ Faster onboarding ✅ Stronger architecture governance ✅ Easier audits (critical in Healthcare & BFSI) When should you create an ADR? Create an ADR when the decision is: ✔ High impact ✔ Hard to reverse ✔ Has multiple valid options Examples: • REST vs GraphQL vs FHIR APIs • AWS vs Azure vs Hybrid • RDBMS vs NoSQL vs Data Lake • OAuth2 vs mTLS security • Build vs Buy decisions What a good ADR looks like Not a PPT. Not a 20-page document. Just 1–2 pages answering: • Why was this needed? • What did we choose? • What did we reject (and why)? • What are the consequences? Stored in Git. Versioned. Transparent. Architect’s mindset shift 🚫 “This is the best design.” ✅ “This was the best decision given the constraints.” That’s professional architecture. If you’re aiming to grow as a Solution Architect / Enterprise Architect / Healthcare Architect, start writing ADRs. It’s one of the fastest ways to: • Improve decision quality • Build credibility • Think like a senior architect 💡 Good architecture isn’t just about systems — it’s about decisions. #SolutionArchitecture #ADR #SoftwareArchitecture #HealthcareIT #CloudArchitecture #APIs #TechLeadership #ArchitectureGovernance

AI fails in large codebases? Only if you use it wrong. I hear this argument a lot: “AI coding agents only work for greenfield apps or generic problems. Once your system is big and proprietary, they’re useless.” I strongly disagree. But not for the reason most people expect. The problem isn’t proprietary code. The problem is unstructured context. Right now, most teams use AI like this: “Here’s the repo, impllement X. Figure it out.” That fails every single time. Here’s what actually works for me while working on patent-level proprietary algorithms. 1️⃣ Start with planning, not prompting I never ask an agent to “build the system.” I break requirements down using C4-style thinking until I get tasks that fit into one focused session and all architecture decisions made upfront. AI performs best when decisions already exist. 2️⃣ Use ADRs but optimize how the agent reads them I store Architecture Decision Records in a dedicated folder. The trick: A README inside that folder that lists every ADR, links to it and gives a one-line summary of the decision. When I prompt the agent, I point it to that file. Result: it doesn’t load everything, it pulls only the ADRs that matter for the task. That’s deliberate context, not brute force. 3️⃣ Think in diagrams, not files During planning, I always create diagrams. After implementation I update the diagrams to keep them in sync with the code. Why this matters: diagrams let the agent reason about the system without scanning the repo. This drastically reduces context size and it guides the agent exactly where to look. This is context compression, not documentation for its own sake. 4️⃣ Point to exemplar code on purpose I explicitly link to files that show: CQRS done right, validation patterns, transaction handling and Unit of Work usage. I don’t want the agent to infer patterns. I want it to follow them. Clarity beats autonomy. The real takeaway 👇 AI agents don’t fail because your codebase is large or proprietary. They fail because: ❌architecture decisions live only in people’s heads ❌documentation isn’t structured for retrieval ❌context is implicit instead of explicit Fix that and AI becomes more effective as systems grow.

Most engineering teams have no idea why half their architecture exists Decisions get made, code ships, and months later nobody remembers why. That’s why you need ADRs (Architecture Decision Records) Not 50-page design docs. Just short notes that capture the reasoning behind decisions. Here’s how to make them stick: 1 - Keep it short. One paragraph is often enough 2 - Write them during code reviews. If reviewers ask lots of “why” questions, that’s your signal to write an ADR. 3 - Use a simple template. Context, Decision, Alternatives, Consequences. 4 - Store them in the repo, right next to the code. This way your AI agents (Claude, Cursor, etc.) can reference them for context…..we’ve seen some pretty crazy results with this one Done right, ADRs become your team’s decision journal They cut confusion, prevent rehashing, and make onboarding faster How are others integrating ADRs into their teams?