Living Documentation

Living Documentation: Utilizing text-to-code for version-controllable documentation integrated into CI/CD pipelines

One of the most transformative aspects of the modern AI-powered C4 workflow is the shift from static, image-based diagrams to living documentation — architecture models that are text-based, version-controlled, automatically rendered, and kept in sync with the codebase through CI/CD pipelines. This approach, often called “diagrams as code”, turns architecture from a one-time artifact into a continuously evolving, trustworthy source of truth.

At the heart of living documentation is the principle that everything is text: the C4 model is expressed in a human- and machine-readable format, generated or refined by AI, stored in Git, and automatically visualized on every change.

Why Text-to-Code Beats Binary Images

Traditional diagrams saved as PNG, SVG, or proprietary .drawio / .vsdx files suffer from the same fatal flaws:

• Impossible to diff meaningfully in pull requests
• No history of who changed what and why
• No automated validation or linting
• Manual updates required after every code change → staleness
• Hard to search, grep, or reuse programmatically

Text-based representations solve all of these:

• Git tracks every line change → full history and blame
• Pull requests show semantic diffs (“renamed container from ‘Backend’ to ‘Order Service’”)
• CI can run checks: validate C4 compliance, detect broken links, enforce naming conventions
• Rendered images are regenerated fresh on every build → always current
• Text is searchable, copy-pasteable, and composable across tools

Core Text Formats Used in Practice (2026)

1. C4-PlantUML (most popular for open-source and Visual Paradigm workflows)
   • Plain-text DSL built on PlantUML syntax
   • Supports all C4 levels + supporting views
   • Example snippet:
     text

     @startuml
     !include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Container.puml
     
     Person(customer, "Customer", "Uses the system")
     System_Boundary(c1, "E-Commerce Platform") {
         Container(mobileApp, "Mobile App", "React Native", "Customer-facing app")
         Container(apiGateway, "API Gateway", "Kong", "Routes requests")
         Container(orderService, "Order Service", "Spring Boot", "Handles orders")
         ContainerDb(postgres, "PostgreSQL", "Orders & Customers")
     }
     Rel(customer, mobileApp, "Uses", "HTTPS")
     Rel(mobileApp, apiGateway, "Calls APIs", "HTTPS/REST")
     Rel(apiGateway, orderService, "Forwards", "HTTPS")
     Rel(orderService, postgres, "Reads/Writes", "JDBC")
     @enduml
   • Rendered via PlantUML server, Kroki, or Visual Paradigm’s built-in engine
2. Structurizr DSL (excellent for workspace-based, multi-view models)
   • More programmatic, great for large systems
   • Integrates natively with Structurizr tools (on-prem or lite)
3. Mermaid (simpler syntax, great for quick Dynamic/Sequence diagrams)
   • Increasingly supported in GitHub, GitLab, Confluence, Notion

Integrating into CI/CD Pipelines

A typical modern pipeline (GitHub Actions, GitLab CI, Azure DevOps, Jenkins) does the following on every push/PR:

1. Validate the .puml / .dsl files
   • Syntax check with PlantUML CLI or custom linter
   • Optional: architectural fitness functions (e.g., “no direct container-to-database arrows without facade”)
2. Render diagrams
   • Use PlantUML server, Kroki, or local CLI to generate PNG/SVG/PDF
   • Store artifacts in pipeline cache or publish to GitHub Pages / GitLab Pages
3. Publish living documentation
   • Upload to Confluence / Notion via API
   • Embed in ADRs (Architecture Decision Records)
   • Update a central architecture portal (e.g., Structurizr on-prem, Backstage, or custom site)
   • Post links or inline images to PR comments / Slack / Teams
4. Alert on drift (advanced)
   • Compare generated model against static code analysis (ArchUnit, jqAssistant)
   • Fail build if significant divergence is detected

AI’s Role in Making It Truly “Living”

The AI Chatbot (Architect) and Blueprint Generator don’t just create diagrams — they maintain them:

• Regenerate PlantUML text after every meaningful conversation change
• Commit updated .puml files directly (with approval) or suggest PRs
• Detect when code changes imply architecture drift (“New Kafka consumer detected in repo — shall I add it as a container?”)
• Keep documentation in sync during refactors, renames, or decompositions

Result: Architecture documentation becomes self-healing and self-updating — as close to zero manual maintenance as possible while remaining human-readable and reviewable.

Bottom-Line Benefits

• Always current — no more “this diagram is from last year” excuses
• Reviewable like code — architecture decisions get the same scrutiny as implementation
• Collaborative — entire team can propose changes via PR
• Searchable & queryable — grep for “payment” across all models
• Reusable — embed snippets in READMEs, ADRs, onboarding docs
• Governed — CI enforces standards, Desktop applies final corporate polish

Living documentation via text-to-code is no longer a nice-to-have — it is the default expectation for teams that treat architecture as a strategic asset rather than an afterthought.

In the hands-on exercises ahead, you’ll create a small but complete living C4 model: prompt the AI to generate C4-PlantUML text, commit it to a sample repo, set up a simple GitHub Action to render and publish diagrams, and see how one architectural conversation becomes a continuously updated, versioned artifact that lives forever in your pipeline.