Power Platform Docs via Azure DevOps

Key insights

• Power Platform Documentation Extension automates creation of solution docs inside Azure DevOps so teams get consistent, ready‑to‑publish Markdown output.
  It removes manual steps and fits into existing DevOps workflows.
• Solution metadata captured includes tables, relationships, option sets, security roles, and workflows so you see the full structure of a solution.
  The output is formatted for wikis or repositories for easy sharing and review.
• Pipeline tasks are simple to add: install the extension, run an Export Solution step, then run Generate Solution Documentation to produce the Markdown artifact.
  You can preview results directly in an Azure DevOps wiki or store the files with build artifacts.
• Microsoft Dataverse is required for full metadata export, so the solution must exist in a Dataverse environment with a database.
  The process supports Canvas and Model‑driven apps, AI Builder models, and other Power Platform components.
• CI/CD integration ensures documentation stays current by generating docs automatically during builds and releases, improving governance, traceability, and auditability.
  This approach supports regulated environments and regional compliance where applicable.
• Documentation as build artifact lets teams version, review, and publish docs from the same pipeline used for builds and deployments.
  Start by adding the extension to your Azure DevOps organization, add the documentation steps to your pipeline, and confirm the Markdown output in your wiki or repo.

Tech Tweedie’s recent YouTube video walks viewers through an automated approach to documenting Power Platform solutions inside Azure DevOps. In the video, he demonstrates the Power Platform Documentation Extension which produces solution metadata in Markdown so teams can publish docs to a wiki or repository automatically. The tool extracts details like tables, relationships, option sets, roles, and workflows, packaging them as artifacts in a CI/CD flow. As a result, teams can keep technical documentation aligned with builds without manual copying and pasting.

Overview of the automation

The video begins by explaining why automation matters: documentation often lags behind code, and manual updates create risk. Therefore, Tech Tweedie positions the extension as a step to tie documentation generation into standard build and release pipelines. He emphasizes that the approach works with solutions stored in a Dataverse environment that has a database, and pairs naturally with the Power Platform Build Tools in Azure DevOps. Consequently, documentation becomes a reproducible artifact of the build process rather than an afterthought.

How the extension works in a pipeline

In practical terms, the video shows adding tasks such as Export Solution followed by Generate Solution Documentation into a pipeline. Once configured, these steps export solution metadata and render it as readable Markdown files that can be stored with other build artifacts. Then, teams can preview that output inside an Azure DevOps wiki or commit it to a repository for broader access. Moreover, the process supports a range of Power Platform components, which makes it useful across model-driven apps, canvas apps, and AI models.

Benefits for teams and compliance

Automating documentation brings several clear advantages: it reduces manual effort, improves consistency, and increases traceability across releases. Furthermore, generation on every build helps teams maintain up-to-date documentation during iterative development and supports governance by producing an auditable record. This approach also helps teams working in regulated environments because it makes it easier to show what changed and why. In short, automation strengthens transparency while reducing tedious manual work.

Tradeoffs and technical challenges

However, Tech Tweedie also makes clear that automation introduces tradeoffs. First, pipeline complexity increases as you add export and documentation steps, which can lengthen build time and require additional maintenance. Second, generated documentation can become noisy if teams include every minor object without filtering, so striking a balance between completeness and relevance is essential. Finally, permissions and environment setup for Dataverse and Azure DevOps can be a hurdle, since tasks need adequate access to export solution metadata reliably.

Common pitfalls and mitigation

In practice, the video highlights several pitfalls teams should watch for and how to address them. For example, large solutions may produce voluminous output that is hard to navigate, so Tech Tweedie recommends limiting exports to relevant components or splitting documentation into smaller topics. Furthermore, teams should validate generated Markdown formatting and adjust templates when necessary, because default output might not match internal style guides. Finally, testing the pipeline in a development environment before rolling it into production helps catch permission and performance issues early.

Practical steps and recommendations

For teams ready to adopt this pattern, the video outlines a simple rollout path: install the documentation extension into your Azure DevOps organization, add the export and documentation tasks to an existing pipeline, and configure artifact publishing to your chosen wiki or repo. Then, preview output in the wiki and iterate on templates to match your documentation standards. Additionally, schedule documentation generation on major builds or releases rather than every minor commit if build time or storage is a concern, which balances freshness with cost and efficiency.

Governance and maintenance advice

To keep documentation useful over time, Tech Tweedie suggests pairing automation with governance practices like versioning, review gates, and selective exports. Consequently, teams should treat generated files as artifacts that require the same quality checks as code, including peer review and periodic cleanups. Moreover, documenting the documentation process—how tasks are configured and where artifacts land—helps new team members understand and maintain the system. Ultimately, automation does not remove the need for human oversight, but it does make oversight more focused and predictable.

Conclusion

Tech Tweedie’s walkthrough demonstrates a pragmatic way to bring documentation into a CI/CD workflow using Azure DevOps and Power Platform tooling. While automation delivers consistency and auditability, teams must weigh pipeline complexity, output noise, and access requirements when adopting the approach. By starting small, validating outputs, and applying governance, organizations can enjoy better documentation with manageable overhead. In other words, the method helps teams deliver clearer, more reliable technical records while keeping development processes efficient.

Keywords

Power Platform documentation, Azure DevOps Power Platform, Generate solution documentation, Power Platform docs automation, Power Platform ALM documentation, Dataverse solution documentation, Azure DevOps pipeline documentation, Power Platform CI/CD documentation