Automating cross-repo documentation with GitHub Agentic Workflows

One of the most frustrating gaps in software development is the lag between shipping features and documenting them. The .NET Aspire team recently tackled this problem head-on using GitHub’s agentic workflows—autonomous AI agents that can execute multi-step tasks across repositories without human intervention. Instead of relying on developers to manually write documentation after merging code, they built a workflow that detects changes, generates documentation drafts, and opens pull requests for subject matter experts to review. It’s a practical example of how AI agents can bridge the gap between velocity and documentation accuracy.

Here’s how it works technically: when code gets merged to the main branch, a GitHub workflow triggers and uses Claude (via AWS Bedrock) to analyze the changes and generate documentation. The agent doesn’t just create a static document—it can reason about what was changed, understand the context from commit messages and code diffs, and structure documentation that matches existing patterns. The workflow then automatically commits these changes to the documentation repository and opens a pull request, complete with context about what changed and why. This approach is more sophisticated than simple template-based generation because the agent can adapt to different types of changes: a new configuration option gets documented differently than a breaking API change.

For teams like Aspire, this solves several real problems. Documentation now stays closer to feature releases instead of lagging by sprints or months. SMEs still review everything—the agent isn’t replacing human judgment, it’s replacing the blank-page problem. Developers spend less time on documentation boilerplate and more time on technical accuracy. If you’re managing multiple repositories with interconnected features, imagine the compound benefit: every cross-repo change automatically generates documentation PRs across your docs repository, with the agent smart enough to identify impacts that developers might miss during a fast-paced sprint.

The practical takeaway for your team is that agentic workflows aren’t just about chatbots answering questions. They’re about embedding AI into your CI/CD pipelines to handle specific, well-defined tasks that currently require human context-switching. If you’re struggling with documentation debt or maintaining consistency across multiple repositories, this pattern is worth exploring—especially since GitHub workflows are free to set up and AWS Bedrock makes it straightforward to integrate Claude into your automation without managing separate API keys or infrastructure.