How Drasi used GitHub Copilot to find documentation bugs

Using AI agents to validate documentation against live code execution is becoming table stakes for data platform teams. Drasi's approach—pairing GitHub Copilot with automated testing of documented examples—addresses a real pain point: drift between what your docs claim and what actually runs. For teams managing data pipelines, ETL workflows, or analytics infrastructure, this matters because broken examples erode trust faster than any outage. The architectural implication is clear: treat documentation validation as part of your CI/CD pipeline, not a separate manual process. This aligns with the shift toward continuous governance in cloud data platforms, where Azure and similar vendors are embedding compliance and validation deeper into their services. My recommendation: audit your internal documentation now. Run your documented code samples through execution tests. If Copilot can catch these bugs in OSS projects, your team's custom documentation is almost certainly harboring similar issues that confuse new engineers and waste onboarding time.