AI-driven workflows help the Umbraco delivery become high-quality and help developers deliver faster with complete governance. This blog post talks about 10 practical workflows in Umbraco. Any team can automate and how partnering with Techxot can help make those workflows really for production.

AI in Umbraco and developer productivity

The Umbraco teams need to make a lot of effort on repetitive tasks like writing controllers, writing tests, maintain documents, regression fixing, or Umbraco cloud deployment. This is where AI in Umbraco stands out; AI turns these repetitive workflows into programmable tasks with the help of prompts and context. These outputs are aligned with your enterprise coding standards and architectures.

Many teams see this as faster releases, less bugs, and better code visibility and code health. Automating workflows means developers saving more time and spending more time finding solutions to CMS and business challenges.

For further readings see Official Umbraco documentation and Microsoft’sASP.NET core dependency documents for wider reference in this guide.

Workflow 1: Generating Boilerplate Code in Umbraco

AI in Umbraco helps developers by generating boilerplate codes which turn the architectural convention and example controller into reusable scaffold. This workflow can be automated by feeding the AI model your base controller, sample document and naming rule, this model matches your enterprise coding samples. This automation also helps cut the setup time by over 60% without sacrificing quality.

Example prompt and output (C#):

// "Generate an Umbraco API controller for Article content, // following this BaseApiController and using dependency injection // for IArticleService." [ApiController] [Route("umbraco/api/[controller]")] public class ArticleController : BaseApiController { private readonly IArticleService _articleService; public ArticleController(IArticleService articleService) { _articleService = articleService; } [HttpGet("{id}")] public async Task<IActionResult> Get(int id) { var article = await _articleService.GetByIdAsync(id); return article == null ? NotFound() : Ok(article); } }

Workflow 2: Writing Unit Tests using AI

AI automated workflows for Unit tests help development teams write unit tests by inferring cases from existing controllers and services. This helps developers start working from AI-generated tests, which are reviewed and refined by teams. This workflow automation accelerates coverage growth and decreases regressions.

When dealing with Umbraco migrations from Web Forums to modern Umbraco, the AI generated tests help achieve more than 50% coverage on APIs in weeks and not months.

Example test skeleton (xUnit):

public class ArticleControllerTests { [Fact] public async Task Get_Returns_NotFound_When_Article_Missing() { // arrange var service = Substitute.For<IArticleService>(); service.GetByIdAsync(Arg.Any<int>()).Returns((Article)null); var controller = new ArticleController(service); // act var result = await controller.Get(42); // assert Assert.IsType<NotFoundResult>(result); } }

Workflow 3: Faster Error Debugging with AI

When development teams use AI-automated debugging, it frees them from manually scanning Umbraco logs and stacks. Developers paste the code errors into the AI assistants and generate hypothesis, step by step fixes and identify root causes of errors. This workflow automation can be efficient for complex dependency injection issues or content cache inconsistencies.

Example prompt:

“Explain this error and list 3 likely fixes aligned with Umbraco best practices”

This helps decrease time required to resolve errors in deployment environments where the logs are noisy and multi-tenant.

Workflow 4: Refactoring Legacy Umbraco Code

AI analyzes legacy Umbraco codes for controllers, SurfaceControllers, and custom handlers and refactor them to align them with modern Umbraco versions and clean architectures.

Before / after table

Aspect

Legacy implementation

AI‑assisted refactor suggestion

Controller type

SurfaceController for all actions

Split into API + MVC controllers per responsibility

Data access

Inline queries in controller

Move to repository with dependency injection

View logic

Complex Razor with business rules

Move rules into service, keep views lean

AI automation in this workflow will help your enterprise modernize older Umbraco codebase with least regression.

Workflow 5: Creating API Documentation using AI

AI turns the APIs into documents using C# interface and controllers. AI helps developers by generating end point descriptions, parameters, response, and examples.

For example:

“Generate developer friendly markdowns and OpenAPI-style document for Umbraco API.

This prompt creates a markdown that development teams can use to publish on developer portals, and architectural significant documents while maintaining integrity quality.

Workflow 6: Reviewing Pull Request with AI

AI drives the Pull Request and continuously reviews it for anti-patterns, missing tests, and security concerns. This doesn’t completely eliminate human code review rather makes it faster and helps them technical teams to focus more on architecture and business logic.

For example:

“Suggest improvements to performance and readability without changing the behavior”

Workflow 7: Optimizing Umbraco Search Queries using AI

The Umbraco solutions importantly depend on Examine, Elastic search, or custom search providers. When a query that is poorly tuned, it can deteriorate the UX and performance. AI models identify these query codes and configurations and optimize them by suggesting the tuning strategies that help improve relevance and performance.

Example prompt:

Poorly tuned prompt:

public IEnumerable<ISearchResult> SearchArticles(string term) { var searcher = ExamineManager.Instance.GetIndex("ExternalIndex").Searcher; var results = searcher.CreateQuery() .NodeTypeAlias("article") .And() .Field("bodyText", term) .Execute(); return results; }

AI-Optimized Suggestion:

public IEnumerable<ISearchResult> SearchArticles(string term) { var searcher = ExamineManager.Instance.GetIndex("ExternalIndex").Searcher; var results = searcher.CreateQuery() .NodeTypeAlias("article") .And() .GroupedOr( new[] { "title", "summary", "tags", "bodyText" }, term.MultipleCharacterWildcard() ) .Execute(maxResults: 20); // Boost title matches above body matches var ranked = results .OrderByDescending(r => r["title"]?.ToString() .Contains(term, StringComparison.OrdinalIgnoreCase) == true ? 2 : 1) .ThenByDescending(r => r.Score); return ranked; }

Workflow 8: Generating deployment Scripts and Pipelines

AI helps in creating CI/CD pipelines by generating YAML scripts (Azure DevOps, GitHub Actions, GitLab CI) specifically tailored for Umbraco cloud and custom hosting. These pipelines when generated with AI serve as the starting point, after which developers work on security and compliance readiness.

Example GitHub Actions skeleton:

name: Umbraco CI/CD on: push: branches: [ main ] jobs: build-and-deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-dotnet@v4 with: dotnet-version: "8.0.x" - run: dotnet restore - run: dotnet build --configuration Release - run: dotnet test - name: Deploy to Umbraco Cloud run: ./scripts/deploy-to-umbraco-cloud.sh

Workflow 9: Automating Schema and Content Migration

Schema and content migration in Umbraco versions or environments are a repetitive task, which can be seamlessly automated using AI. AI helps create migration scripts, C# codes and SQL snippets when source schema and target schema are clearly described. This helps the migration repetitive tasks to happen predictably and reduce maximum human errors Read more