Agentic AI for .Net Applications Development

About this course

COURSE SUMMARYTitle: Build an AI Agent Pipeline, From Requirements to ReleaseTag line: Requirements to release, without the hand-offs.

OVERVIEW

Build a working delivery pipeline run by AI agents. A RAG knowledge base holds the requirement documents. Requirements

Agent turns them into Azure DevOps stories. Development

Agent writes the code and opens a pull request. PRReviewAgent reviews it. ReleaseAgent deploys it. TestAgent runs browser tests from Excel. TriageAgent turns failures into bugs. Orchestrator

Agent runs the whole loop with one command. The target is a real ASP.NET Core MVC app with a SQL Server database.

FUNCTIONALITYRAGSystemIngests PDF, Word, Excel, text and image files, with OCR for scans. Splits text into chunks, embeds them locally and answers questions with citations to the source file and page. Skips unchanged files. Available as a Python library, a REST API and an MCP server. No paid AI cost.

RequirementsAgentReads whole documents from the RAG. Asks Claude for one Feature and 3 to 8 user stories per document, with acceptance criteria. Creates Epic, Feature and User Story items in Azure DevOps. Safe to re-run.

DevelopmentAgentTakes a ready story or a named bug. Works in its own workspace on a story branch. Claude reads and writes files and runs the build in a loop. Drafts test cases for review, opens a pull request and updates the work item. Never merges its own pull request.

PRReviewAgentReviews open pull requests, posts a comment and votes at most Approved with suggestions. Never merges.

ReleaseAgentBuilds the latest master, deploys to Kestrel or IIS, runs health checks and comments on the work items. Never rolls back by itself. Uses no AI model.

TestAgentTest cases are plain Given, When, Then rows in Excel. Claude turns them into Cucumber feature files, which is the only AI step.

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