Home Events AI Engineering for Intelligent .NET Systems: RAG, Agents & Enterprise Integration

AI Engineering for Intelligent .NET Systems: RAG, Agents & Enterprise Integration

Description

This course explores how to design and build intelligent, AI-powered applications within .NET ecosystems using modern techniques such as Retrieval-Augmented Generation (RAG), agent-based workflows, and enterprise-grade AI integration. Participants will learn how to extend ASP.NET Core systems with large language models, connect AI to structured and unstructured data sources, and orchestrate multi-step reasoning using agents. The course emphasizes practical implementation using Azure OpenAI, Semantic Kernel, and vector-based retrieval, enabling the development of scalable, secure, and production-ready AI features. Through hands-on exercises, learners will build end-to-end intelligent services that go beyond traditional APIs.

 

🕒 Duration: 8 hours

👥 Target Audience:

  • Software Engineers/ Architects (.NET, Backend, Full stack), DevOps / SRE Engineers, CTOs / Engineering Managers

 

  • Seniority: Mid-Senior
Webinar Content
Module 1: AI System Architecture in .NET Introduction to LLMs & Enterprise Integration From APIs to Intelligent Services Foundations of AI-powered systems
  • Overview of LLM capabilities and limitations
  • Integrating Azure OpenAI with ASP.NET
  • Core APIs Designing
  • AI-native endpoints vs traditional APIs
  • Architectural patterns for AI-enabled services

 

  • AI Practice: Build a simple ASP.NET Core endpoint that calls an LLM to process user input
Module 2: Retrieval-Augmented Generation (RAG) Data Integration (SQL + Documents) Embeddings & Vector Search Building RAG pipelines
  • Concepts of embeddings and vector databases
  • Connecting SQL Server and external documents to LLMs
  • Implementing retrieval pipelines with Azure
  • AI Search or similar
  • Improving response accuracy using contextual data

 

  • AI Practice: Build a RAG pipeline that retrieves data from SQL Server and enhances LLM responses
Module 3: Agent-Based Systems Semantic Kernel / Agent Frameworks Tool Usage & Multi-step Reasoning Designing agent workflows
  • Agent architecture (tools, memory, orchestration)
  • Implementing multi-step workflows (task → reasoning → action)
  • Using Semantic Kernel or LangGraph concepts in .NET
  • Integrating APIs and tools into agent flows

 

  • AI Practice: Create a simple agent that processes a task, queries data, and returns a structured response
Module 4: Enterprise Integration & Evaluation Security & Responsible AI AI System Testing & Optimization Production readiness
& evaluation
  • Secure integration with Azure OpenAI (private endpoints, data control)
  • Handling hallucinations and response validation
  • Evaluation techniques for AI outputs (quality, relevance)
  • Monitoring and improving AI system performance

 

  • AI Practice: Evaluate AI responses, detect issues, and refine prompts or retrieval strategy for improved accuracy

 

Learning Objectives:

After attending this webinar participants will be able to:

  • Design and implement AI-powered APIs using ASP.NET Core
  • Build RAG pipelines integrating SQL Server and vector search
  • Develop agent-based workflows using Semantic Kernel or similar frameworks
  • Integrate Azure OpenAI securely within enterprise architectures
  • Evaluate and improve AI system responses and reliability
Prerequisite Knowledge
  • Experience with .NET (ASP.NET Core Web APIs) and basic database concepts
  • Familiarity with REST APIs and general software architecture

 

Tags:

Date

28 May 2026, 16:00-19:00

Cost

Free

Labels

AI-Native
Category

Organizer

Code.Hub
Email
[email protected]

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