In this two-day masterclass by Sam Newman, you’ll examine how architecture can change and adapt, not only to enable new types of AI-assisted SDLC, but also to build AI-enabled products. You’ll also explore how to architect for uncertainty - helping give you flexibility and reduce risk as the AI landscape shifts around you.

2 Days

25-26 Jan

Architecting For AI

Architecture

Rather than moving your data into a separate search system, you'll learn how Azure SQL can combine relational data, full-text search, vector search, and AI models to deliver relevant, grounded results. You'll build the pipeline yourself, from embeddings and retrieval through ranking and generation. This matches the workshop's core goal of letting SQL developers own the end-to-end AI search pipeline.

Preparing data for AI: Structure, chunk, and enrich existing relational data for retrieval.

Embeddings and vector search: Generate embeddings, create vector indexes, and retrieve by meaning instead of exact words.

Full-text and hybrid search: Combine semantic and keyword retrieval to overcome the limitations of either approach alone.

Query rewriting: Turn natural-language requests into better searches, filters, and retrieval strategies.

Ranking and reranking: Fuse results with techniques such as Reciprocal Rank Fusion and improve which results reach the model. The workshop specifically demonstrates why combining vector and full-text rankings produces more robust retrieval.

RAG and agents: Use retrieved application data to ground AI responses and give applications and agents access to production-style data.

We'll use Azure SQL Database throughout, including its native vector type and indexes, full-text search, external models, and integration with Microsoft Foundry.

A working end-to-end RAG retrieval pipeline

Practical experience with vector, full-text, and hybrid search

A clear understanding of embeddings, query rewriting, ranking, and RRF

An AI application grounded in real relational data

Reusable patterns for building reliable, scalable AI applications and agents

Yes, if you know SQL and want to move beyond AI demos into applications grounded in real data.

You don't need to be an AI expert. If you understand basic SQL and have some familiarity with prompts, embeddings, or RAG, you're ready.

Building Reliable AI Apps and Agents with Azure SQL Database

Database

AI Agents

To be fair, the people who abuse this term are also the people who probably fall under this category. I prefer “Agentic Programming” instead. I have pushed thousands of lines of code in production using agentic programming. Code that would have taken me months to write myself now takes merely days.

In this workshop, we’ll explore agentic programming as a disciplined engineering practice, not a toy.
Using Claude Code, I’ll show how to collaborate with AI agents to produce maintainable, reviewable, and production-ready code, without surrendering architectural control, correctness, or performance.

What Claude Code, Codex and Cursor are, how they work, and how you should use them

Designing clear agent boundaries and responsibilities.

How to turn vague intent into explicit, testable instructions.

Create plans that can run for hours on end, generating and testing code as they go.

Much more (workshop contents are constantly updated as new models and tools are released)

This is not a “let the AI take the wheel” session. It’s about teaching the AI to work like a senior engineer, while you stay firmly in charge. If you believe AI coding tools are dangerous, flaky, or fundamentally incompatible with serious software engineering, then this workshop is for you.



A laptop with your favourite IDE installed

Vibe Coding for Production

In this hands-on workshop, you'll build AI agents that can reason about data, use tools, and hold meaningful conversations.

You'll create a personal finance assistant. An agent that understands natural language, retrieves transaction data, maintains memory across conversations, and helps users make better financial decisions.




- Working with Large Language Models: LLM fundamentals, prompt engineering basics, and raw API calls without abstractions.
- Calling AI services from C#.
- Tool calling: Giving agents the ability to take actions and retrieve external data.
- Reasoning loops: How agents think, plan, and iterate.
- Memory and context management: Maintaining state across conversations.

We'll use Azure AI services throughout the workshop, but the patterns and code apply to other providers like OpenAI, Anthropic, and Google.


By the end of this workshop, you'll have:


- A working AI agent integrated into a .NET backend
- Practical experience with tool use and memory
- A clear mental model for when agents add value versus simpler AI approaches
- Confidence to apply these patterns in your own applications


Yes, if you want to move beyond basic AI integration and build something that reasons. 


- Know how to build APIs with C#
- Have some exposure to AI concepts (prompts, completions, embeddings)
- Prefer learning by doing over slides and theory



- Your favourite IDE
- .NET 10
- Docker
- Git and a GitHub account
- An Azure account (expect around 5–10 € of usage)

NDC { London }

Workshops

09:00 - 17:00 (UTC±00)

Vibe Coding for Production

Nick Chapsas

Build Your First AI Agent in .NET

Gui Ferreira

Architecting For AI

Sam Newman

Building Reliable AI Apps and Agents with Azure SQL Database

Jerry Nixon