Most software starts out pretty simple: front end, back end, maybe a database, and you’re done. Then the client wants an API, a mobile app, notification emails, realtime chat; cloud gives you elastic hosting, apps that dynamically scale based on workload and traffic, and if you’re just starting out with distributed systems design, the possibilities can be overwhelming. APIs, message queueing, REST, GraphQL, gRPC… what should you choose, how does it work, how do you get started?

This workshop gives you a hands-on introduction to the most important messaging patterns used in modern application development. Using C# and .NET, we’ll build a series of small example apps and services, wire them together using these patterns, and discuss how - and why - you’d apply the same patterns in your own applications.


Working with HTTP APIs: testing and tooling

Interactive documentation with OpenAPI and Swagger

Distributed queues with RabbitMQ and EasyNetQ

Contract-first development using protocol definitions

Integrating browsers with distributed systems

Adding instrumentation to your distributed apps

Principles of observability: spans, traces, metrics and logs


This workshop is aimed at developers with some experience writing applications using C# and Microsoft .NET. The workshop can be run in person or online. Attendees will be writing and running .NET code during the workshop, so will need a computer running Windows, macOS or Linux, and the Microsoft .NET 10 SDK from 

. The sample application and examples used during the workshop all run on .NET 10, and are tested on Linux, macOS and Windows.

2 Days

1-2 June

Building Distributed Systems with .NET 10

.NET

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.- Securing your agentic code

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

Two-day training session on AI agents, MCP, and Skills facilitated by Aleksander Stensby from GritAI. This training gives you the foundation to build MCP servers and MCP Apps, reusable agent skills, and wire everything into capable autonomous agents that actually work.

This hands-on workshop will equip you with practical skills to build your own MCP servers, CLI tool wrappers, and interactive MCP Apps — and then go further by creating reusable skills that turn simple tool-calling agents into capable, autonomous systems. You'll leave with a working agent powered by tools and skills you built yourself.

Day 1: Context & Connectivity — MCP Servers, Apps & Tools

Introduction to the AI agent stack: LLMs in a loop with tools and why context is king

Core MCP concepts: Host/client/server architecture, tools, resources, and prompts

Building a working local MCP server (Python or TypeScript)

MCP Apps: Adding interactive UI components to your MCP Server

CLI tools with direct API access as an alternative and complement to MCP

Hands-on exercises: Build an MCP server, extend it with an MCP App

What skills are and why they're the missing layer in most agent setups

Skills vs. tools vs. prompts: Understanding the capability stack

The skill creator pattern: Using AI to capture and formalise skills from workflows

Workflow capture: Turning expert knowledge into reusable agent skills

Agentic loop architecture: How LLMs, tools, and skills combine across frameworks

Provider-agnostic patterns: Claude, OpenAI, Gemini and beyond

Final exercise: Building a capable agent combining MCP tools and custom skills

Basic Python or JavaScript programming knowledge

Familiarity with AI/ML concepts (helpful but not required)

Laptop with necessary development environment

This training is targeted at developers and people with a medium+ technical skill level and interest for AI.



Bring your laptop with your preferred IDE and either Python or Node installed. We recommend an AI coding agent like Claude Code, Cursor, or GitHub Copilot.
Bring your favorite sauce! You need access to an LLM provider like Anthropic, OpenAI or Azure Foundry, but you can also use Ollama if you would like to run everything locally and free. Claude Code is highly recommended and will be used for a lot of the demonstrations during the workshop.

Context and Skills Are All Your Agent Needs

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)

Build Your First AI Agent in .NET

GenAI

This two-day workshop takes a deliberate journey from foundational messaging concepts to the patterns required for real-world, production-grade systems. We will start with the simplest possible asynchronous designs—queues, events, and pub/sub—and progressively evolve them as new problems emerge: duplicates, message loss, scaling limits, partial failures, and long-running workflows.

Instead of presenting messaging patterns as a checklist, this workshop shows why each pattern exists, what breaks without it, and what trade-offs it introduces. Every step forward is motivated by a concrete failure mode.


Over two days, we will grow a system from "Messages get sent" to: "Messages survive crashes, retries, scaling, and time"

Retries, poison messages, and dead-letter queues

But we will not stop there; we will also talk about building a UI for our system.

By the end of the workshop, you won’t just know these patterns — you will gain knowledge of when you actually need them, when you don't, and how to combine them without creating accidental complexity.

From Basics to Beyond: Building Production-Grade Messaging Systems

What are the different architectures? Why should we choose one over the other? Or should we choose a combination of them for some applications? How do we make such decisions? What should be the process or the approach that will lead us towards selecting a more suitable or appropriate architecture, one that comes from proper analysis rather than infatuation or desire to build something because that's seems to be the current trend.


In this workshop, we will take a very systemic approach to going from requirements to choosing suitable architectures. We will cover the following topics, not by simple talking about these, but by way of hands-on exercises and interactive and collaborative sessions:

- Challenges with selecting Architectures
-  Functional and non-functional requirements
- Evaluating trade offs
- Methodical Mapping from requirements to patterns
- Architectural Patterns
- Underpinnings of monoliths and distributed architectures
- Hands-on exercises to analyze application requirements
- Groups labs to evaluate the architectural patterns for different application characteristics
- Using AI to assist with requirements analysis
- Cautiously Using AI to guide along the selection process
- Next steps beyond choosing architecture




- Senior programmers
- Team leads*Architects
- Technical managers and directors



Dr. Venkat Subramaniam is an award-winning author, founder of Agile Developer, Inc., an instructional professor at the University of Houston, and creator of the dev2next and Arc of AI conferences.
He has trained and mentored thousands of software developers in the US, Canada, Europe, Asia, and Africa, and is a regularly-invited speaker at several international conferences. Venkat helps his clients effectively apply and succeed with sustainable agile practices on their software projects.
Venkat is a (co)author of multiple technical books, including the 2007 Jolt Productivity award winning book Practices of an Agile Developer. You can find a list of his books at <

NDC { Copenhagen }

Workshops

09:00 - 17:00 (UTC+02)

Vibe Coding for Production

Nick Chapsas

Context and Skills Are All Your Agent Needs

Aleksander Stensby

Build Your First AI Agent in .NET

Gui Ferreira

From Basics to Beyond: Building Production-Grade Messaging Systems

Poornima Nayar

Architectural Patterns: Common Solutions for Building Large Scale Systems

Venkat Subramaniam

Building Distributed Systems with .NET 10

Dylan Beattie