But how does agentic AI work under the hood?
How are models able to understand when to use a tool and how to use it?

In this demo intensive session Alan will explain the concepts of agentic AI in detail. Starting with the basics of function calling in the Open AI models, he will cover the techniques used to provide tools to models, and how the models are able to understand and use the tools. The use of prompting to guide models and improve tool call behavior will be covered, along with guidelines for adding tools to your solution. Alan will also cover the protocols used by Open AI models as well as how the model context protocol is used to provide compatibility between services and LLM solutions.

Join this session if you want to understand what goes on under the hood of function calling, MCP and agentic AI.


Talk (60 min)

AI Agents In-Depth – Function Calling, MCP and Tool Use Under the Hood

Machine Learning

GenAI

Breaking and hardening the AI Cage

Security

In this session, we’ll cut through the hype and focus on practical, developer-tested strategies for getting the most out of AI in your workflow.

You’ll learn how to master prompting and context engineering, make the most of long context windows, streamline debugging and testing, and integrate AI into your daily development practices without sacrificing code quality or security. We’ll also explore how emerging standards like Model Context Protocol (MCP) unlock new workflows by connecting your AI assistant directly to tools such as GitHub, Slack, Playwright or Figma — turning it from a code generator into a true development teammate.

Whether you’re new to AI-assisted coding or already experimenting with it daily, you’ll leave this talk with concrete ideas and techniques you can apply immediately to level up your development process.

10 tips to level up your ai-assisted coding

By now most of you have set up your own coding agent, maybe with sub-agents, custom instructions, and skills. But how do you go from a useful assistant to a system that runs autonomously, overnight, unsupervised, in production?

I'll walk through how we built a fully autonomous development workflow at Nordic Corporate Bank where an AI agent picks up a task, implements the solution, runs tests, creates a pull request, and reviews its own code, all before a human ever gets involved.

When developers open their laptops in the morning, finished pull requests are waiting for review. Build failures and production exceptions are automatically routed to the agent, which fixes them right away. Developers spend their time on architecture, design, and the problems that actually require a human, plus building the system that builds the system.
This is not a proof of concept or a keynote demo. This is code deployed to production every day. In a bank.

How We Built an Autonomous Development Workflow at Nordic Corporate Bank

In this talk, I’ll share practical agentic AI workflows, relevant research on why this way of working matters, and what happens when you take it further with agent teams working in parallel.

How do we preserve the mental model when AI generates thousands of lines of code in seconds?

Expect concrete examples throughout, set in a retro pixel-art cyberpunk world spanning ocean floors and desert landscapes.

Polymorphic Mindset

In this session, I'll share what I've learned from using Claude Code daily and teaching it to developers in workshops. We'll go into the features that change how you work. Things like shaping Claude's behaviour through 

, building your own commands and skills, automating feedback loops, and understanding the small configuration details that most people overlook.

We'll also look at use cases beyond software development. We'll use Claude Code as an actual AI-assistant, not only a code-assistant.

This isn't a getting-started guide. It's a tour of the parts of Claude Code that only become visible once you stop treating it like an autocomplete tool.

If you use Claude Code but feel you're not getting the most out of it, this session will help you see what it can really do.

What Claude Code Can Do That You Haven't Tried

Tools

Here's what we've picked up along the way patterns that held up, things we had to rethink, and a few demos of where we are right now.

Agentic Engineering: Notes from Building and Shipping with Agents

Gary Lopez - Placeholder

Although things worked, something felt off. Implementations were technically correct but oddly inconsistent.


I realized that AI didn't make any mistakes, but that I hadn't made the decisions. AI couldn't cross any boundaries, because I never drew them. Speed made it impossible to ignore implicit thinking.

This is not a talk about prompting, frameworks, tooling or models. It's about recognizing where decisions were missing all along. AI makes it uncomfortably fast to see where boundaries are missing.

NDC { AI }

Agenda

10:20 - 11:20 (UTC+01)

What Claude Code Can Do That You Haven't Tried

Gui Ferreira

Breaking and hardening the AI Cage

Stök .

Joona Hoikkala

Polymorphic Mindset

Marina Santos Haugen

11:40 - 12:40 (UTC+01)

AI Agents In-Depth – Function Calling, MCP and Tool Use Under the Hood

Alan Smith

How We Built an Autonomous Development Workflow at Nordic Corporate Bank

Hallstein Brøtan

13:40 - 14:40 (UTC+01)

Agentic Engineering: Notes from Building and Shipping with Agents

Mats Oustad

Gary Lopez - Placeholder

Gary Lopez

15:00 - 16:00 (UTC+01)

10 tips to level up your ai-assisted coding

Aleksander Stensby

Where thinking stops

Yngve Bakken Nilsen