/Events /AgentCon 2025 - Copenhagen /Schedule
AgentCon 2025 - Copenhagen
Thursday, June 19
Welcome + Registration
Main Track ·Empowering Developers with Next-Gen AI
Welcome to a new frontier of intelligent application development with Azure AI Foundry. In this keynote, discover how AI Agents are redefining the way applications understand, reason, and take action. Learn how Azure AI Foundry equips developers with intuitive tools and frameworks to build AI-driven solutions that dynamically respond, learn, and adapt to real-world scenarios. From retrieval-augmented generation (RAG) to vector search, explore how Agents revolutionize information retrieval and decision-making. Get inspired and see how developers can harness AI Agents to craft intelligent semi autonomous, and impactful applications that seamlessly integrate into business workflows and user experiences. The future of AI-powered development starts here.
As part of Developer Relations organisation at Microsoft, Lee leads a team of highly skilled and motivated advocates who deliver value to Microsoft customers and help them do more with less. Developer Relations collaborates with our customers and bring them on the journey of digital transformation highlighting how they can use Microsoft tools, services, and technologies. Lee participates closely throughout the whole project. Starting from the initial stages of brainstorming architecture to code reviews, pair programming, project documentation and blog post.
Learnings of building reliables agent systems
Going from the demo to production unveils systemic challenges that needs to be addressed early on for getting agentic systems right. Over the last couple of years of building agents we have learnt the hard way how to address those and make agentic systems reliable, this session will show how to tackle the common challenges and best practice learnt.
Alejandro brings two decades of experience in building complex systems, his expertise relies in Cloud Computing Distributed Systems and Machine Learning. On a daily basis he works as a technical advisor for global companies and a hands-on ML Engineer exploring and implementing latest technologies.
Lunch
Main Track ·Building an AI Agent that understands local development plans
Getting accurate insights into a property shouldn’t require sifting through outdated PDFs and legalese.
In this session, we’ll showcase an AI agent that does the hard work for you — surfacing rich, contextualized property information and parsing local development plans (lokalplaner) into human-readable insights.
You’ll get a detailed walkthrough of how the system ingests public data, uses agent workflows to extract and summarize key points, and interacts with users through a clean, responsive UI.
We’ll also break down the architecture, including the prompt design, retrieval pipelines, and how we handle rules using AI agents. Real demos, real code, real value.
Being on the edge of technology and innovation has always been my passion. My role as Tech Evangelist is a match made in heaven—I can use my passion and drive to build amazing and innovative solutions for one of the most dedicated and focused Microsoft partners in Denmark. I guess it is no surprise that I love the community. I get energized by arranging events for and with the community, and I feel amazed when speaking about how to use the newest technologies and the value they can bring. Throughout my 25+ years as a professional developer and strategic business influencer, I have participated in many kinds of conferences, community events, and public talks. These range from small community talks with a deep focus on specific technical topics to high-level discussions about the future of technology and humanity at large conferences.
A passionate young developer with over 7 years of experience in C#, I have spent the past 2 years specializing in artificial intelligence. Known for combining strong backend expertise with cutting-edge AI development, I bring fresh perspectives and technical depth to every project. Whether building scalable applications or exploring innovative AI solutions, I'm always dedicated to pushing the boundaries of what's possible in modern software development.
Grafana Assistant: how to build an observability assistant
In this talk, you'll receive a behind the scenes look how we build an observability assistant that is actually useful by writing queries and building dashboards. Learn more about how we experiment with single- and multi-agent setups, tool calling, evaluation and "human in the loop" concepts.
Maurice is a Product Manager at Grafana Labs where he is responsible for AI in observability within the Grafana ecosystem. He previously worked in the renewable energy and healthcare industries and specializes in delivering impactful AI solutions.
Yasir Ekinci is a Principal Software Engineer in the Grafana AI squad at Grafana Labs, with over a decade of experience in machine learning. Besides wrangling the machine, he can be found scuba diving, and tinkering with a homelab.
Coffee Break
Main Track ·From Text to Insight: Building Multimodal AI Agents for Complex Real Estate Document Processing
In the fragmented world of real estate documentation, where standards vary wildly and critical information hides in text and visual elements, we built an agentic system using LangChain that transforms chaotic documents into structured, actionable insights. This talk walks through our journey from traditional NER approaches to a multimodal agent architecture that handles the complexity of diverse real estate documents like the Danish "Energimærkning" (Energy Label), where crucial information is icons rather than text. I'll share practical insights on when to use managed services versus custom LLM solutions, how we addressed verification challenges, and our approach to rebuilding structured output through our document builder component for comprehensive buyer guides. Key Takeaways
- Practical comparison of NER approaches versus LLM-based extraction for non-standardised documents
- The cost of development and training is lower than with traditional ML
- Real-world implementation challenges and solutions for multimodal document analysis in production
- Strategies for reconstructing structured information from unstructured sources
Magnus Udbjørg is the Chief Technology Officer at TestaViva, where he spearheads the development of innovative software solutions that empower organisations to enhance product quality and accelerate time-to-market. With over 15 years of experience in software engineering and leadership, Magnus has a proven track record of architecting scalable systems and driving cross-functional teams to deliver mission-critical applications.
Achieving Precision in AI: Retrieving the Right Data Using AI Agents
In the race to harness the power of generative AI, organizations are discovering a hidden challenge: precision. Models are only as effective as the data they access, yet most approaches to Retrieval-Augmented Generation (RAG) lack the dedicated, fine-tuned pipelines needed to ensure the right information is delivered at the right time.
Today, most RAG systems pull from vast, generalized data lakes, leading to noisy outputs and frustrating inefficiencies. The result? Wasted resources, inconsistent responses, and missed opportunities for real-time decision-making. But what if you could create an AI system that doesn’t just retrieve data—but understands its context, delivering precise, actionable insights in milliseconds?
This is where agenticRAG comes into play—a breakthrough in AI architecture that pairs dedicated retrieval pipelines with intelligent agents to deliver pinpoint accuracy. By segmenting your data storage and retrieval processes specifically for training vs. inference, you can achieve hyper-focused precision while dramatically reducing latency and costs. Imagine an AI system that knows exactly what data it needs and how to get it with zero lag—a system that’s tuned to perform like a well-trained expert in your domain.
Curious to discover how you can optimize your AI applications for laser-focused accuracy? Join me as to learn more about AgenticRAG and fine tuning your models.
Adi is an experienced Software Engineer and people manager. For most of her professional life, she has worked with data and machine learning for operations and analytics. As a data practitioner, she developed algorithms to solve real-world problems using machine learning techniques and leveraging expertise in Apache Spark, Kafka, HDFS, and distributed large-scale systems. Adi has taught Spark to thousands of students and is the author of the successful book — Scaling Machine Learning with Spark. In early 2024, she embarked on a new adventure with data streaming, specifically Flink, and she can't get enough of it.