Thirteen years ago, I attained dual undergraduate degrees in electrical engineering and math, then promptly started full-time at InterSystems using neither. One of my most memorable and stomach-churning academic experiences was in Stats II. On an exam, I was solving a moderately difficult confidence interval problem. I was running out of time, so (being an engineer) I wrote out the definite integral on the exam paper, punched it into my graphing calculator, wrote an arrow with “calculator” over it, then wrote the result.

Hi, colleagues!

As you can see the new topic of the programming contest - AI Agents.

The topic was over-hyped recently on the Internet and has different meanings. You might get curious about what we mean by AI agents in regard to the InterSystems programming contest.

Hi Developers!

We are happy to present the bonuses page for the applications submitted to the InterSystems AI Programming Contest!

See the results below.

Introduction

To achieve optimized AI performance, robust explainability, adaptability, and efficiency in healthcare solutions, InterSystems IRIS serves as the core foundation for a project within the x-rAI multi-agentic framework. This article provides an in-depth look at how InterSystems IRIS empowers the development of a real-time health data analytics platform, enabling advanced analytics and actionable insights. The solution leverages the strengths of InterSystems IRIS, including dynamic SQL, native vector search capabilities, distributed caching (ECP), and FHIR interoperability. This innovative approach directly aligns with the contest themes of "Using Dynamic SQL & Embedded SQL," "GenAI, Vector Search," and "FHIR, EHR," showcasing a practical application of InterSystems IRIS in a critical healthcare context.

Now it is possible ask your IRIS server using an AI Chat or compose other agent applications to get:

Hey Community!

We're happy to share the next video in the "Code to Care" series on our InterSystems Developers YouTube:

⏯ Agentic AI in Action: Building a Decision-Making Loop with LLMs

https://www.youtube.com/embed/TCR1LC49qmw
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☤ Care 🩺 Compass 🧭 - Proof-of-Concept - Demo Games Contest Entry

Introducing Care Compass: AI-Powered Case Prioritization for Human Services

In today’s healthcare and social services landscape, caseworkers face overwhelming challenges. High caseloads, fragmented systems, and disconnected data often lead to missed opportunities to intervene early and effectively. This results in worker burnout and preventable emergency room visits, which are both costly and avoidable.

Care Compass was created to change that.

Audience

Those curious in exploring new GenerativeAI usecases.

Shares thoughts and rationale when training generative AI for pattern matching.

Hi Developers!

Here're the technology bonuses for the InterSystems AI Programming Contest: Vector Search, GenAI and AI Agents that will give you extra points in the voting:

• Agent AI solution - 5
• Vector Search usage - 4
• Embedded Python - 3
• LLM AI or LangChain usage: Chat GPT, Bard, and others - 3
• IntegratedML usage - 3
• Docker container usage - 2
• ZPM Package deployment - 2
• Online Demo - 2
• Implement InterSystems Community Idea - 4
• Find a bug in Vector Search, or Integrated ML, or Embedded Python - 2
• First Article on Developer Community - 2
• Second Article On DC - 1
• First Time Contribution - 3
• Video on YouTube - 3
• Suggest a new idea - 1

See the details below.

Hi Community,

Check out this new video that shows how to build the UI for InterSystems FHIR server from scratch using AI with no previous frontend experience:

📺 Building and Deploying React Frontend for the InterSystems FHIR server in 20 minutes with Lovable AI

🗣 Presenter: @Evgeny Shvarov, Senior Manager of Developer and Startup Programs, InterSystems

https://www.youtube.com/embed/NmQipSlYaeg
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I am brand new to using AI. I downloaded some medical visit progress notes from my Patient Portal. I extracted text from PDF files. I found a YouTube video that showed how to extract metadata using an OpenAI query / prompt such as this one:

ollama-ai-iris/data/prompts/medical_progress_notes_prompt.txt at main · oliverwilms/ollama-ai-iris

If you’ve ever watched a true artisan—whether a potter turning mud into a masterpiece or a luthier bringing raw wood to life as a marvelous guitar—you know that magic isn’t in the materials, but in care, craft, and process. I know this firsthand: my handmade electric guitar is a daily inspiration, but I’ll admit—creating something like that is a talent I don’t have.

In the previous article, we saw how to build a customer service AI agent with smolagents and InterSystems IRIS, combining SQL, RAG with vector search, and interoperability.

In that case, we used cloud models (OpenAI) for the LLM and embeddings.

This time, we’ll take it one step further: running the same agent, but with local models thanks to Ollama.

See the Langchain IRIS Tool in action on YouTube. You can see IRIS metrics, discover classes, generate fake data, and so on. Project using Ollama, IRIS VectorDB, Streamlit and Langchain.