When using standard SQL or the object layer in InterSystems IRIS, metadata consistency is usually maintained through built-in validation and type enforcement. However, legacy systems that bypass these layers—directly accessing globals—can introduce subtle and serious inconsistencies.

For my hundredth article on the Developer Community, I wanted to present something practical, so here's a comprehensive implementation of the GPG Interoperability Adapter for InterSystems IRIS.

Every so often, I would encounter a request for some GPG support, so I had several code samples written for a while, and I thought to combine all of them and add missing GPG functionality for a fairly complete coverage. That said, this Business Operation primarily covers data actions, skipping management actions such as key generation, export, and retrieval as they are usually one-off and performed manually anyways. However, this implementation does support key imports for obvious reasons. Well, let's get into it.

Over the past couple of months, I have been working on the SMART on FHIR EHR Launch to test the capabilities of IRIS for Health using two open-source apps from CSIRO: SMART-EHR-Launcher and SMART Forms App. This journey has been incredibly interesting, and I’m truly grateful for the opportunity to work on this task and explore more of IRIS for Health’s potential.

I know that people who are completely new to VS Code, Git, Docker, FHIR, and other tools can sometimes struggle with setting up the environment. So I decided to write an article that walks through the entire setup process step by step to make it easier to get started.

I’d really appreciate it if you could leave a comment at the end - let me know if the instructions were clear, if anything was missing, or if there’s anything else you'd find helpful.

The setup includes:

✅ VS Code – Code editor
✅ Git – Version control system
✅ Docker – Runs an instance of IRIS for Health Community
✅ VS Code REST Client Extension – For running FHIR API queries
✅ Python – For writing FHIR-based scripts
✅ Jupyter Notebooks – For AI and FHIR assignments

Before you begin: Ensure you have administrator privileges on your system.

In addition to reading the guide, you can also follow the steps in the videos:

For Windows

https://www.youtube.com/embed/IyvuHbxCwCY
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As we all know, InterSystems is a great company.

Their products can be just as useful as they are complex.

Yet, our pride sometimes prevents us from admitting that we might not understand some concepts or products that InterSystems offers for us.

Today we are beginning a series of articles explaining how some of the intricate InterSystems products work, obviously simply and clearly.

In this essay, I will clarify what Machine Learning is and how to take advantage of it.... because this time, you WILL KNOW for sure what I am talking about.

Due to MySQL's interpretation of SCHEMA differing from the common SQL understanding (as seen in IRIS/SQL Server/Oracle), our automated Linked Table Wizard may encounter errors when attempting to retrieve metadata information to build the Linked Table.

(This also applies to Linked Procedures and Views)

Introduction

Database performance has become a critical success factor in a modern application environment. Therefore identifying and optimizing the most resource-intensive SQL queries is essential for guaranteeing a smooth user experience and maintaining application stability.

This article will explore a quick approach to analyzing SQL query execution statistics on an InterSystems IRIS instance to identify areas for optimization within a macro-application.

Rather than focusing on real-time monitoring, we will set up a system that collects and analyzes statistics pre-calculated by IRIS once an hour. This approach, while not enabling instantaneous monitoring, offers an excellent compromise between the wealth of data available and the simplicity of implementation.

We will use Grafana for data visualization and analysis, InfluxDB for time series storage, and Telegraf for metrics collection. These tools, recognized for their power and flexibility, will allow us to obtain a clear and exploitable view.

More specifically, we will detail the configuration of Telegraf to retrieve statistics. We will also set up the integration with InfluxDB for data storage and analysis, and create customized dashboards in Grafana. This will help us quickly identify queries requiring special attention.

To facilitate the orchestration and deployment of these various components, we will employ Docker.

The "Ask Developer Community AI" tool is an excellent resource for studying for the certification. I asked it about each topic that will be covered in the test and the results are below.
Note: I classified each answer by the assertiveness that I consider as good, average and bad.

Note 2: The article has 4 parts, each one for an exam area.

The "Ask Developer Community AI" tool is an excellent resource for studying for the certification. I asked it about each topic that will be covered in the test and the results are below.
Note: I classified each answer by the assertiveness that I consider as good, average and bad.

Note 2: The article has 4 parts, each one for an exam area.

InterSystems FAQ rubric

The default settings for Stay Connected and Pool Size for business hosts using the TCP adapter are as follows:

Hi Community,

In this article, I will introduce my application iris-AgenticAI .

The rise of agentic AI marks a transformative leap in how artificial intelligence interacts with the world—moving beyond static responses to dynamic, goal-driven problem-solving. Powered by OpenAI’s Agentic SDK , The OpenAI Agents SDK enables you to build agentic AI apps in a lightweight, easy-to-use package with very few abstractions. It's a production-ready upgrade of our previous experimentation for agents, Swarm.
This application showcases the next generation of autonomous AI systems capable of reasoning, collaborating, and executing complex tasks with human-like adaptability.

Application Features

• Agent Loop 🔄 A built-in loop that autonomously manages tool execution, sends results back to the LLM, and iterates until task completion.
• Python-First 🐍 Leverage native Python syntax (decorators, generators, etc.) to orchestrate and chain agents without external DSLs.
• Handoffs 🤝 Seamlessly coordinate multi-agent workflows by delegating tasks between specialized agents.
• Function Tools ⚒️ Decorate any Python function with @tool to instantly integrate it into the agent’s toolkit.
• Vector Search (RAG) 🧠 Native integration of vector store (IRIS) for RAG retrieval.
• Tracing 🔍 Built-in tracing to visualize, debug, and monitor agent workflows in real time (think LangSmith alternatives).
• MCP Servers 🌐 Support for Model Context Protocol (MCP) via stdio and HTTP, enabling cross-process agent communication.
• Chainlit UI 🖥️ Integrated Chainlit framework for building interactive chat interfaces with minimal code.
• Stateful Memory 🧠 Preserve chat history, context, and agent state across sessions for continuity and long-running tasks.

Hi all,

As part of the development an API to know what is the instance of IRIS is connected, I've found some methods to know information about the server that can help you.

Get the server name: $SYSTEM.INetInfo.LocalHostName()

Get the server IP: $SYSTEM.INetInfo.HostNameToAddr($SYSTEM.INetInfo.LocalHostName())

Get the instance name: $PIECE($SYSTEM,":",2)

I just realized I never finished this serie of articles!

In today's article, we'll take a look at the production process that extracts the ICD-10 diagnoses most similar to our text, so we can select the most appropriate option from our frontend.

Looking for diagnostic similarities:

From the screen that shows the diagnostic requests received in HL7 in our application, we can search for the ICD-10 diagnoses closest to the text entered by the professional.

Prompt

Firstly, we need to understand what prompt words are and what their functions are.

Prompt Engineering

Hint word engineering is a method specifically designed for optimizing language models.
Its goal is to guide these models to generate more accurate and targeted output text by designing and adjusting the input prompt words.