Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.
Keywords: IRIS, Agents, Agentic AI, Smart Apps
Transformer based LLMs appear to be a pretty good "universal logical–symbolic abstractor". They started to bridge up the previous abyss among human languages and machine languages, which in essence are all logic symbols that could be mapped into the same vector space.
Wondering for 3 years we might be able to just use English (etc human natural languages) to do IRIS implementations as well, one day.
You’ve seen how tools like Lovable are shaking up web development. People are spinning up entire apps just by talking to an AI, almost like pair‑programming on steroids.
Now imagine bringing that same “vibe coding” experience into healthcare. know, it sounds crazy. Healthcare is complex, full of regulations, and usually gives us a headache just thinking about the interoperability rules.
That’s exactly the space where withLove lives: an AI‑Native, Low‑Code platform built entirely on InterSystems IRIS for Health.
Hi developers!
I'm testing vibecoding with ObjectScript and my silicon friend created a code-block that got me thinking "what's wrong"?
Here is the piece of code:
for01AI wanted to quit from a method with a return value. Good intention, but bad use of the command.
And ObjectScript compiler compiles this code with no error(?) (syntax linter in VSCode says it's a syntax, kudos @Brett Saviano ).
But in action, it produces <COMMAND>, of course.
Wanted to share with you as this is a good case where return
Welcome to the finale of our journey in building MAIS.
Now, the stage is set. Our agents are ready, defined, and eager to work. However, they remain frozen in time. They require a mechanism to drive the conversation, execute their requested tools, and pass the baton to one another.
Today, we will assemble the Nervous System
10:47 AM — Jose Garcia's creatinine test results arrive at the hospital FHIR server. 2.1 mg/dL — a 35% increase from last month.
What happens next?
No chatbot. No manual prompts. No black-box reasoning.
This is event-driven clinical decision support with full explainability:
✅ Triggered automatically by FHIR events ✅ Multi-agent reasoning (context, guidelines, recommendations) ✅ Complete audit trail in SQL (every decision, every evidence source) ✅ FHIR-native outputs (DiagnosticReport published to server)
Built with:
You'll learn: 🖋️ How to orchestrate agentic AI workflows within production-grade interoperability systems — and why explainability matters more than accuracy alone.
In Part 1, we laid the technical foundation of MAIS (Multi-Agent Interoperability Systems). We have successfully wired up the 'Brain', built a robust Adapter using LiteLLM, locked down our API keys with IRIS Credentials, and finally cracked the trick code on the Python interoperability puzzle.
However, right now our system is merely a raw pipe to an LLM. It processes text, but it lacks identity.
Today, in Part 2, we will define the Anatomy of an Agent We will move from simple API calls to structured Personas.
Some concepts make perfect sense on paper, whereas others require you to get your hands dirty. Take driving, for example. You can memorize every component of the engine mechanics, but that does not mean you can actually drive.
You cannot truly grasp it until you are in the driver's seat, physically feeling the friction point of the clutch and the vibration of the road beneath. While some computing concepts are intuitive, Intelligent Agents are different. To understand them, you have to get in the driver's seat.
Hey Community!
We're happy to share the next video in the "Code to Care" series on our InterSystems Developers YouTube:
Hi everyone.
I'm going to give you a quick tip on how to implement an AI agent to search the Intersystems documentation integrated into Teams.
Yes, I know the documentation page has its own AI search engine and it's quite effective, but this way we'd have faster access, especially if Teams is your company's corporate tool.
You can also create another AI agent to search articles published in the developer community (which also has its own integrated AI search engine).
Hi,
We're working on new capabilities to help you build Agents and AI applications faster with InterSystems IRIS. In order to better understand which entry points and development methodologies would help you most, we've created this brief survey: Building AI solutions with InterSystems IRIS.
Filling it in should not take much more than 5 minutes, and your feedback on this exciting topic will help us fine tune our designs and prioritize the right features.
Thanks in advance!
benjamin
Hey Community!
We're happy to share the next video in the "Code to Care" series on our InterSystems Developers YouTube:
In this article I will be discussing the use of an alternative LLM for generative IA. OpenIA is commonly used, in this article I will show you how to use it and the advantages of using Ollama
In the generative AI usage model that we are used to, we have the following flow:
Hey Developers,
Enjoy the new video on InterSystems Developers YouTube
⏯ Integrating AI Agents into InterSystems IRIS - Patterns and Techniques @ READY 2025
Hey Developers,
Enjoy the new video on InterSystems Developers YouTube
Hey Community,
Enjoy the new video on InterSystems Developers YouTube:
⏯ Analytics and AI with InterSystems IRIS - From Zero to Hero @ Ready 2025
Yes, yes! Welcome! You haven't made a mistake, you are in your beloved InterSystems Developer Community in Spanish.
You may be wondering what the title of this article is about, well it's very simple, today we are gathered here to honor the Inquisitor and praise the great work he performed.
Perfect, now that I have your attention, it's time to explain what the Inquisitor is. The Inquisitor is a solution developed with InterSystems technology to subject public contracts published daily on the platform https://contrataciondelestado.es/ to scrutiny.
Hey Community,
Enjoy the new video on InterSystems Developers YouTube:
This anthropic article made me think of several InterSystems presentations and articles on the topic of data quality for AI applications. InterSystems is right that data quality is crucial for AI, but I imagined there would be room for small errors, but this study suggests otherwise. That small errors can lead to big hallucinations. What do you think of this? And how can InterSystems technology help?
In my previous article, I introduced the FHIR Data Explorer, a proof-of-concept application that connects InterSystems IRIS, Python, and Ollama to enable semantic search and visualization over healthcare data in FHIR format, a project currently participating in the InterSystems External Language Contest.
In this follow-up, we’ll see how I integrated Ollama for generating patient history summaries directly from structured FHIR data stored in IRIS, using lightweight local language models (LLMs) such as Llama 3.2:1B or Gemma 2:2B.
The goal was to build a completely local AI pipeline that can extract, format, and narrate patient histories while keeping data private and under full control.
All patient data used in this demo comes from FHIR bundles, which were parsed and loaded into IRIS via the IRIStool module. This approach makes it straightforward to query, transform, and vectorize healthcare data using familiar pandas operations in Python. If you’re curious about how I built this integration, check out my previous article Building a FHIR Vector Repository with InterSystems IRIS and Python through the IRIStool module.
Both IRIStool and FHIR Data Explorer are available on the InterSystems Open Exchange — and part of my contest submissions. If you find them useful, please consider voting for them!
With the rapid adoption of telemedicine, remote consultations, and digital dictation, healthcare professionals are communicating more through voice than ever before. Patients engaging in virtual conversations generate vast amounts of unstructured audio data, so how can clinicians or administrators search and extract information from hours of voice recordings?
Enter IRIS Audio Query - a full-stack application that transforms audio into a searchable knowledge base. With it, you can:
Hey Community,
The InterSystems team put on our monthly Developer Meetup with a triumphant return to CIC's Venture Café, the crowd including both new and familiar faces. Despite the shakeup in both location and topic, we had a full house of folks ready to listen, learn, and have discussions about health tech innovation!
Hey Community,
The InterSystems team recently held another monthly Developer Meetup in the AWS Boston office location in the Seaport, breaking our all-time attendance record with over 80 attendees! This meetup was our second time being hosted by our friends at AWS, and the venue was packed with folks excited to learn from our awesome speakers.
The topic of the August meetup was Agentic Orchestration & Multi-LLM Systems, and our speakers brought some amazing demos: First, @Nicholai.
Hi Community,
We're excited to share the new video in the "Rarified Air" series on our InterSystems Developers YouTube:
⏯ Leading with Empathy: The Human Side of Customer Centricity
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Along this OMOP Journey, from the OHDSI book to Achilles, you can begin to understand the power of the OMOP Common Data Model when you see the mix of well written R and SQL deriving results for large scale analytics that are shareable across organizations. I however do not have a third normal form brain and about a month ago on the Journey we employed Databricks Genie to generate sql for us utilizing InterSystems OMOP and Python interoperability.
Hey Community,
Enjoy the new video on InterSystems Developers YouTube:
Hey Community,
Enjoy the new video on InterSystems Developers YouTube:
Hey Community,
We're excited to invite you to the next InterSystems UKI Tech Talk webinar:
👉AI Vector Search Technology in InterSystems IRIS
⏱ Date & Time: Thursday, September 25, 2025 10:30-11:30 UK
Speakers:
👨🏫 @Saurav Gupta, Data Platform Team Leader, InterSystems
👨🏫 @Ruby Howard, Sales Engineer, InterSystems
Hey Community!
We're happy to share the next video in the "Code to Care" series on our InterSystems Developers YouTube:
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.
