I have a Business Process that needs to call an external API that may take several seconds to respond. I'm worried that keeping the BP blocked waiting for the response could affect the overall Production throughput. Is there a recommended pattern for handling async calls inside a BPL or custom BP class to avoid this kind of bottleneck?

In our previous article, we explored the basics of unit testing in IRIS and the ways to apply it to a REST API. We even figured out how to test logic before finalizing network configurations and authentication, allowing us to focus solely on testing the API contents. Today, we will build upon that foundation and elevate our unit testing strategy by using another tool: %Populate .

At first glance, the %Populate class appears to be very simple. You can create a class that extends both %Persistent and %Populate and inherit a Populate method designed to generate randomized records.

Hi Community,

Interoperability metrics were enabled for a production using ##class(Ens.Util.Statistics).EnableSAMForNamespace(). However, after running DisableSAMForNamespace() and DisableStatsForProduction(), the metrics continue to appear in the endpoint output.

Is there a way to completely stop these interoperability metrics from displaying in the API metrics stream? Is there an additional step or cache clear required?

Good afternoon,

I have received a WSDL.

I have imported it successfully in HealthShare.

The external is requesting me to send messages with the following structure (using the auto-generated SOAP Bussiness Operation from the WSDL), as you see they need a custom http header titled "Appian-API-Key", as the following:

            curl --request POST \
              --url [ endpoint ] \
              --header 'Appian-API-Key:  << API KEY >> \
              --header 'Content-Type: application/xml' \
              --data '<?xml version='\''1.0'\'' encoding='\''utf-8'\''?>
            <soapenv:Envelope xmlns:soapenv='\''http://schemas.xmlsoap.org/soap/envelope/'\''>
              <soapenv:Body>
                <tns:acceptMessage xmlns:tns='\''http://ws.connectors.connect.mirth.com/'\''>
                <![CDATA[  Message  ]]>
                </tns:acceptMessage>
              </soapenv:Body>
            </soapenv:Envelope>'

 I’m currently working with REST APIs in InterSystems IRIS and would like to better understand the recommended security practices for production environments.

I’m especially interested in:

• Authentication and authorization methods
• Token management strategies
• Role-based access control
• API gateway recommendations
• Encryption and secure communication practices
• Monitoring and logging for API activity

I would also appreciate learning about common mistakes to avoid when deploying APIs publicly or integrating with external systems.

What approaches or tools have worked best in your environments?

Redoc is an Open Source solution capable of rendering API specifications in OpenAPI 2.0 or 3.0+ as very beautiful and functional web portals. Currently, to have something similar, we need the ZPM SwaggerUI extension or we need to install IAM - InterSystems API Manager and then configure the IAM Developer Portal. Well, now the community has one more option, iris-redoc. This solution installs a web application on your IRIS instance that uses Redoc to present a beautiful web portal for your REST APIs:

I want to develop a REST API for uploading PDF files. I started with OPENAPI spec file. When I test, I get error 8727 referencing missing required parameter file.

"{"$c(13,10,9)"""errors"":[ {"$c(13,10,9,9,9)"""code"":8727,"$c(13,10,9,9,9)"""domain"":""%ObjectErrors"","$c(13,10,9,9,9)"""error"":""ERROR #8727: Parameter required: file."","$c(13,10,9,9,9)"""id"":""RESTRequired"","$c(13,10,9,9,9)"""params"":[""file"""$c(13,10,9,9,9)"]"$c(13,10,9,9)"}"$c(13,10,9)"],"$c(13,10,9)"""summary"":""ERROR #8727: Parameter required: file.

We didn't start with a big AI strategy.

We had a legacy InterSystems Caché 2018 application, a lot of old business logic, and a practical need: build a new UI and improve code that had been running for years. At first, I thought an AI coding agent would help only with a small part of the work. Maybe some boilerplate, some REST work around the system, and a bit of help reading old ObjectScript.

In practice, it became much more than that.

Hello all,

I have a EnsLib.HTTP.GenericMessage inbound from a webhook with a GC stream.

My router is defined as the following:

Introduction — The Problem with AI Streaming in ObjectScript

Today, I would like to introduce a problem I encountered and the solution I found when integrating AI APIs into an ObjectScript application. My initial tests were successful, yet somewhat frustrating.

The HTTP call worked; the request was properly sent to my LLM APIs. But then, silence... a long wait. Eventually, the entire response arrived as a single block.

Technically, it worked, but the user experience was disappointing compared to a ChatGPT session.

If you have ever dug through a large IRIS namespace looking for where a particular string, method call, or pattern was used, you know the pain: there was no built-in way to do a grep-style search across your server-side ObjectScript code from VS Code — at least not without jumping through some hoops.

That is what ObjectScript Search fixes.

Try it today with a simple install from the VS Code Marketplace. If you don't like it, uninstalling is just as easy. But I think you will like it — it is a huge quality-of-life improvement for anyone doing ObjectScript development in VS Code.

The problem

How many times have we migrated an IRIS Instance to another machine, maybe even another version, and after a few days realized we forgot that one SSL Configuration critical for a Business Operations to work? Or maybe a credential, or a lonely class in a package by itself?

The solution

The simple solution is to make a checklist¹. A checklist of the entities we have to move. But simple checklists on Word documents are often forgotten, or just ignored.

Why This Integration Matters

InterSystems continues to push AI capabilities forward natively in IRIS — vector search, MCP support, and Agentic AI capabilities. That roadmap is important, and there is no intention of stepping back from it.

But the AI landscape is also evolving in a way that makes ecosystem integration increasingly essential. Tools like Dify — an open-source, production-grade LLM orchestration platform — have become a serious part of enterprise AI stacks.

iris-budget

I created iris-budget app for the InterSystems Full Stack Contest in 2026. By full stack, we mean a frontend web or mobile application that inserts, updates, or deletes data in InterSystems IRIS via REST API, Native API, ODBC/JDBC, or Embedded Python.

My app uses multiple REST APIs to add a new category or retrieve a list of categories of expenses and income.

First web application /csp/coffee

I inherited /csp/coffee from module.xml in iris-fullstack-template.

Second web application /csp/budget

For this project, I created a swagger file called "budget.json.