LocalStack this week revealed it has acquired WonderTwin AI, a provider of an emulator of software-as-a-service (SaaS) applications that is used to build custom applications for those platforms.

Colin Neagle, vice president of marketing for LocalStack, said the emulators WonderTwin AI has developed will be integrated into the company’s namesake emulation platform that application development teams currently rely on to emulate cloud services provided by Amazon Web Services (AWS).

LocalStack and WonderTwin AI make it possible for application developers working on a local machine to build applications that are designed to be deployed on some type of external cloud platform using a local sandbox to test and validate integrations without having to connect to a service or build against a live application programming interface (API). That issue has been especially critical in an era where more code will soon be generated by AI coding agents that may for one reason or another circumvent the guardrails that might have been put in place to limit access to sensitive data.

The overall goal is to make it possible for application developers to experiment and iterate without waiting for access to shared environments that might not provide the level of interactive feedback required or increase costs as, for example, live APIs are invoked.

In general, application developers still prefer to build software on a local machine that they have more control over. LocalStack was created to provide a local instance of an AWS environment that enables developers to build software that is destined to run in a cloud environment. The goal is to reduce the number of instances where code that runs on a local machine doesn’t actually run as intended in the cloud.

It’s not clear how much code is developed first on a notebook, desktop PC or local server, but in the age of artificial intelligence (AI) the amount of code being generated by individual developers is increasing exponentially. Every time code has to be sent back for revision, it slows the overall pace of application development.

The speed at which DevOps teams make that adjustment will naturally vary from one team to another, but the sooner everyone realizes that a piece of AI-generated code might not run as intended in a cloud computing environment, the less stress there is likely to be for all concerned. The challenge and the opportunity, of course, is identifying those points of friction before the amount of code being created by AI agents might soon become too overwhelming to effectively deploy and manage.

Ultimately, DevOps teams need to meet application developers where they proverbially are rather than forcing them all to develop applications in the cloud. The simple truth is many developers, if inclined, will simply evade any cloud mandate using shadow IT resources they are well able to deploy and manage. The challenge, as always, is finding a way to centrally manage development environments in a way they will actually embrace versus passively resist.