Continuous integration (CI) is the process of integrating changes from multiple contributors to create a single software project. A key component for a smooth CI pipeline is testing. Tests prove that the code does exactly what it says on the tin and that it’s safe to merge the code into the central repository. Tests also anticipate edge cases and ensure that the code handles such cases in a deterministic manner.

In today’s ever-evolving digital landscape, data is the new gold. And with the advent of generative AI in cloud services, businesses have a unique chance to transform their data in powerful new ways.

Over the last several months, AI has been everywhere in the technology space and far beyond. Since it directly affects the tech ecosystem, however, it’s no surprise that developers have harnessed artificial intelligence to create tools that boost productivity and enhance workflows. Artificial intelligence is essentially a computer’s ability to perform tasks at the same level (and often beyond) as intelligent beings.

As your applications grow, your teams may be faced with managing a complex, expanding mesh of potentially thousands of loosely connected APIs—each one a new point of failure that can be difficult to track and patch. API sprawl comes naturally in rapidly expanding, distributed applications, and the difficulty of maintaining centralized knowledge and toolsets for your APIs creates friction when teams need to leverage APIs they don’t own.

Today’s modern applications are made up of thousands of loosely connected private and publicly exposed APIs, each serving a specific function. This dynamic API landscape, in combination with the decentralized nature of microservice development, can be overwhelmingly challenging to manage—let alone govern or secure adequately. API sprawl is often created as a result, leading to fragmented or nonexistent internal API documentation, knowledge bases, and toolsets.