Systems | Development | Analytics | API | Testing

Citizens today compare government digital services to the best experiences they have in the private sector. A missed benefit payment. A tax portal that crashes during filing season. A license renewal that takes weeks instead of minutes. Today, software failures aren’t just IT problems, they’re public trust problems, and many state and local agencies are struggling to deliver digital services quickly, effectively, and consistently.

Companies already spend more than $1 trillion a year trying to catch bad code before it ships. AI is making that job a lot more difficult.

Test automation tells you what happened. Release assurance empowers you to decide what to do about it, closing the loop from business intent to production confidence.

Automated data integration enables AI and analytics based on mission-critical operational data stored in IBM Z.

Interoperability means the optionality to adapt to rapidly changing future demands.

A Node.js application can continue starting, accepting traffic, and passing health checks long after the runtime underneath it has become unsupported. That creates a dangerous assumption: In reality, “running” and “supported” are two very different states. A Node.js release is more than a JavaScript executable.

Platform teams want to maximize the value of data in motion, but separate workstreams for stream processing, monitoring, KRaft migrations, and data governance create friction as environments grow. As a result, teams spend more time managing operational overhead and less time building. Today, we’re excited to announce Confluent Platform (CP) 8.3.0, built on Apache Kafka 4.3.0, reinforcing our core capabilities as a data streaming platform.

Enterprise teams depend on automated testing to sustain rapid release cycles, yet scaling quickly reveals a major bottleneck: the maintenance footprint. Consider a financial enterprise running a nightly regression suite of 3,000 scripts. A developer updates the checkout UI, renaming a submit button’s class from-submit to-primary. The core payment logic stays completely untouched, yet 400 test cases fail overnight.

At its simplest, AI debugging automates repetitive coding tasks like searching logs, reading stack traces and comparing sessions. But good AI debugging is a much more challenging concept that relies on focused context, runtime evidence and structured investigation paths. In this post we’ll show you how to debug with AI, not just productively but also responsibly. Let’s get into it.

When was the last time you made a major business decision and were completely certain the data behind it was accurate, complete, and trusted? For most organizations, that certainty is less common than it should be. Qlik and Amazon Quick together solve one of the biggest obstacles to AI adoption: knowing whether you can trust the output.