Systems | Development | Analytics | API | Testing

The original promise of AI was that it would write most of the code for us. In reality, we’re not there yet. So where can AI meaningfully improve developer productivity today? In this post, we look at how AI powers development productivity across the SDLC, practical tools to use and frameworks for overcoming AI operationalization bottlenecks.

Over the past few weeks, an unusual experiment has been unfolding quietly and then very publicly.

A global leader in entertainment, gaming, and hospitality partnered with WSO2 to take the organization’s first step to becoming an agentic enterprise by building an intelligent solution that would transform how support teams operate. The solution, a virtual support engineer, automated a manual issue resolution process and reduced resolution times from 2 hours to 1 minute, helping to provide a better support experience for both customers and internal operations teams alike.

Gartner has named Qlik a Leader in the 2026 Gartner Magic Quadrant for Augmented Data Quality Solutions, our seventh time being recognized as a Leader in this Magic Quadrant. As AI becomes operational, data quality matters more than ever. We’re past the phase where AI just produces outputs. AI is starting to initiate, route, and act across real workflows.

Creating AI writing tools is messier than you’d think. You start with this grand vision of an assistant that actually helps people write better, not just spits out generic text. Then reality hits. Models hallucinate. Users have wildly different needs. And suddenly you’re facing questions about responsibility, accuracy, and whether you’re building something genuinely useful or just another gimmick.

Today, we’re thrilled to announce that Koyeb has entered into a definitive agreement to join Mistral AI to advance cutting-edge AI infrastructure. Koyeb will bring its platform, technology, and team to accelerate Mistral Compute offering. Compute is designed to provide leading teams across the globe the same state-of-the-art infrastructure Mistral AI uses to build, run, and scale frontier models and AI software.

Last week we publicly released Kai, our in-platform AI assistant, a data engineering agent that can build integrations, write transformations, debug failures, and document your entire project. I'm extremely proud of the team and what we've delivered. Yes, everyone has an AI assistant now. But most are chat wrappers that look great in scripted demos and fall apart with real work.

AI-assisted software development makes building new features to help our customers an exercise in speed. Rainforest has a deep culture of experimentation and iteration, and we’re actively exploring how AI can help us work smarter. At our core, we aren’t just building an AI test generation tool; we are constantly experimenting with how AI can make our own development cycles leaner, faster, and more intuitive. That includes experimenting with AI-assisted software development.