Systems | Development | Analytics | API | Testing

In this guest post, Intellyx Principal Analyst Jason English explores what it takes to level up quality engineering in the age of agentic AI, and why visibility, context, and governance are the keys to getting there. One day in an agentic developer’s life: Developer “CodeBud agent, create me a suite of test cases to validate the feature you just built.” CodeBud Done. Test suite created.

In February, two CNBC journalists built a replica of Monday.com’s interface using Claude Code with no prior developer experience and less than $20 in credits. Software stocks at the time were already wobbly, with investors fearing that AI could erode seat-based pricing and undercut proprietary UI. To some, the CNBC news confirmed the speculation.

You've built an AI chat app on the Vercel AI SDK. It works in development. The model responds, the stream comes through, and the UI updates cleanly. Then you ship to production, and the transport layer starts showing its edges. Most of these failures are quiet: things that work in demos and break in ways that are hard to pin down until you know where to look. They share a common cause: DefaultChatTransport is built for HTTP, and HTTP has structural properties that some production requirements exceed.

As API ecosystems grow, developers and platform teams often work in separate environments. Platform teams manage APIs, gateways, and governance centrally, while developers recreate those configurations locally for testing and debugging. Over time, this can lead to configuration drift, inconsistent workflows, and security gaps. The release introduces our first native Kong Konnect integration, allowing developers to discover, import, and test Gateway configurations directly from Konnect.

Is agentic AI testing more hype than substance? Clarifying what this category of software testing actually is, how it works in practice, and where it still falls short.

The React Native New Architecture is no longer optional. From React Native 0.82 onwards it is mandatory, the legacy architecture is gone, and every team still running it is now carrying technical debt that will need to be resolved. For most teams, the migration conversation quickly turns to tooling. Does our CI/CD pipeline still work? Does our crash reporter still integrate correctly? Do our analytics tools need updating?

This recognition reflects the critical role automated data movement continues to play in helping organizations unify data, improve decision-making, and prepare for the future of AI.