Systems | Development | Analytics | API | Testing

Enterprises are under pressure to deliver software faster without sacrificing trust. AI generated code, continuous delivery, and increasingly agentic systems are accelerating change faster than traditional quality practices can validate it. For enterprises running multi-layered tech stacks, weekslong regression cycles and performance issues that are discovered by customers in production are symptoms of a behind-the-scenes quality model that was built for a slower era.

Last week we introduced AI Transport v0.2.0 and made one idea the centre of the design: the session is the channel. Every input, output, and lifecycle event for an AI conversation is just a message published to an Ably channel, which is what makes a session durable, multi-party, and resumable. In v0.3.0, we added first-class support for presence and LiveObjects to AI sessions, allowing you and your agent to see who's online and update shared state in real time.

Your dashboards have a new reader, and it doesn’t have judgment.

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.

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.

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?