Systems | Development | Analytics | API | Testing

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.

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.

Generative AI has completely changed the landscape of enterprise automation, knowledge work and operational efficiency. In 2026, the question is no longer whether these models can perform complex tasks, but whether they can do so reliably enough for mission-critical systems. Despite the availability of sophisticated models and expansive context windows, technology leaders continue to face frustration. Organizations struggle to produce consistent and repeatable results.

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.

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.

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.

The conversation around AI validation has rapidly outgrown simple prompt engineering and single-turn model checks. While the industry spent the last few years establishing baseline protocols for individual AI agent testing, enterprise automation has already advanced to the next engineering frontier: the Multi-Agent System (MAS).