Systems | Development | Analytics | API | Testing

Most analytics programs fail the ROI test for one simple reason: they measure dashboard output, not workflow impact. A team can ship reports, charts, and alerts, yet still miss the real question: does the analytics change what busy people do next? That is the core issue for embedded analytics ROI. How do we measure whether embedded analytics actually delivers business value for busy end users, frontline teams, and executives?

Generative AI systems do not fail because models are weak. They fail because architectures are incomplete. Once organizations accept that prompts cannot guarantee reliability, a new challenge emerges: how to design systems that systematically convert successful AI behavior into repeatable, governable, and auditable workflows.

You are planning a trip with an AI assistant on your laptop. You are chatting with the agent, and as you progress it is dropping pins on a map, building a day-by-day itinerary, adding up a budget, and streaming its reasoning as it goes. The state of your interactive session is a combination of the chat history, the synthetic UI constructed by the agent during that process, and structured state, the itinerary, arising from the decisions you each make.

Not every test automation problem looks the same. A team maintaining complex desktop applications in a controlled financial services environment has different automation needs than a team shipping web and mobile updates every two weeks. The application, the environment, and the people creating tests all shape what “good automation” has to do.

Managed Python User-Defined Functions (UDFs) in BigQuery let you define and execute Python scalar functions directly within your BigQuery SQL queries.

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

The problem. Predictive ML pipelines that maintain separate batch and streaming code paths for the same features carry training-serving skew, the gap between the features a model was trained on and the features it sees at inference time. Skew silently degrades model accuracy and doubles infrastructure cost. The recommendation. Adopt a unified streaming (kappa) architecture.

Hyper-personalization in 2026 is the ability to act on a user's current intent within the current session, using signals from across the journey. Batch customer data platforms (CDPs) can't do this. They can't capture intent as it forms, can't hold session state, and can't activate inside the intent window.

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.

We audited our skills library a few months ago and found twelve dashboards hiding in it. Not dashboards. Skills that built dashboards. Someone needed a view of some data, asked Claude to put it together, got a long HTML page out of it, and then wrapped the whole thing in a skill so others could run it again. Twelve times over, by different people, for different questions.