Systems | Development | Analytics | API | Testing

If you're running a customer-facing AI agent on Temporal, you've probably seen this: a user refreshes the tab mid-response. When the page comes back, the response resumes, but anything they were about to send back (an interruption, a tool approval) is gone. Temporal keeps the workflow itself crash-proof, but getting its output to a browser, and taking anything back from the browser into the workflow, has always meant building it yourself.

Picture a software testing failure at your company. The consequences range from money lost on weeks of rework to life-threatening catastrophe. These are the errors that keep engineers, their directors, and executives up at night. At some point, those risks came to life in the real world and pushed software teams to develop rigorous testing methodologies. But for some reason, we don’t think about data the same way. We think of it as reliable, durable, and highly unlikely to cause a big issue.

As many other data teams do, we have a dedicated Slack channel (#data) where the whole company can ask data-related questions that get picked up by a designated, weekly-rotated data analyst (data mage). It can become tiresome quickly as the questions are fairly repetitive and it takes away precious time from other high-impact tasks. The team built clean, well-documented marts and reports in Metabase that are used regularly, but these were still missing something that makes reporting truly self-service.

Like many other countries, India has made moves to protect consumers’ data. Comparable to the European Union’s General Data Protection Regulation (GDPR), India’s Digital Personal Data Protection Act (DPDPA) establishes new, higher standards for data privacy, timely breach notification, and consent management.

Cyber insurance has become a standard line item in enterprise risk management, and for good reason. The financial consequences of a significant cyber event, whether a ransomware attack that halts operations for weeks or a data breach that triggers regulatory scrutiny and third-party liability, can far exceed what any operational budget was sized to absorb. Insurance exists to handle that tail. Most organizations recognize this benefit and carry a policy.

The new Konnect Dev Portal Toolkit extension for Visual Studio Code gives you a live, portal-accurate preview of your content right beside the file you are editing. You watch a page take shape exactly as it will appear to your developers, as you type, without ever leaving your local editor.

*You wouldn't expose a REST API without a gateway. What about Kafka?* You would never expose a REST API without an API gateway in front of it. Authentication, rate limiting, observability, access control — these aren't optional extras. They're mandatory, and we've spent the last decade building the API gateway pattern to solve exactly this problem. So here's the question worth sitting with: would you expose Kafka or any event stream without a gateway?

If you've been following the agentic AI wave, you've probably noticed that the conversation tends to center on the agents themselves: which LLM to use, which orchestration framework to pick, which use cases to tackle first. But a growing body of analyst research is pointing to a different bottleneck, one that's hiding in plain sight: integration. Forrester's David Mooter argues that integration must sit at the center of your AI strategy — not as plumbing, but as a strategic capability.

When a PropTech product struggles, the issue usually isn’t the code. A skilled team can build the wrong thing well: a listing platform on a data model that buckles when the second MLS feed arrives, a CRM that ships to spec while agents quietly stop using it, an MVP that investors, agents, and end users each expected to do something different. That gap between what the business assumes, what users need, and what the architecture can support is what we mean by PropTech product misalignment.

In the first part of this series, I argued that discernment, the ability to recognise when an AI-generated answer is wrong, is becoming one of the most valuable capabilities inside an organisation. The question this piece addresses is simpler and harder: who is actually being given the opportunity to develop it? The AI opportunity gap is real. It is not primarily a gap in access to tools. It is a gap in permission. And I believe that gap starts earlier than most leaders realise, often in school.