Systems | Development | Analytics | API | Testing

Until recently, we extensively used HashiCorp's CDK for Terraform. Then they announced the end of its development. We didn't want to spend months migrating to a new ecosystem, but we needed a replacement. In the Infrastructure Team at Ably, we like Go. I wondered if there was an opportunity for something simpler.

Most AI content assumes your data is in the cloud. But for a meaningful segment of enterprises, cloud-only AI tools block them at the pass. For regulated industries like manufacturing and healthcare, data residency requirements, compliance mandates, security policies, and simple operational reality mean sensitive data must remain on-premises.

Most QA teams already track the basic metrics: how many tests ran, how many passed, how much coverage exists, and how many defects turned up. Those numbers still matter, and engineering leaders will keep asking for them. The real challenge is turning those numbers into decisions, and that gets harder as AI-assisted development speeds up the volume, frequency, and complexity of software change.

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.

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.

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.

*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?