Systems | Development | Analytics | API | Testing

There is a moment most travel platform teams are now experiencing for the first time. The AI-powered booking assistant is live. The conversational search feature is generating rave reviews from product managers. The personalised itinerary engine is pulling data from a dozen microservices in real time. And then peak season arrives. Response times climb. The AI layer starts queuing. The booking funnel drops. Users abandon. And the engineering team realises something uncomfortable.

A strategic framework for using AI-powered quality engineering to accelerate delivery without sacrificing regulatory requirements.

Are you still relying on manual testing for your Dynamics 365 Finance & Operations (D365 F&O) environment? In today’s fast-paced digital landscape, where Microsoft rolls out frequent updates and business needs evolve rapidly, manual testing is no longer just inefficient. It’s a strategic risk.

Performance testing without observability is like diagnosing a sick patient using only a thermometer. You get one number. You miss everything that matters. Observability-driven performance testing combines load testing with metrics, logs and distributed tracing to identify not just when performance degrades, but exactly why.

Your dashboards are lying to you, not through bad data, but through incomplete data. They show you what you told them to watch. They cannot show you what you did not know to ask. Telemetry-driven performance engineering uses metrics, logs, traces and profiling to detect and diagnose issues that traditional dashboards cannot capture. The failures that hurt most are not the ones you predicted; they are the ones your monitoring was never designed to catch.

Quick Answer Open Banking performance is as critical as security because slow API responses lead to transaction failures, user abandonment, and loss of trust. To ensure success, banks must optimise latency across API chains, monitor p95/p99 metrics, and design systems for speed from the start. Imagine a digital banking customer. Let’s call him David, standing at a crowded airport terminal. He’s trying to book a last-minute flight through a travel aggregator.

A routine Dynamics 365 Finance & Operations evergreen update introduced “Ledger Posting Logic Enhancements.” No alarms were raised. The system ran smoothly. But behind the scenes, something changed. Revenue postings—critical to how the business understands its performance—started flowing into incorrect accounts and dimensions due to an interaction with custom logic. No crashes. No errors. Just silent misclassification.

There’s a moment every cloud team eventually faces. Dashboards look green. CPU is stable. Memory isn’t spiking. Auto-scaling is configured. And yet, users say the system feels slow. Welcome to cloud-native performance engineering. After working across environments hosted on Amazon Web Services, Microsoft Azure and Google Cloud Platform, I’ve realised something important: Cloud doesn’t eliminate performance problems. It simply changes their shape.

In the traditional banking world, “Quality” was defined by the perimeter. If the core banking system was stable and the customer portal didn’t crash, QA had done its job. We operated in a world of controlled environments. We owned the code, the server and the user experience. Then came Open Banking. Suddenly, the perimeter has vanished. Today, a bank’s value is determined by how well it communicates with external fintechs, payment aggregators and retail ecosystems.

I built AI Agents to handle my test automation, and they immediately tried to unionise. I’ve been doing test automation for long enough that I can spot a flaky test from far away, with the sixth sense of a person who has been personally betrayed by “WaitForTimeout(5000)” more times than they’ll admit in polite company.