Systems | Development | Analytics | API | Testing

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.

The world of Salesforce is changing fast and so is the job of Quality Assurance (QA). It used to be that QA was just the team that checked if a new feature worked correctly right before it went live. Think of them as the final proofreaders. But with powerful new tools like Agentforce (Salesforce’s smart agent system) and a huge focus on Data 360 (getting all your data in one trusted place), QA has become something much more important: the main way your company avoids major problems.

There’s a particular kind of exhaustion that comes with traditional performance testing. You spend weeks building perfect load scenarios, run them overnight and wake up to a wall of red in your monitoring dashboard. Half your day disappears into log files, trying to piece together what went wrong. And just when you think you’ve got it right, a minor UI update breaks everything and you’re back to square one. If this sounds familiar, you’re not alone.

The digital economy has upgraded from simple transactional interactions with users. Now consumers demand the Autonomous Digital Experience (ADE) – the customer journey is driven by predictive, self-learning systems, which is essential for competitive success. This is driven by Predictive Personalisation, which uses machine learning to predict personalised affinity and intent of user actions, delivering personalised content, products and messages in real-time.