Systems | Development | Analytics | API | Testing

We are launching with this post a new series of blog articles and LinkedIn posts titled "Features Sitting Idle". In this series, we explore key features of OctoPerf that are either misused, misunderstood, or simply unknown to our users. It's time to shine a light on these hidden gems, features that are already there, ready to become a central part of how you test. This is probably the most common situation after a load test.

This is Part 2 of the AI Software Factory series. In Part 1, we established that the Agile methodology is buckling under the weight of “elastic code.” When AI agents can generate functionality in seconds, two-week sprints and manual task management become organizational bottlenecks. We introduced the concept of the AI Software Factory: a shift from managing human tasks to managing business intent through a “Funnel of Increasing Trust.” But a factory requires infrastructure.

We’ve been building an AI agent that can take a production bug, find the root cause in captured traffic, write a fix, and validate it before a human reviews it. We call it Agent Factory. Last week we ran it on ourselves, against a real bug in our own production service. The first thing we did was get the workflow wrong.

Healthcare IT systems operate in environments where even a minor failure can create clinical risks and regulatory consequences. Modern healthcare ecosystems depend on microservices, legacy databases, and multiple third-party integrations, creating vulnerabilities that traditional testing approaches often fail to detect. Engineering teams are managing sensitive information at high-velocity, ensuring that a failed connection or a missed rule never gets in the way of patient treatment.

Credit used to be a destination. You went to a bank, filled out forms, waited days, sometimes weeks, and hoped for approval. That model is quietly disappearing. Today, credit shows up exactly where you need it. While shopping online. While booking logistics. Even while managing business cash flow inside a SaaS dashboard. No redirects. No friction. No traditional loan journey. This shift is what we call Embedded Lending. It is not just a feature.

Mobile-first businesses often enter the market confident in their app’s speed, but the reality is that many overestimate their performance – and pay for it through user churn and lost revenue. With 5.78 billion unique mobile users worldwide as of October 2025, representing 70.1% of the global population, the pressure to deliver a fast, reliable experience is immense.

In an API-driven setup, a gateway often sits between clients and backend services: it can validate input, aggregate upstream responses, and give you one place to observe traffic. Koa is a strong fit for that role. Its core stays small, async/await is first-class, and middleware composes in a predictable stack. In this article, you will build a compact API gateway with Koa that: You will also wire up AppSignal for the Node.js stack.

While AI accelerates development velocity by a factor of ten, a critical consequence remains: testing hasn’t kept pace. According to SmartBear research, 70% of software professionals report that their application quality has already degraded due to AI-accelerated development. Even more concerning, 60% have experienced quality issues in the past year as development velocity outstrips testing capacity.

Since its debut in June 2023, SwiftData has fundamentally changed how Apple developers approach persistence. Devs the world over love it for its versatility, its declarative ease and its powerful querying system. But if you’re new, SwiftData can take some getting used to. Failures can feel less transparent and relationships can play out differently to how you might expect. So in this tutorial we’ll show you how SwiftData works and how to.

“Garbage in, garbage out.” We are not the ones who said this, George Fuechsel did. But when we are talking about AI today, it is hard not to repeat it. We spend a lot of time discussing what AI can do, the outputs, the predictions, the impact it can create. Much less attention goes to what is actually going into these systems.