When Air India crashed at Ahmedabad airport, there was grief. Outrage. Endless debates about how it could have been prevented.
But as time passed, people moved on. Memories are short.
Software is no different. Every production failure is like a plane crash. It causes chaos, sparks urgent conversations, and everyone promises, “This will never happen again.” Then slowly, it is forgotten until the next crash.
The State of QE Today
In today’s engineering world, with tools like Copilot and Cursor, quality engineering (QE) is at a crossroads. Some teams are rethinking their entire approach. Others are frozen, unsure where to begin. Confusion and chaos dominate, fueling endless discussions and experiments, but very little clarity.
The need for bold, clear solutions has never been greater.
The Black Box Lesson
Aviation is the safest form of travel today, not because planes never fail, but because every failure is studied deeply.
When a plane goes down, investigators don’t just fix the immediate issue. They analyze every piece of data from the black box to ensure the same mistake never happens again anywhere in the world.
This creates a cycle of continuous learning and improvement. As a result, aviation gets safer every single day.
Software has no such black box.
When production fails, teams scramble to fix the issue. Meetings are held. Documents are written. Promises are made. Then everyone moves on.
There is no lasting memory. No system to prevent the same failure from happening again. The same problems resurface release after release.
Imagine if airlines worked this way, relying on memory and hope instead of data. It would be chaos. Yet this is exactly how most software teams operate today.
Layers Upon Layers: Why Test Automation Feels Broken
To understand why testing feels so complex today, we need to look at how automation itself evolved.
Every wave brought new tools, new frameworks, and new possibilities. But instead of simplifying, these layers are stacked on top of each other, making test automation harder to manage, not easier.
1990s – The Dawn of Web and Desktop Automation
1992 – Lynx, a text-mode browser, allowed scripted navigation with keystrokes. 1995–1997 – Netscape Navigator introduced remote commands to open and control browsers. 1998 – Mercury launched Astra QuickTest, later evolving into HP UFT, one of the first mainstream GUI testing tools.
This era introduced the basic idea of interacting with software programmatically. But tools were fragile and tightly bound to specific environments.
2004 to 2016 – The Selenium and Mobile Revolution
2004 – Selenium disrupted web testing with an open-source, cross-browser framework. 2009 – Selenium merged with WebDriver, creating a standardized interface for browser automation. 2011 to 2017 – PhantomJS brought headless browser testing before Chrome had native headless support. 2012 – Appium launched, bringing mobile automation to iOS and Android through system-level accessibility APIs. This was a turning point as suddenly teams needed to test web, desktop, and mobile all at once.
This was also when testing started to fragment. Browser, mobile, and backend systems became separate silos, each requiring unique tools and expertise.
2017 to 2020 – The Headless Chrome Breakthrough
2017 – Chrome announced headless mode and introduced Puppeteer, enabling direct automation via Chrome DevTools Protocol (CDP). 2018 – Puppeteer 1.0 shipped, providing a faster, more reliable foundation for browser automation.
For the first time, teams could automate browsers without heavy emulation layers like PhantomJS.
But while headless Chrome solved some problems, it also expanded the surface area of testing, leading to even more scripts, configurations, and maintenance work.
2014 to 2024 – The Rise of API and Contract Testing
As microservices exploded, API testing became critical. It was no longer enough to test just the UI. Teams needed to validate the behavior of dozens, sometimes hundreds, of interconnected services.
In 2014, PACT was introduced, giving rise to contract testing as a formal practice. It allowed teams to define and verify the “contracts” between services, ensuring that one system’s expectations matched another’s.
This was a big leap forward. For the first time, distributed teams could catch breaking changes before they reached production.
But there was a catch.
As systems grew, so did the number of APIs and contracts. Suddenly, teams were dealing with thousands of tests. Managing them became a full-time job.
For companies like JPMC, Capital One, or Barclays, this was feasible. They had practically infinite resources and dedicated teams for every layer of testing, including UI, API, performance, and more.
For everyone else, it was overwhelming. The testing cost skyrocketed, while ROI often felt like little more than a line in the CIO’s next blog post.
Instead of simplifying, this wave added another layer of complexity to an already overburdened testing strategy.
2020 to 2024 – The Playwright Era
2020 – Former Puppeteer engineers at Microsoft launched Playwright, focusing on cross-browser automation with a cleaner API. 2021 to 2024 – Playwright exploded in popularity for its multi-language support and stability across Chrome, Firefox, WebKit, and Edge.
Playwright abstracted away many of the browser’s rough edges, making testing accessible to more teams.
But this simplicity came at a cost: Hidden complexity behind the scenes. A second layer of network hops. State drift and synchronization issues at scale.
2025 – Back to the Metal
2025 – AI-driven automation platforms like Browser-Use began dropping Playwright entirely, speaking directly to CDP for speed, reliability, and control.
This was a significant philosophical shift. Instead of adding more abstraction, teams began removing layers to get closer to the source of truth.
Lesson: Every layer you add hides detail and adds fragility. Over time, these layers make automation feel like a house of cards.
The AI Phase: More of the Same
Then came AI. The industry reaction was predictable. Bolt AI onto legacy workflows.
AI was marketed as a transformation, but in many cases, it simply automated the noise. The core problem remained. Too many tests, too much complexity, too little impact.
I have spent months speaking with CTOs, QE leaders, and engineering teams, and the consensus is clear. AI alone will not fix the foundations. Layering it on top of broken systems just creates a flashy, ineffective facade.
The API First AI Future: Where Real Testing Lives
The future of testing will not be driven by UI clicks or endless scripts. It will be driven by APIs.
Every modern product is powered by a network of services. Whether you’re booking a ride, checking your bank balance, or asking an AI chatbot a question, the experience depends on APIs quietly working behind the scenes.
Think about it:
How do you truly test a chat interface? It’s not just about what appears on the screen, but about the API calls that interpret the user’s message, retrieve the right data, and deliver a response in real time.
How do you validate a food delivery app? The flow crosses multiple systems, payments, restaurants, drivers, inventory, and notifications. All of that lives at the API layer.
Even the most advanced AI assistants rely on APIs to connect models, context, and external data sources.
As technology evolves, UIs will change, but APIs will remain the backbone. The teams that embrace API-driven testing will build faster, break less, and adapt to whatever comes next.
This is why Loadmill focuses on APIs first. It’s the only way to keep up with the complexity of modern systems and deliver meaningful, reliable testing at scale.
At Loadmill, we believe this moment calls for reinvention, not incremental tweaks.
We have spent the past year redesigning our approach from the ground up. Our goal is not to add more noise but to eliminate it entirely.
Our guiding principle:
Testing should simplify, not multiply. It should capture learnings, not just detect failures. And AI should be a strategic advantage, not a checkbox feature.
Hybrid Autonomous Testing: A New Paradigm
This vision led to Hybrid Autonomous Testing, a fundamentally new approach to automation and quality.
With Loadmill, teams can balance tests across their entire stack, from front-end user experiences to deep backend workflows, without writing a single line of code.
This is not just an incremental improvement. It is a step change:
10x faster execution, 75% boost in productivity, Lower maintenance costs, and fewer late-night firefights
About 10% of the customers we speak with have managed to get to a hybrid testing model on their own. But it required an extraordinary amount of time, energy, and expertise. The future of Loadmill is about making hybrid testing a no-brainer, accessible to every team without the uphill struggle.
Most importantly, Hybrid Autonomous Testing acts like a black box for software. Every failure becomes a lesson that strengthens the system.
From Crashes to Control
Runway systems keep planes safe before takeoff. Testing should do the same for software.
Today, most teams are reactive, fixing problems after users are impacted. With leaner QE teams and growing complexity, this is no longer sustainable.
AI, when applied correctly, breaks the cycle. It helps companies release faster, reduce risk, and prevent disasters before they hit production.
Write to us to know more about Hybrid Autonomous Testing [email protected]