#055 • 13 January 2026

Contents of this Newsletter

☑

How to Evaluate Testing Tools in Under 6 Minutes

⏰ : 4 min

✅

Quash Tests Your Apps

⏰ : 4 min

🔔

What We Shipped Last Week

⏰ : 2 min

📓

Blog Picks

⏰ : 10 min

🗞️

Weekly AI News

⏰ : 5 min

MAIN STORY

AI In Testing Is Becoming Standard

AI in testing is no longer treated as an experiment or a future bet. It is increasingly assumed. Teams are no longer asking whether they should use AI in their QA workflows. They are trying to understand which tools actually work once the demo ends and the app starts changing.

That tension explains why the market feels noisy right now. Adoption is accelerating faster than judgment.

The Shift That Is Already Underway

There are two signals that explain why this moment feels different from previous AI waves in testing.

AI adoption in testing is accelerating rapidly

Over the past year, usage of AI powered testing tools has roughly doubled. Today, an estimated 60 to 80 percent of organizations use AI in some part of their QA workflow. Industry reports such as G2’s Winter 2026 evaluations show AI capabilities moving from secondary features to primary decision drivers.

This shift is not about replacing automation or eliminating test engineers. It is about acknowledging that script heavy approaches struggle to survive modern release cycles. Applications change frequently, environments behave unpredictably and the cost of maintaining brittle automation compounds over time.

Agentic and autonomous testing is emerging

The industry is starting to move beyond AI assisted testing tools that generate scripts or suggest test cases. A new class of systems is beginning to focus on execution itself. Agentic testing refers to systems that can understand intent, plan steps, execute flows and react to what actually happens on the device or inside the app.

Large platforms and services firms are now publicly investing in this direction. Analysts increasingly describe this shift as a move away from traditional automation rather than an extension of it.

Generating tests is relatively easy. Executing them reliably in real environments and adapting when things change is where most systems break down.

When this kind of shift happens, evaluation frameworks tend to lag behind.

What We Are Learning From Real Evaluations

Today, nearly every testing tool describes itself as AI powered or agentic. The terminology sounds similar but meaningful differences are difficult to spot until weeks after adoption.

Here is a practical lens that teams can reuse when evaluating AI testing tools.

Authentication realism

Can the system reliably get through real login flows without constant supervision? This includes OTP handling, delayed SMS delivery, session expiration and inconsistent network conditions. If authentication appears to work perfectly in demos without explanation, it is usually being simplified or skipped. Authentication is the first real world filter, and it quietly eliminates many tools.

Tolerance to UI change

When text changes, layouts shift or banners appear, does the system re understand the screen or does it repeatedly retry fragile selectors? Many tools that claim to be AI driven still collapse into traditional automation when the UI changes in minor ways. This is often where AI claims start to weaken.

Device and environment handling

Mobile testing does not happen on a single device profile. Screen sizes, operating system versions, performance constraints and OEM behavior vary widely. When something goes wrong, does execution degrade gracefully or fail outright? Tools that assume consistency across devices struggle to scale beyond ideal conditions.

Failure clarity

When a test fails, can you understand why within thirty seconds? Clear logs, screenshots, execution steps and contextual reasoning matter more than pass or fail counts. Debug time has always been the real cost of testing, even when dashboards look green.

Setup and adoption cost

How long does it take to run the first meaningful test? If setup requires extensive SDK work, permissions, infrastructure changes or custom languages, adoption rarely fails immediately. It fades slowly as teams lose momentum.

Regression and maintenance over time

Does the system reduce flaky failures and maintenance effort as usage grows, or does it simply replace manual work with AI supervision? After the first month, this is usually the metric that determines whether a tool stays or quietly gets sidelined.

Where The Industry Is Heading Next

What is notable is that even established platforms are starting to acknowledge this shift. In the words of TestMu AI, formerly LambdaTest, their recent rebrand messaging emphasizes that traditional testing models no longer scale and reflects a broader market reality rather than a marketing trend. The industry is rejecting approaches that depend on rigid scripts and fragile assumptions in environments that change constantly.

As teams move into 2026, a few things feel clear. AI in testing is here to stay and agentic execution is becoming an expectation rather than an edge. The real advantage now lies in knowing how to evaluate tools beyond surface level claims.

Teams that develop this judgment will move faster with fewer reversals. The rest will look impressive until their tools meet production.

Your Apps, We Test

Each week, we put our execution engine to work on real apps.

This week, we put our execution engine to work on Gullak, the digital gold savings and investment app. Using a natural language prompt, we asked Quash to open the app, tap on the live gold price indicator and validate the Price Trends view. Quash verified that the Gold tab was selected and then systematically switched between the 6M, 1Y, 3Y, 5Y and Max filters, confirming after each selection that the chart and performance percentage updated correctly.

After validating the filters, Quash enabled the gold price alert toggle, set a price drop alert, and confirmed that the alert was successfully created. This run validated both interactive data visualisation changes and alert setup flows — two areas that are often prone to subtle UI or state issues.

Want us to test your app next? Drop us a mail at hello@quashbugs.com

What Did We Ship?

Suites Preview Panel

v1.1.6 | January 13, 2026

This week, we shipped the Suites Preview Panel, making it easier to inspect individual tasks inside a suite before and during execution.

You can now preview a specific task directly from the suite view, displaying the device screen right alongside the task. This gives you immediate visual context into what the task does, how the app looks at that step and where it fits in the overall flow.

The preview panel helps teams quickly understand, validate, and debug suite setups without running the entire suite end to end. It’s especially useful when reviewing longer flows, checking task ordering or making small edits with confidence.

Check out our Recent Blogs

News

This Week In AI

Meta Launches “Meta Compute” to Build Gigawatt-Scale AI Infrastructure

Meta announced a new AI infrastructure initiative called Meta Compute, focused on building and operating massive, gigawatt-scale data center capacity to support training and deployment of frontier AI models. The effort includes expanding Meta-owned compute, long-term power planning, and tighter integration between models, infrastructure, and products. This move signals Meta’s intent to control more of its AI stack end-to-end, as access to power and compute increasingly defines who can compete at the frontier.

Apple Taps Google’s Gemini to Power Siri’s Long-Delayed AI Overhaul

Apple reportedly chose Google’s Gemini models to power a major upgrade to Siri, marking a significant strategic shift away from relying purely on internal AI capabilities. The decision comes after delays in Apple’s own generative AI roadmap and reflects urgency to stay competitive as AI assistants become core user interfaces. If finalized, this would place Google’s models deep inside Apple’s ecosystem, impacting billions of devices.

Anthropic Launches Cowork, a Desktop AI Agent Built on Claude

Anthropic introduced Cowork, a general-purpose AI agent integrated into the Claude Desktop macOS app that extends Claude’s capabilities beyond chat and coding to everyday knowledge work. Building on Claude Code, Cowork lets users grant the AI access to a folder on their computer so it can read, create, edit, and organize files autonomously from drafting reports to organizing downloads or generating spreadsheets — all without coding. It’s being released as a research preview for Claude Max subscribers with plans to expand to more users as it evolves.

All Newsletter Issues | Security | Contact Us | Blogs

AI Testing Is Becoming Standard & Here’s How We Judge Tools That Actually Work 🧑🏻‍⚖️

Platform

Solutions

By role

By use-case

Resources

Comparison

Company

Platform

Solutions

By role

By use-case

Resources

Comparison

Company

Get Quash

Platform

Solutions

By role

By use-case

Resources

Comparison

Company

Get Quash