TL;DR

• Autonomous testing is maturing quickly, but regulated organizations must evaluate platforms through the lens of traceability, auditability, and control.
• Forrester’s Autonomous Testing Platforms Landscape, Q3 2025 shows that the real differentiators now are explainability, risk-based orchestration, and AI governance—not just automation speed.
• Use this checklist to choose a platform that accelerates delivery while protecting oversight.

Download Forrester’s full report for detailed market insights

Rethinking Autonomy for Regulated Teams

With hundreds of tools now promising “AI-driven automation,” sorting true autonomy from clever scripting has become increasingly difficult. This matters even more for regulated teams planning their 2026 quality strategy. Speed is no longer the only concern. Proof, traceability, and controlled execution are now essential.

Forrester’s recent analysis highlights a market shifting from test automation to AI-augmented and agentic systems that generate, maintain, and execute tests under human supervision. The key question for regulated buyers is not whether autonomy will help, but whether the platform provides clear governance around how that autonomy operates.

Use this checklist to evaluate solutions with the guardrails required for safety-critical or compliance-heavy environments.

Core Capabilities Every Autonomous Testing Platform Should Provide

These capabilities form the baseline for operating safely and efficiently in regulated sectors.

Plain-language test authoring and execution
Non-technical reviewers should contribute without adding risk. Natural-language authoring and guardrails make collaboration safe and auditable.

Transparent AI actions
Every generated or changed step must be reviewable. No black-box maintenance. No silent updates.

Evidence management and auditability
Exportable logs, change histories, and evidence packs should support internal and external audits without manual rework.

Role-based control and gated approvals
Automation should accelerate work, but never bypass required compliance workflows.

Adaptive, governed maintenance
Self-healing is useful only when changes are traceable and reversible. Regulated teams need adaptive maintenance under human oversight.

If a platform lacks any of these essentials, it’s not built for environments where documentation and control are mandatory.

Where Advanced Platforms Differentiate

Once the fundamentals are covered, regulated organizations should look at the capabilities that separate mature autonomous solutions from those still catching up.

Intent-based visual and experience validation
Pixel comparison is brittle. Intent-driven validation ensures the interface appears correct, accessible, and compliant across devices and browsers.

Governance dashboards
AI actions, risk coverage, and test triggers should be visible and easy to trace for auditors and managers.

Actionable analytics and reporting
Evidence should turn into insights that support risk management, release approvals, and executive reporting.

Risk-based orchestration
Platforms should prioritize tests based on business criticality, change impact, and historical issues—not just run everything in bulk.

Applying Autonomous Testing in Regulated Workflows

Organizations across healthcare, life sciences, financial services, and other regulated industries are already adopting autonomous testing—but always with governance in place.

In the pharmaceutical sector, EVERSANA INTOUCH takes a hybrid approach, combining Applitools Eyes for Visual AI validation with Applitools Autonomous for intelligent test generation. This end-to-end strategy ensures quality products, supports compliance-ready evidence, reduces maintenance, and provides end-to-end coverage across complex workflows—all while keeping human reviewers in charge. Read the EVERSANA INTOUCH case study.

These hybrid models show how autonomy can increase coverage and speed without loosening control.

Applying the Checklist to Your Evaluation Process

Use this framework when comparing platforms side by side:

• Map your highest-risk business journeys. Focus on areas tied to compliance, customer safety, or financial impact.
• Prioritize transparency. Ensure the platform shows why AI takes each action and allows review before changes go live.
• Assess evidence and governance. Exportable results, audit-ready logs, and approval gates are non-negotiable.
• Evaluate adaptability. Autonomous maintenance should reduce manual effort but still operate inside defined boundaries.

• Reassess regularly. The market is moving fast. Capabilities that seem advanced today will become baseline expectations.

Choosing with Confidence

Autonomous testing is reaching maturity, but regulated organizations need more than speed—they need governance, visibility, and trust. Forrester’s research confirms that platforms built with explainability and risk alignment at the center are the ones best suited for compliance-driven teams.

Use Forrester’s analysis and this checklist to guide your next evaluation and choose an autonomous testing solution that accelerates both delivery and confidence. Download the Autonomous Testing Platforms Landscape, Q3 2025 report.

The post Buyer’s Checklist for Autonomous Testing in Regulated Environments appeared first on AI-Powered End-to-End Testing | Applitools.

Update & TL;DR

This post was written while Forrester’s research on agentic and autonomous testing was still emerging. Since publication, Applitools has been included in The Forrester Wave: Autonomous Testing Platforms, Q4 2025. The perspective outlined below reflects how this shift has since been validated and formalized by independent industry analysts.

• Agentic automation shifts testing from brittle, script-driven execution to intelligent systems that adapt based on change, risk, and context.
• AI augments human intent rather than replacing QA teams, enabling people to focus on quality strategy, governance, and risk decisions.
• This model is increasingly shaping how autonomous testing platforms are evaluated in the market.

Forrester, a leading global research and advisory firm, identified a major turning point in software testing in its Autonomous Testing Platforms Landscape, Q3 2025. The research describes a shift from traditional scripted automation to AI-augmented systems that can learn, adapt, and act under human guidance. This shift signals the rise of agentic automation: intelligent systems that create, run, and optimize tests within defined boundaries.

As delivery cycles compress and complexity grows, quality and engineering leaders are redefining what effective testing means in practice. Agentic automation bridges human intent with machine-driven precision—transforming testing from a reactive maintenance task into a proactive engine for reliability, speed, and continuous improvement.

From Automation to Intelligence

Traditional automation accelerated execution but left teams managing brittle scripts and endless maintenance. AI-augmented testing changes that dynamic. These systems:

• Learn continuously from results and application change.
• Adapt test scope and prioritization based on business risk.
• Optimize coverage while maintaining human oversight.

The result is testing that behaves less like a checklist and more like a self-improving quality partner, one that scales reliability across every release.

The Three Business Values Driving This Shift

Forrester highlights three outcomes motivating investment in more intelligent testing systems:

1. Accelerate Time to Value – AI-driven generation and self-healing shorten feedback loops and reduce maintenance.
2. Reduce Strategic Risk – Risk-based orchestration and built-in governance connect quality metrics directly to business priorities.
3. Democratize Testing – Low-code authoring and natural-language interaction let non-developers participate in quality, closing skill gaps.

Agentic automation brings these together: human-directed intent, machine-driven efficiency, and transparent oversight.

How AI-Augmented Systems Complement Human Expertise

AI in testing works best as augmentation, not replacement. By handling repetitive execution and maintenance, intelligent systems free QA professionals to focus on:

• Defining risk and coverage strategy.
• Establishing governance frameworks that maintain trust.
• Collaborating earlier with product and development teams.

Agentic automation shifts QA leadership from running tests to steering quality outcomes.

The Role of Visual and Experience Validation

Intelligent automation depends on reliable validation signals. Traditional assertions can’t always capture what matters to real users: layout, accessibility, and experience consistency. 

Visual and experience validation fill that gap, giving AI-augmented systems context they can trust. When machines validate what users actually experience, teams gain both speed and confidence—without rigid pixel-level comparison.

Building Toward AI-Augmented Readiness

Forrester describes this as a maturing market: organizations are blending traditional automation with AI capabilities to move toward greater autonomy over time. QA leaders can start by:

1. Stabilizing automation foundations and addressing flakiness.
2. Adopting AI-assisted detection of UI and data changes.
3. Integrating experience-level validation for richer feedback.
4. Connecting quality analytics to business metrics for continuous improvement.

Each step builds the trust and data maturity required for agentic automation to succeed under human orchestration. As adoption increases, these maturity steps align with how leaders in the market are being evaluated on autonomous capabilities.

What QA Leaders Can Do Next

Forward-looking teams are already experimenting with:

• Adaptive execution that prioritizes tests dynamically.
• Governance dashboards linking coverage, risk, and compliance.
• Visual AI that helps systems understand real user impact.

The goal isn’t full autonomy—it’s AI-augmented confidence: testing that’s faster, smarter, and more inclusive across roles. Read the full report now.

The post Agentic Automation: Preparing QA Leaders for the Next Leap in Testing appeared first on AI-Powered End-to-End Testing | Applitools.

Testing has always been about more than just catching bugs. For QA and engineering leaders, it’s about enabling collaboration across teams, keeping pace with rapid release cycles, and maintaining confidence in quality. But traditional approaches often break down when skill gaps, silos, and tool fragmentation get in the way.

Modern testing platforms are changing that—not by replacing testers, but by using AI to bridge technical and non-technical team members, giving everyone a way to contribute to test creation and maintenance.

AI as the “Trail Guide” for Testing

Think of AI as an experienced trail guide: it understands the terrain, spots shortcuts, and helps both experts and first-timers reach their destination faster.

For testing teams, this means:

• Non-technical testers can describe flows in plain language and see them converted into robust test steps.
• Engineers save time on repetitive tasks and focus on complex automation.
• Teams build trust by working from the same results.

Key Capabilities of Modern Testing Tools

AI-powered platforms don’t just make testing easier, they expand what teams can accomplish together. Some of the most impactful capabilities include:

• Plain-language test authoring: Write test steps in English, not code.
• Interactive recording: Capture actions directly in the browser, instantly translating clicks into test steps.
• LLM-assisted authoring: Automatically generate test steps and validations.
• Data-driven testing: Parameterize values, generate contextual test data, and run variations without rewriting scripts.
• JavaScript injections for advanced logic: Give power users the ability to add complexity when needed.
• Self-maintaining suites: Tools can crawl a site, adapt to changes, and keep tests stable over time.

Deterministic LLMs: Reliable Execution at Scale

Not all AI is created equal. General-purpose models can hallucinate or create inconsistent results — exactly what teams don’t want in testing. Purpose-built, deterministic LLMs address this by focusing on consistency, speed, cost, and security:

• Consistency: Predictable execution without variance.
• Speed: Optimized models built specifically for test authoring and execution.
• Cost control: More efficient to run at scale.
• Security: Use of synthetic data ensures sensitive information is never exposed.

Visual AI for Complete Coverage

AI doesn’t just streamline test authoring. Visual AI extends coverage across devices, browsers, and operating systems with far fewer steps to maintain.

• Visual assertions reduce the need for brittle, locator-based checks.
• Multi-device coverage comes with less authoring overhead.
• Group maintenance lets teams accept or reject changes across multiple screens with a single action.

This creates both broader coverage and long-term scalability.

The Impact on Team Collaboration

The real value isn’t just in new features — it’s in how teams work together. AI-powered tools let QA, developers, and business testers all contribute to the same automated workflows. That reduces bottlenecks, speeds up release cycles, and shifts attention to what matters most: quality insights and critical thinking.

Takeaway for QA and Engineering Leaders

AI isn’t here to replace testers — it’s here to elevate them. By bridging skill levels, reducing repetitive work, and maintaining tests automatically, modern platforms create a more collaborative, efficient testing culture.

For mid-size to enterprise organizations, the benefits are clear:

• Faster test authoring and maintenance.
• Broader participation across roles.
• Reliable execution with reduced risk.

Next step: Watch Code & No-Code Journeys: The Collaboration Campground now on-demand, or speak with a testing specialist to explore how AI-powered testing can unify your team and simplify your QA strategy.

The post How Modern Testing Tools Use AI to Bridge Teams and Simplify QA appeared first on AI-Powered End-to-End Testing | Applitools.

Test automation is essential but often time-consuming. Writing and maintaining tests, generating reliable data, switching tools for API calls, and keeping everything aligned across environments can slow down even the best teams.

The latest updates to Applitools Autonomous, part of the broader Applitools Intelligent Testing Platform, introduce features that significantly reduce this overhead. From natural language test authoring to integrated API testing and deterministic execution, these additions help teams move faster with fewer manual steps. Alongside ongoing improvements to Applitools Eyes, the platform continues to evolve to support modern testing workflows at scale.

New Applitools Autonomous Highlights at a Glance:

• Natural language test creation powered by LLMs
• On-the-fly test data generation
• Enhanced API testing with visual builder
• Deterministic test execution (no LLMs at runtime)
• Upcoming support for mobile apps, IDE integration, and Storybook workflows

Natural Language Test Creation, Powered by LLMs

Instead of writing test steps manually or wrestling with locators, you can now describe your intent in plain English. Applitools Autonomous converts your input into executable test steps.

Autonomous interprets the instruction and adapts to your application’s context. Tests can be created by typing, recording interactions, or letting the system generate steps automatically. This approach makes test authoring more accessible, easier to maintain, and more readable across teams.

On-the-Fly Test Data Generation in Context

Need a specific persona, value range, or edge case? Autonomous now includes built-in test data generation. No external tools required.

Just describe what you need, a French fashion designer or a prime number over 1,000, and the platform generates valid, realistic data at runtime. Datasets are generated ahead of time, so test execution remains fast and predictable.

Enhanced API Testing in the Same Flow

You can now send and validate API requests directly within your test flow, using a Postman-style interface.

Author steps in several ways:

• Describe them in plain English
• Use raw HTTP or cURL
• Use the interactive UI builder

Once executed, responses can be inspected, variables extracted, and values asserted. UI, API, and visual checks all operate in a single environment—no tool switching needed.

Deterministic Execution Model for Reliable AI-Powered Tests

A standout feature of this release is the deterministic execution engine behind every test.

“You don’t need to be a prompt engineer or even a developer to scale automation. But when your test runs, it executes with the speed and reliability of code.”
— Adam Carmi, CTO and Co-founder of Applitools

Unlike some platforms that rely on live LLMs during execution—an approach that can be slow or unpredictable—Applitools separates test creation from test execution.

• LLMs assist during authoring and data generation.
• Test runs are powered by a proprietary deterministic model that ensures speed, stability, and consistent behavior.

This offers the flexibility of AI and the dependability of code, without trade-offs.

What’s Coming Next

Applitools continues to invest in both Autonomous and Eyes, with upcoming features focused on deepening cross-functional collaboration, improving performance, and expanding platform coverage.

For Applitools Autonomous:

• Native mobile app testing: Author and execute tests across devices and operating systems.
• Autonomous MCP server: Translate high-level test cases or BDD scenarios into full test flows.

For Applitools Eyes:

• Eyes MCP server: Move Visual AI directly into your workflow. Maintain, review, and run tests directly from your preferred IDE.
• Visual testing in Storybook: Approve changes directly where components are built.
• Performance improvements for component tests: Shorter pipelines and faster feedback loops.
• Figma collaboration enhancements: Sync designs and visual testing for consistent results.

Where Things Stand Now

Whether you’re building automation for the first time or looking to reduce the overhead of test maintenance, this release meets teams where they are. With natural language authoring, integrated data and API support, and a deterministic execution engine, Applitools helps teams reduce manual effort and work more confidently.

If you’re already using Applitools, now’s a great time to explore the latest features. If you’re just getting started, we invite you to see what’s possible with a free trial for you and your team.

The post Applitools Autonomous and Eyes: New AI Features, Better Execution, and What’s Next appeared first on AI-Powered End-to-End Testing | Applitools.

Have you ever encountered a situation where you try to take a screenshot, but instead of the beautifully well-crafted UI, all you’ve got is an image of a spinner/skeleton/loading screen? Handling animations and loading artifacts in visual testing can be daunting.

Don’t worry – it can happen to anyone, and we’re not here to judge you

One of the SDK engineers here at Applitools, Noam, breaks this down into a hands-on tutorial, hoping it will help you get a better understanding of the industry’s best practices around visual testing of rich and dynamic UI experiences.

Let’s dive right in!

Framework Native Solutions

Most frameworks already have different mechanisms to handle animations and loading artifacts. Keeping things simple is often the best way to achieve code stability and maintainability. Using your framework’s built-in tools would most often be the best approach.

For example:

Playwright JS

// Playwright: wait for spinner to be removed
await page.waitForSelector('.spinner', { state: 'detached' });
await eyes.check()

Cypress

// Cypress: wait for spinner to not exist
cy.get('.spinner').should('not.exist'); 
cy.eyesCheck()

Selenium JS

// Selenium: wait for spinner to be invisible
const spinnerElements = await driver.findElements(By.css('.spinner'));
if (spinnerElements.length) {
  await driver.wait(until.elementIsNotVisible(spinnerElements[0]), 5000);
}
await eyes.check()

A Common Pitfall

Even if the UI appears visually unchanged, frontend frameworks like React, Vue, and Angular may re-render elements under the hood. This can lead to stale element references, especially when capturing regions right after a DOM change.

Consider the following example:

cy.get('.main').then($el => {
  cy.get('.spinner').should('not.exist'); // spinner disappears after main was located

  cy.eyesCheckWindow({
    tag: 'main',
    target: 'region',
    element: $el, // stale reference if .main was replaced
  });
});

1. First, Cypress locates .main
2. Then, Cypress waits for the spinner to disappear
3. This example would fail (even if the new element has the exact same properties) if the main element is replaced by another element

How to avoid that?

When possible (e.g., Playwright), it’s preferred that you use locators instead of selectors. If you can’t, it’s better if you use selectors instead of DOM references (element: ‘.main’).

Videos, CSS Animations, GIFs

There are many techniques to eliminate other types of dynamic behaviour in web pages. Playwright, for example, provides a Clock API that allows pausing JavaScript time-related events (including JS-driven animations). It’s also possible to install custom CSS snippets to pause and reset CSS-related animations. Other JS specialized crafted snippets would be required for resetting GIFs, videos, and so on – you get the idea.

This never-ending cat-and-mouse game can be prevented by using Applitools Ultrafast Grid (UFG). Instead of rendering web pages on locally executed browsers, the UFG team maintains specialized logics and fine-tuned commands that ensure a stable and consistent rendering experience. While UFG offers more than just rendering stability, it’s worth noting that classic screenshots can still achieve stable results. UFG just makes it easier!

Algo-Based Solutions

If you intentionally want to capture dynamic content (e.g., animations, changes), a smarter strategy is to embrace that variability and use smart matching algorithms to compare just what you need, like those found in Applitools Eyes.

Any match level can be used for the entire screenshot or specific regions of the screen. Read more in the Match Level Best Practices tutorial. For example, algorithms like the Layout match level can drastically improve your experience with localization testing.

The waitBeforeCapture Setting

Performing wait operations can become more complicated when:

1. Testing with no-code visual testing SDKs (e.g eyes-storybook)
2. Testing with advanced Eyes features like lazyLoading and layoutBreakpoints

The waitBeforeCapture setting was invented for these types of use cases (and a few others).

This setting can receive three types of arguments:

1. Milliseconds – the simplest approach. While it’s not always the most innovative or sophisticated pattern, in many cases, it “does the trick.” In general, waiting for explicit timeouts during tests is not recommended. However, when compared to clock manipulations and code injections, sometimes the simplicity and stability is worth the longer run-time.
2. Selector – when we’re waiting for something to appear, most SDKs support passing a selector, and Applitools Eyes will automatically wait for an element that matches this selector to appear in the web page.
3. Custom function – see code example

// eyes-storybook
  waitBeforeCapture: async () => {
    while (document.querySelector('.spinner')) {
      await new Promise((resolve) => setTimeout(resolve, 100))
    }
    return true;
  }

// eyes-playwright
await eyes.check({
  name: 'my-step',
  async waitBeforeCapture() {
    await page.locator('.spinner').waitFor({state: 'hidden'})
  },
})

The waitBeforeCapture setting can be defined in your applitools.config file, in your eyes.check settings, using the Target settings builder, and in other similar places. Please refer to the documentation of the specific SDK you’re using for concrete examples.

Storybook Play Functions

A nice eyes-storybook-specific trick to achieve a desired rendering state would be a Storybook Play Function.

Applitools Eyes will run your play functions and wait for them to finish before capturing anything on the screen. Use Play Functions to navigate the story to an interesting state and wait for the story to be stable inside the play function to help Eyes understand what the best time is to capture the screenshot.

Applitools is Here to Help

We hope you’ve found this article interesting, and maybe it solved some of the most common visual testing issues you may have encountered. Go ahead and try these examples out for free with Applitools Eyes.

However, if something isn’t clear or if you’d like advice regarding the best way to incorporate visual testing into your organization, please don’t hesitate to reach out to our experts! Testing is our passion, and we’re here to help.

Quick Answers

The post Handling Animations and Loading Artifacts in Visual Testing appeared first on AI-Powered End-to-End Testing | Applitools.

Two recent episodes from Thoma Bravo’s Behind the Deal video series take you behind the scenes of Applitools—offering both a strategic and technical lens on how we’re transforming test automation with Visual AI and autonomous testing.

One episode focuses on the big-picture vision behind Thoma Bravo’s investment. The other digs into the founding story, engineering mindset, and what it really takes to build a testing platform that scales.

How Applitools Uses AI to Revolutionize Test Automation

Host: Carl Press (Thoma Bravo) | Guests: Alex Berry (CEO), Adam Carmi (Co-founder & CTO) | Watch on YouTube

• Why this is the inflection point for AI in testing
• How Applitools helps teams increase coverage while reducing maintenance
• The business logic behind Thoma Bravo’s investment

“With Visual AI, we’re dramatically reducing test maintenance while expanding coverage across the digital experience.”
– Alex Berry, Applitools CEO

Beyond Automation: How Applitools Improves Speed, Scalability & Accuracy

Host: Carl Press | Guests: Alex Berry, Adam Carmi | Watch on YouTube

• The origin story behind Applitools’ platform
• Challenges of scaling visual testing across devices and environments
• Insights from Alex and Adam on culture, leadership, and innovation

“Our goal was to solve the test flakiness problem for good—and make it effortless for teams to deliver quality at scale.”

– Adam Carmi, Applitools Co-Founder & CTO

What’s Next for AI in Software Development?

These episodes offer more than just company insight—they highlight the shifting expectations around quality, speed, and AI in modern software development. If you’re exploring how to future-proof your test strategy, or simply want to see what’s possible with Visual AI, these conversations are a great place to start.

Have questions about how this applies to your team? Reach out to start a conversation—we’re here to help you evaluate if the Applitools Intelligent Testing Platform is the right fit for your goals.

The post Behind the Deal: How Applitools is Scaling AI-Driven Testing appeared first on AI-Powered End-to-End Testing | Applitools.

You’ve got your unit tests. Your end-to-end flows. Maybe even 100% code coverage. But bugs still slip through.

That’s because full code coverage doesn’t guarantee full test coverage. Visual glitches, browser inconsistencies, and content drift often escape traditional automation — and they’re exactly the kinds of issues your users notice first.

In The Coverage Overlook, the kickoff session of our Testing Your Way: Code & No-Code Journeys webinar series, we explored five critical coverage gaps most teams miss — and how to close them with AI-powered visual testing and no-code tools.

1. Visual and Layout Bugs

Code-based assertions won’t catch when an element shifts, disappears, or overlaps. That’s where Visual AI steps in.

By analyzing the rendered UI — not just the DOM — Visual AI identifies layout issues, missing images, overlapping text, and subtle visual defects with a single line of code (or none at all).

“Visual AI can instantly catch layout shifts, missing elements, and new text that coded assertions would miss — all without the maintenance burden of custom locators.”
— Tim Hinds, Applitools

2. Cross-Browser and Device Inconsistencies

Most test suites default to Chrome. But real users span dozens of devices and browsers.

Visual AI tools like Applitools Eyes can validate your app across multiple browsers and screen sizes in parallel — using a single test run. No custom scripting required.

3. Dynamic Content Variations

Personalized content, A/B tests, and location-based content are tough to verify with scripted tests alone.

Visual AI combined with flexible match algorithms can confirm layout structure while ignoring safe visual differences — helping your team catch what matters, without writing exceptions for every variant.

4. Lower-Priority Flows and Pages

Teams tend to focus their test coverage on critical flows — like checkout or login — and leave lower-traffic pages untested.

No-code tools like Applitools Autonomous make it easy to cover the rest. A built-in crawler can scan your site and establish visual baselines across dozens (or hundreds) of pages — all without writing a single test script.

5. Accessibility Gaps

Code coverage can’t catch color contrast failures, missing labels, or overlapping elements that make your UI inaccessible.

Visual AI can. And with upcoming enforcement of the European Accessibility Act, now is the time to start catching these issues early.

Watch the Full Session On-Demand: Code & No-Code Journeys: The Coverage Overlook

Closing the Gap

Code coverage still has value — but modern teams are shifting toward user-centered test coverage.

As shared in the session, teams like Eversana are combining code-based, no-code, and visual testing strategies to expand coverage, accelerate feedback, and reduce risk. With this blended approach, they’ve achieved:

• 65% reduction in regression testing time
• 750+ hours saved per month
• 90% test stability
• A unified testing culture across manual testers, developers, and QA

What’s Next in the Series?

The journey continues with The Maintenance Shortcut, where we explore how teams are reducing flaky tests, eliminating brittle locators, and cutting test maintenance with Visual AI and Autonomous.

The post Think You Have Full Test Coverage? Here Are 5 Gaps Most Teams Miss appeared first on AI-Powered End-to-End Testing | Applitools.

Recently, we shared that Applitools has been named the 2025 CIO Review AI-Powered Test Automation Solution of the Year. This award is more than just a badge of honor—it’s a powerful validation of the work we’ve done with our customers to redefine how software teams build and test with confidence using AI. It marks a key milestone in our mission to help organizations deliver visually perfect applications, faster and smarter than ever before.

“At Applitools, we’re not just following the AI trend—we’re leading it. With a decade of expertise, in-house AI models, and a relentless focus on trust and innovation, we provide AI-powered test automation that enterprises can truly rely on.”

– Alex Berry, Applitools CEO

Why We Won

CIO Review selected Applitools for this award based on the impact and innovation of our Visual AI platform, which enables engineering teams to:

• Accelerate test cycles by 10x through automated visual regression
• Reduce flaky tests and false positives with intelligent image-to-image comparisons
• Ensure perfect pixel-level UX across browsers, devices, and environments
• Scale test automation with code-free UI testing or advanced SDK integrations

Applitools goes beyond traditional test automation by applying Visual AI algorithms that emulate the human eye and brain, identifying functional and visual bugs that other tools miss. Our platform integrates seamlessly with leading test frameworks (like Cypress, Selenium, Playwright, and Appium), CI/CD tools, and cloud platforms—making it a powerful, plug-and-play enhancement to any QA strategy.

Real Results, Real Customers

Over 300 enterprise teams in banking, healthcare, e-commerce, and SaaS trust Applitools to safeguard their user experience with unmatched speed and precision.

Here’s what some of them are saying:

• “Applitools has helped us reduce production defects, streamline visual checks, and free up our team to focus on what humans do best—critical thinking and edge cases.” — Eric Terry, Senior Director of Quality Control at EVERSANA INTOUCH
• “Our quality increases exponentially with Applitools. We run it with every build.” — Walt Harris, Head of Quality at Medallia
• “Applitools automates our visual validation, enabling our engineers to focus their time on delivering value to our customers faster, which has a meaningful impact on our business.” — Jamie Whitehouse, Director of Product at Sonatype

These aren’t just wins for our customers—they’re proof that smarter test automation can unlock velocity, stability, and better digital experiences.

Try Applitools for Yourself

If you’re exploring how AI can elevate your testing game, now’s the perfect time to see why Applitools is setting the industry standard – Schedule a Demo or Start Your Free Trial today.

Read the article on CIO Review—Applitools: Smarter, Faster, Flawless Software Testing Platform

The post What It Means to Win: Inside Applitools’ Journey to the 2025 CIO Review AI-Powered Test Automation Award appeared first on AI-Powered End-to-End Testing | Applitools.

AI is transforming software testing—but without clear context, even the smartest models can fall short. The new Model Context Protocol (MCP) aims to solve that problem, and it’s picking up momentum fast. Here’s what QA and development teams need to know—and why it matters right now. If you have questions about how we’re building for the future or how this fits into your testing strategy, let us know—we’d love to talk.

What Is MCP?

MCP, or Model Context Protocol, is an open standard designed to help applications provide AI models with structured context. Think of it as a standardized way for tools and systems to tell an AI assistant what’s going on—who the user is, what they’re doing, and what resources are available.

Anthropic introduced MCP in late 2024, and it’s already being adopted by major players like OpenAI, Microsoft, and testing leaders building next-generation AI workflows. Addy Osmani, an engineering leader at Google, calls MCP “the USB-C of AI integrations,” highlighting its potential to standardize the connection between tools and intelligent agents.

Why Context Matters in AI-Assisted Testing

Large language models (LLMs) are only as good as the context they receive. Without proper inputs, you get generic outputs—or worse, hallucinations. For QA teams using AI to generate tests, interpret failures, or automate user flows, missing context leads to fragile results and wasted time.

MCP helps solve this by passing structured information to the model: which test framework is in use, what files are open, what code just changed, and more. That means faster, more relevant AI assistance—and more accurate automation.

What MCP Enables in Testing Workflows

MCP makes it easier for tools and AI assistants to share structured context—like which framework is active, what code changed, or what the user is trying to do. That unlocks more accurate test generation, better debugging, and scalable, reusable automation.

It also supports dynamic discovery, so AI systems can find and connect with available tools at runtime—no brittle configs or manual setup required.

As testers ourselves, we take a measured approach to adopting new AI standards like MCP. That means vetting integrations for stability and reliability, so our customers can move fast without sacrificing trust.

Why It’s a Big Deal Now

There are two key reasons to pay attention to MCP today:

First, the standard is taking off. Thought leaders like Angie Jones, Filip Hric, Tariq King, and Addy Osmani are publishing real-world MCP demos and contributing open-source tools. It’s not theoretical anymore—it’s happening.

Second, the stakes are high. As more testing platforms integrate AI (including Applitools Autonomous), the ability to connect tools through open standards like MCP is becoming a competitive differentiator.

How Applitools Fits In

Applitools has long focused on intelligent automation—delivering AI-powered test creation, visual validation, and self-healing across platforms. As open standards like MCP emerge, we’re building on that foundation to extend context-sharing across tools, so teams can:

• Automatically create or update visual and functional tests based on code changes
• Route test context through the pipeline for faster root cause analysis
• Improve AI-generated tests with better accuracy and explainability
  

Security is also critical. As MCP evolves, host-mediated permissions and encrypted communication protocols are being considered by contributors to ensure context is shared safely and responsibly.

At Applitools, we’re building these principles directly into the future of Autonomous and Eyes—and we’d love to walk you through what’s on our roadmap. If you’re already an Applitools customer, reach out to your account team to schedule a preview conversation. If you’re not already using Applitools, schedule time with one of our testing specialists—we’re here to help.

The post MCP: What It Is and Why It Matters for AI in Software Testing appeared first on AI-Powered End-to-End Testing | Applitools.

The excuse “we don’t have time to write tests” doesn’t hold up anymore. AI has reshaped the way teams approach software testing, making it faster, smarter, and more accessible than ever. Tools like GitHub Copilot, ChatGPT, and Applitools Autonomous can generate reliable automated tests without slowing down your development flow.

If you’ve ever struggled with limited testing resources or hesitated to adopt AI-enhanced workflows, now is the perfect time to embrace AI-powered testing.

How GitHub Copilot Helps Accelerate Unit Test Creation

GitHub Copilot can dramatically speed up unit test creation. It can generate unit tests directly in your editor with a single prompt. For example, typing “create unit tests for Hello.tsx” in VS Code can instantly produce functional test cases using React Testing Library.

While Copilot’s first drafts were impressive—correctly using accessible locators and matching key UI elements—it’s important to note that AI-generated tests often require slight refinements.

Expecting a one-shot from AI is probably unrealistic—but in my experience, it gets you pretty darn close.

Copilot typically picks up on your dependencies, infers structure, and outputs readable, executable tests. If the results aren’t perfect, for instance, using fragile selectors or inconsistent naming, you can quickly iterate. Adjusting your prompt often resolves these issues. In many cases, reprompting is faster than manual edits.

Accessible locators and consistent naming can be enforced through clearer prompting or by storing preferences in a centralized configuration file

The key? Good prompts make a big difference. Prompting Copilot to use best practices, like favoring accessible selectors, resulted in much cleaner and more reliable output.

Taking Testing Further with Playwright and Copilot

Beyond unit tests, AI can support end-to-end testing for full user flows. Using Copilot with a framework like Playwright, you can prompt test generation by simply referencing a live URL and desired interactions.

For example, pointing Copilot to a public demo app like TodoMVC and requesting end-to-end tests will often result in tests for adding, completing, deleting, and filtering tasks—all without writing code manually.

To further improve coverage, ChatGPT can help by generating a requirements document for the app. This doc acts as a guide to ensure tests align with expected behaviors.

The better the input we provide the LLM, the better output we’re likely to get. A requirements doc is a really important piece of input.

Once the requirements are defined, you can direct the AI to use them when generating tests, producing more complete and targeted coverage. Just remember to include your preferences for things like locator strategy and naming conventions in your prompt or project config.

The message is clear: Combining ChatGPT and Copilot creates a powerful AI-assisted workflow for test generation. This approach cuts down on manual scripting while improving test depth.

Boosting End-to-End Testing with Applitools Autonomous

Applitools Autonomous handles creating automated tests with AI differently. Instead of writing code or interacting with the DOM, you provide a URL, and the system automatically scans the app. It generates visual and functional tests and organizes results into a centralized dashboard.

Highlights of what Autonomous can do include:

• Crawl an entire application from just a URL and automatically generate visual and functional tests
  
• Use plain English commands to create, edit, and validate tests (no coding needed)
  
• Validate UI, behavior, and API responses in one workflow
  
• Capture dynamic data like confirmation IDs, verify API responses, and support parameterization without code

Unlike traditional recording tools, Autonomous intelligently builds stable, scalable tests while seamlessly validating across browsers. It even flags hidden 404 errors—showcasing the tool’s ability to catch issues early.

Another key point is that anyone, regardless of technical background, can create sophisticated tests using natural language. At the same time, it maintains the depth and flexibility senior developers demand.

Key Takeaways for Modern Testing Workflows

Today’s AI software testing tools are designed for real-world developer needs:

• Copilot accelerates unit and E2E test creation with natural language prompts.
• ChatGPT fills documentation gaps by drafting requirements for better test coverage.
• Applitools Autonomous redefines E2E testing, combining visual validation and functional flows—from UI to visual to API—and plain-English test authoring. It integrates these into a single, no-install SaaS platform.

AI doesn’t replace the tester’s critical thinking — it augments your workflow, helping you focus on improving test quality, not just checking boxes.

In Summary…

The landscape of automated testing is still evolving. With tools like Copilot, ChatGPT, and Applitools Autonomous, building and maintaining high-quality automated tests no longer has to be a slow, painful process. Whether you’re a front-end engineer, QA lead, or tech manager, adopting AI-powered workflows will free up your team’s time. It will increase your confidence in releases and bring better quality to every sprint.

Want to learn more about how to create automated tests with AI? Watch the full session on demand to see in-depth demos.

The post Creating Automated Tests with AI: How to Use Copilot, Playwright, and Applitools Autonomous appeared first on AI-Powered End-to-End Testing | Applitools.

If you’ve already explored how AI-powered, no-code test automation tools can expand who contributes to testing, the next question is: how do you choose the right AI approach for your broader strategy?

Teams today face more pressure than ever to deliver faster without compromising quality. Traditional test automation can’t keep pace—it’s often brittle, siloed, and difficult to scale across teams.

AI-powered testing offers new ways to accelerate coverage, improve stability, and reduce manual effort. But not all AI is created equal. Understanding the differences between AI-assisted, AI-augmented, and autonomous testing models can help you adopt the right tools at the right time—with the right expectations.

Understanding the AI Testing Landscape

AI is showing up everywhere in the testing conversation, but it’s not always clear what type of AI is in play—or how much human involvement is still required. Here’s a breakdown:

AI-assisted testing

These tools support engineers during test creation. Think: autocomplete, code suggestions, or debugging help. They speed up test authoring but still rely on someone writing the test manually.

AI-augmented testing

These systems go further by analyzing existing test repositories, usage data, or logs to identify missing coverage or redundant cases. The AI assists strategically, but the tester still has the final say.

Autonomous testing

This model allows AI to execute test scenarios based on higher-level inputs—like a test goal or an intent. With access to the application, past test data, and usage patterns, it can decide what to test and how. Human oversight is still essential, but the AI drives more of the process.

Each model – assisted, augmented, or autonomous – shapes who can contribute to testing and how much oversight is needed. Choosing the right mix ensures your entire team can move faster without sacrificing quality.

Solving for Coverage, Speed, and Stability

As testing shifts left—and right—teams need solutions that can handle growing complexity without adding manual effort. AI helps in several key areas.

Reducing Flaky Tests

Flaky tests are a drain on time and confidence. They often result from brittle locators, timing issues, or inconsistent environments.

AI-powered self-healing automatically updates broken selectors when the UI changes, helping teams avoid rework and unnecessary test failures.

Authoring Tests Without Code

AI can also simplify how tests are created. NLP-based test creation, for example, allows users to define actions in plain English or record workflows that are translated into readable steps.

This approach has become one of the most accessible and impactful uses of AI in testing, enabling broader participation—from QA to product to manual testers.

Visual Validation for Real-World UI Testing

Functional scripts may confirm that a button exists—but they can’t always tell if it’s visible, clickable, or correctly placed. Visual AI ensures that tests validate what a user actually sees, not just what’s in the DOM.

This level of intelligence is especially critical for responsive design testing and dynamic layouts.

Choosing an Approach That Fits Your Team

The right AI testing strategy depends on where your team is in its automation journey.

• If you’re accelerating test writing with existing frameworks, AI-assisted tools may be the quickest win.
• If you’re optimizing test coverage and reducing redundancy, AI-augmented systems can help prioritize the right areas to test.
• If you’re expanding test ownership across roles, autonomous testing—especially when paired with no-code NLP creation—offers the scale and accessibility to match.

Many teams benefit from a layered approach, combining all three models across workflows.

And behind the technology, delivery matters. Tools powered by in-house AI models offer faster, more consistent results with greater control over privacy and cost—key factors for scaling in enterprise environments.

What’s Next

AI in testing isn’t about replacing people—it’s about enabling them to do more with less. Whether you’re automating UI tests with NLP, analyzing risk with augmented AI, or building autonomous test flows, the goal is the same: faster releases, better coverage, and fewer late-cycle surprises.

Want to explore how different AI models can work together across your test strategy? Watch the full session on demand and see how teams are applying AI-powered testing models to scale quality without increasing complexity.

The post AI-Powered Testing Strategy: Choosing the Right Approach appeared first on AI-Powered End-to-End Testing | Applitools.

Test automation has traditionally lived in the hands of a few specialists—those with the right coding skills, framework knowledge, and time to maintain complex test suites. But software quality touches every part of the delivery process, from product to engineering to QA.

Modern no-code test automation tools are shifting that dynamic. These AI-powered platforms enable teams across roles to create, run, and maintain automated tests—without writing code. And they’re doing it without sacrificing speed, accuracy, or scale.

Here’s how these tools work, what they solve, and why they’re reshaping the way teams approach software quality.

Breaking the Bottlenecks of Traditional Automation

Traditional test automation frameworks come with steep requirements: deep technical skills, time-consuming setup, and scripts that only a few team members can decipher. This creates bottlenecks. When product owners or manual testers can’t contribute, test coverage shrinks—and feedback loops slow down.

No-code test automation tools address this challenge by allowing users to write tests in plain language. Instead of scripting every action, they can describe intent:

“Enter email in login form.”
“Click the primary button.”

This approach makes test cases easier to read, faster to debug, and simpler to hand off between roles.

From Recorded Actions to Readable Test Steps

Most no-code platforms offer more than just simplified language—they streamline how tests are created in the first place. With action recording, testers interact with the app as a user would. Behind the scenes, the tool converts those actions into plain-English test steps using AI and natural language processing.

This drastically reduces authoring time. And since the resulting steps are readable by anyone on the team, debugging and collaboration get a lot easier.

Compared to traditional scripting, this is a faster, clearer, and more inclusive way to build test coverage.

Expanding Who Can Contribute to Test Automation

When test authoring isn’t limited to engineers, more of the team can contribute to quality. That doesn’t just speed things up—it also improves collaboration and visibility.

• Manual testers move from documentation to execution without needing to code.
• QA engineers delegate simpler test flows and focus on complex or edge cases.
• Product owners and business analysts define expected behaviors directly in test interfaces.
• Developers get fast, readable test results that don’t require decoding selectors or scanning logs.

This shift improves velocity while reducing dependencies on any one person or team.

AI Behind the Simplicity: Powering Stability at Scale

The best no-code test automation tools go beyond accessibility—they’re backed by intelligent automation that’s production-ready.

• Self-healing fixes broken locators automatically, even when UI structure changes.
• Visual AI ensures the UI looks right—not just that elements exist in the DOM.
• Root cause analysis explains test failures clearly, saving hours of manual debugging.

These capabilities give teams confidence that their tests will work reliably across browsers, devices, and environments. And when the platform is powered by in-house AI (not third-party APIs), it ensures greater speed, privacy, and control.

Scaling Quality, Not Just Test Automation

No-code test automation tools don’t eliminate testers—they empower them. When everyone can contribute to testing, teams increase their coverage, accelerate release cycles, and reduce time spent chasing down brittle scripts.

What used to take hours of setup or deep technical expertise can now be achieved through a browser session and plain-English instructions. That’s the power of no-code—and the intelligence of modern AI testing tools.

Want to see how no-code test automation works in practice? Watch the full session on-demand and explore how teams are scaling test coverage with AI-powered tools designed for speed, stability, and collaboration.

The post No-Code, No Problem: How AI Testing Tools Expand Test Automation Across Teams appeared first on AI-Powered End-to-End Testing | Applitools.