Latest news with #softwaretesting
Forbes
11 hours ago
- Business
- Forbes
Agentic AI In Enterprise QA: Powering Intelligent, Autonomous Testing At Scale
Pradeep Govindasamy is the Co-Founder, President and CEO of QualiZeal. We're at the beginning of a new era in quality engineering, one shaped by agentic AI. While generative AI has captured global attention, the real transformation in software testing is only just beginning. I believe we're now entering a phase where AI isn't just assisting people in testing tasks. It's becoming autonomous, goal-driven and capable of acting with intelligence across the lifecycle. At QualiZeal, we're witnessing this shift firsthand. As someone who has spent years in the testing space, I can confidently say that AI is not a far-off future. It's here, being built into our processes today, and it's already beginning to disrupt how we think about quality at scale. Software development and testing are the two most critical pillars in any IT application lifecycle. To get a product into the hands of customers, you first build it, then test it and only then can you ship it. We've seen how tools like GitHub Copilot have revolutionized development. Now, that same level of AI adoption is happening in software testing. This is no small market—it's a $100 billion global industry. And just as smartphones once disrupted legacy devices like BlackBerry, AI is poised to transform testing in a similar way. Every phase of the software testing lifecycle—test case preparation, test design, test data management, performance testing, site reliability engineering—is now being infused with AI to increase efficiency, productivity, and ultimately software quality. Before we talk about agentic AI, we need to understand the evolution. The first step in embracing AI is automating repetitive, rule-based tasks. Once you have robust automation in place, AI capabilities can be layered on top to improve every phase of testing. But agentic AI goes one step further. With standard AI, we build prompts, define logic and teach the models how to behave. With agentic AI, we create systems that learn, adapt and act autonomously. These agents follow instructions and understand intent. They can analyze changes in the system, adjust automation scripts accordingly and execute tests without human intervention. For example, imagine a scenario where a company updates its checkout process, maybe tariffs or payment options change. In the past, a QA team would have to manually identify changes, rewrite test scripts and rerun tests. With agentic AI, the system learns what's changed, modifies the scripts, self-heals when errors occur and continues testing. It even generates a report outlining what it changed and why. This self-healing, self-optimizing capability sets agentic AI apart from traditional automation. And it's a game-changer. We're seeing both technical benefits and measurable business outcomes. With agentic AI, the cost of quality is decreasing. From my observation, the industry average today is about 18%, but with AI-infused testing, we anticipate a 5% drop, driven by reduced manual effort and increased efficiency. In maintenance alone, we've seen a reduction from 20% of team capacity to less than 5%. Even more importantly, release cycles are accelerating. Time to market (TTM) has gone from quarterly to weekly, and now, with agentic AI and DevOps practices, to daily releases. The entire production throughput is becoming faster and more reliable. And decision-making is more seamless because agentic systems provide full transparency through real-time reporting, eliminating the need to compile data across disparate systems. Organizations looking to lead in this space must prepare now. I always say this moment is not just about catching up—it's about disrupting yourself before you get disrupted. Companies that wait too long will miss the opportunity to lead. Those who invest now will be in a position to capture market share and build the next generation of testing capabilities. This preparation requires both a top-down and bottom-up approach. Leadership must allocate budgets, not just wait for client-driven funding, and teams must be empowered to get trained, certified, and exposed to different AI models. AI isn't just a CIO or CTO conversation anymore. It's happening at the board level, and for good reason: this is the foundation for long-term competitiveness. I recommend organizations push their teams to reach at least level three in AI readiness: basic execution. Core functions like engineering and QA need to go further, while ancillary teams like finance and marketing should also gain exposure. Of course, with great power comes responsibility. We need to ensure agentic systems operate ethically, transparently and securely. Especially in regulated industries like healthcare, insurance or banking, any AI-driven decision, no matter how small, can have massive consequences. That's why testing the AI itself is just as important as using AI for testing. There's a growing demand for AI-specific test engineers who can validate agentic systems through high-end exploratory techniques. Traditional testing models like equivalence partitioning or boundary analysis must now be complemented with new approaches tailored to AI behavior. In the near future, eight to 10 new job roles will emerge specifically to test and validate agentic AI systems. These won't be optional. They'll be mission-critical. We estimate that full-scale AI maturity across the testing lifecycle will arrive around 2027. Between now and then, we're in the planning and education phase, training models, customizing LLMs and building the necessary infrastructure. Implementation will accelerate in 2026, and by mid-2027, I expect the majority of enterprise QA environments to be agentic by design. This is a once-in-a-generation opportunity for testers, developers and technology leaders. Gen Z professionals, especially those raised in a digital-native world, will have an edge. They can adopt these tools faster, and many will find themselves building careers in entirely new domains. We're not just building testing systems anymore. We're building trusting systems. Platforms that learn, adapt and support business continuity without human babysitting. That's the future of QA. That's where agentic AI takes us. And the companies that embrace it today? They'll be the ones defining quality tomorrow. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Geeky Gadgets
28-05-2025
- Business
- Geeky Gadgets
Say Goodbye to Manual Code Testing In Cursor with Operative.sh
What if the most time-consuming, error-prone aspect of web development could be transformed into a seamless, automated process? For years, developers have wrestled with the challenges of testing—debugging intricate edge cases, validating functionality across countless scenarios, and making sure flawless performance under pressure. These tasks, while critical, often drain resources and slow down innovation. But now, a breakthrough has emerged. By combining the power of Cursor AI with tools like the toughest bottlenecks in web application testing are being shattered. With AI-driven automation, developers are no longer bound by the manual grind, unlocking a new era of efficiency and precision. AI Labs explores how Cursor and are transforming the testing landscape. From automating repetitive tasks to generating test cases with natural language instructions, these tools are designed to tackle even the most complex scenarios with ease. You'll discover how features like the Web Eval Agent and browser state management streamline workflows, reduce human error, and empower developers to focus on innovation rather than troubleshooting. Whether you're navigating intricate edge cases or optimizing performance, this exploration will reveal how AI is reshaping the future of software testing—one automated process at a time. AI-Powered Code Testing Tools How Cursor and Streamline Testing The integration of Cursor with provides a seamless and efficient way to automate web application testing. This combination enables developers to handle everything from basic login processes to complex edge case scenarios using natural language instructions. By automating the generation and execution of test cases, the need for extensive manual scripting is eliminated. This not only saves time but also reduces the risk of human error, resulting in a more reliable and comprehensive testing process. Even intricate applications benefit from this streamlined approach, making sure robust performance across various scenarios. Core Features That Enhance Testing is equipped with a range of features designed to optimize the testing experience. These features automate repetitive tasks and improve overall efficiency, allowing developers to focus on more strategic aspects of their projects. Key components include: Web Eval Agent: This feature automates browser-based tasks using Playwright, a powerful browser automation framework that ensures consistent and accurate test execution. This feature automates browser-based tasks using Playwright, a powerful browser automation framework that ensures consistent and accurate test execution. Setup Browser State: By saving browser sessions, this feature eliminates the need for repetitive logins, significantly streamlining the testing process and reducing setup time. These tools work in tandem to simplify the testing workflow, allowing developers to concentrate on higher-level tasks while the intricacies of testing are managed automatically. Streamline App Development with AI Code Testing Automations Watch this video on YouTube. Here are more guides from our previous articles and guides related to AI automations that you may find helpful. Getting Started: Installation and Setup Setting up is designed to be straightforward, making sure accessibility for developers of all experience levels. The tool can be installed manually or through an automated process via GitHub. To activate the system, an API key is required, making sure secure and controlled access to its features. Once integrated with Cursor, operates seamlessly within your development environment. This integration allows you to initiate and manage tests effortlessly, making it a practical addition to any development workflow. How the Testing Workflow Operates The testing workflow offered by is both intuitive and efficient, catering to a wide range of development needs. Tests can be executed in either headless or visible browser modes, depending on user preferences. Results are presented in a comprehensive dashboard that provides detailed insights, including: Logs of executed tests for easy tracking and analysis Network requests to monitor application performance Error reports to identify and address issues promptly In addition to these features, the tool automatically generates and tests edge cases, making sure that your application performs reliably under various conditions. This level of automation reduces the need for manual oversight, allowing developers to identify and resolve potential issues early in the development cycle. Performance Insights and Limitations While offers numerous advantages, it is important to acknowledge its limitations. For highly complex scenarios, the testing process may take longer compared to manual scripting. Additionally, certain tests may require manual configuration or the use of supplementary tools to achieve optimal results. Despite these challenges, the tool's ability to automate repetitive tasks and handle edge cases makes it a valuable asset for developers seeking to enhance efficiency and accuracy in their workflows. Why Development Teams Benefit Integrating into your development workflow can significantly improve how your team approaches web application testing. By automating repetitive tasks and identifying issues early, the tool minimizes the likelihood of bugs as your application scales. This allows your team to dedicate more time to innovation and feature development, ultimately enhancing productivity and software quality. The streamlined testing process also ensures that applications are thoroughly validated, reducing the risk of errors in production environments. Looking Ahead: The Future of AI-Driven Testing The evolution of AI-driven tools like is set to transform the field of software testing further. As these tools continue to advance, they are likely to become comprehensive systems for managing and tracking test cases. This progression could make the testing process even more efficient and accessible for development teams, regardless of project complexity. By automating more aspects of testing and integrating seamlessly with other development tools, AI-driven solutions have the potential to redefine how software testing is conducted, paving the way for greater innovation and reliability in the software industry. Media Credit: AI LABS Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
