Everywhere you turn, someone is talking about AI — AI this, AI that. No wonder some people roll their eyes at the mention of artificial intelligence. For some, it’s all smoke and mirrors, just a glorified spreadsheet rather than a technological breakthrough capable of real cognitive reasoning.
And just when you think you’ve caught up, something new appears. First, we had simple machine learning and AI, then came Generative AI, and now Agentic AI is all the rage. If you feel like you’re constantly playing catch-up, you’re not alone.
But whether you love it or loathe it, AI isn’t going anywhere. In fact, some tools are now designed to think, create, and learn—just like Keysight’s Eggplant Intelligence.
The Thinking, Creating, and Learning Framework
This framework simplifies AI by breaking it into three key functions:
- Thinking involves decision-making and adaptability, much like Agentic AI, which enables AI to make choices based on real-time data.
- Creating is tied to generative AI capabilities, allowing AI to generate test cases and user scenarios autonomously.
- Learning follows the principles of traditional machine learning, pioneered by Alan Turing in 1950, and enables AI to improve over time based on historical data.
Figure 1: Eggplant Intelligence supports the entire Quality Engineering Lifecycle
So, what’s the real difference between these AI types? How do they impact software testing? And does anyone actually care? The short answer: there are plenty of differences, they have a huge impact, and yes, you should care.
Before we unravel these questions, let’s take a trip down memory lane to understand how we got here.
The Birth of AI in Software Testing – Keysight Eggplant’s Heritage
Back in 1947, Alan Turing gave a lecture that introduced the idea of a machine’s ability to exhibit intelligent behaviour and learn just like a human. Since then, ‘machine learning’ and artificial intelligence has evolved considerably, and in 2018, Keysight Eggplant integrated such tools into its Digital Automation Intelligence (DAI) platform, which is now known as Eggplant Test. This was groundbreaking then and remains so today, enabling automated software testing to:
- Identify all user journeys – Machine learning algorithms analyze applications and uncover every possible user journey to generate test cases automatically, improving test coverage and reducing manual effort.
- Prioritize test cases – By learning from historical test runs and code changes, the system can pinpoint high-risk areas and prioritize testing where it matters most, optimizing testing time and resources.
- Detect anomalies – AI can track normal system behavior, spot deviations, and flag potential defects early in the development cycle.
- Adapt test scripts – Automated scripts dynamically adjust to application changes, minimizing maintenance and improving long-term test stability.
This goes beyond simple test automation. Imagine changing your payment gateway on an eCommerce site—Eggplant can auto-generate new test cases to reflect the update without requiring hours of script rewrites. That’s the power of intelligent automation.
But AI in software testing isn’t just about running test cases. Keysight Eggplant Test has also led the way in image-based testing, optical character recognition (OCR), and computer vision—critical for automating graphical user interface (GUI) testing in complex, secure environments.
Generative AI – Automating Test Creation
Next up: Generative AI, the “Creating” part of the framework. This subset of AI revolves around understanding and generating human-like language through natural language processing (NLP), including large language models (LLMs).
Generative AI can be used to automate test cases, reducing manual effort while improving accuracy. But Keysight is taking it a step further—our Gen AI capabilities are in development to generate test case frameworks directly from software requirements documentation, allowing testers to refine them rather than start from scratch once launched.
Security is also a major priority, which is why when Eggplant Test with Gen AI is launched it will operate using secure, offline, technology-agnostic LLMs. Unlike cloud-based solutions, our models will be deployed on-premises, ensuring complete control over sensitive data and compliance with strict security regulations.
Cloud-based AI testing tools that use ChatGPT pose risks, such as “shadow prompting,” where unchecked user inputs generate unreliable outputs. While techniques like prompt engineering can mitigate this, on-premises AI solutions eliminate the risk altogether.
Agentic AI – The Next Evolution
Now, we arrive at Agentic AI, the “Thinking” part of our framework. This evolution introduces intelligent agents that can autonomously design, execute, and optimize test cases. Using chain of thought, a technique that stacks multiple commands to perform complex tasks, these agents perform intricate testing, ensuring all possible user interactions and edge cases are covered.
Another breakthrough is computer use agents (CUA) such as large action models (LAMs), which automate browser-based processes by interacting with web applications just like human testers. This is crucial for end-to-end web testing across various devices and browsers.
And then there’s large vision models (LLaVA), which enhance technologies like traditional computer vision to interpret and validate visual data, verifying UI elements and graphical components in applications.
Sound familiar? It should. Eggplant Intelligence already integrates elements of AI, Gen AI, and Agentic AI into a single platform. Our system optimizes test coverage, automates interactions across digital environments, and executes tests just as a human would, all while remaining offline and compliant with AI governance laws in the UK, EU, and US.
AI Testing Compliance – The Keysight Advantage
Many testing tools rely on cloud-based AI architectures, making them non-compliant with the EU AI Act and other regulatory frameworks. Cloud-based solutions often fail to meet the strict security demands of regulated industries, leaving organizations exposed to potential privacy violations.
For industries like aerospace, defense, and healthcare—where data security is non-negotiable—cloud-based AI testing tools are simply not an option. Storing customer or intellectual property data outside a secure firewall can lead to legal consequences and hefty fines.
This is why Keysight Eggplant is the only AI-powered testing solution that prioritizes security, transparency, and governance. Our on-premises approach ensures that all sensitive data remains secure, meeting even the most stringent compliance requirements.
And let’s be clear—using cloud-based AI for test script generation or test reports is not only risky but illegal in many jurisdictions. GDPR and other data protection laws prohibit storing customer data outside of an organization’s firewall, making cloud AI tools a liability for compliance-conscious businesses.
The Future of AI in Software Testing
AI in testing isn’t just about keeping up with the latest buzzwords. It’s about making smart, future-proof choices that balance innovation with security, scalability, and compliance.
Keysight Eggplant has been pioneering AI-driven testing since 2017, long before many of today’s players entered the field. As AI evolves, we continue to push boundaries, ensuring our platform remains at the cutting edge of secure, offline AI testing.
So, if you’re serious about automated software testing and need a future-proof, AI-driven platform that doesn’t compromise security, compliance, or flexibility—it’s time to take a closer look at Keysight Eggplant.
Contact us today for a 14-day free trial or have a read of the Ultimate AI Testing Playbook.
Header image is a photo by Mauro Sbicego on Unsplash.