You have automated tests. They pass. A user reports a bug anyway.
The script confirmed the button existed. It didn't confirm the button was visible, unobstructed, or rendering correctly on the user's screen. That gap between what automation verifies and what users actually see is where visual bugs live.
What is visual testing with AI?
Visual testing with AI evaluates the rendered interface, what a user actually sees, rather than the code behind it. Instead of checking whether an element exists in the DOM, it observes what appears on screen.
This catches a different class of defect:
- Layout breaks that don't affect DOM structure
- Elements rendered off-screen or hidden behind other components
- Visual regressions after design system or framework updates
- State changes that complete in code but produce the wrong visual output
Traditional script-based tests often miss these entirely. They confirm an element exists and a click registered. They don't confirm what the user actually sees.
A script checks: "does the login button exist?" An agentic test checks: "is the login button visible, unobstructed, and in the position a user would expect?" These are different questions with different answers.
How visual testing evolved into agentic testing
Visual testing got the direction right. Instead of asking "did the code run?" it asked "did the interface look correct to a user?" That shift from code verification to user-perspective validation was the right instinct.
But screenshot comparison has limits. It works well in controlled environments: a browser, a fixed viewport, a known baseline. The problems show up when UI changes, when interfaces live outside the browser, or when a test needs to do more than compare images.
When a component moves or a design system updates, a screenshot baseline breaks. Someone has to find the broken tests, understand why they failed, update the baselines, and verify the fix. That loop, not the initial authoring, is what drains QA capacity. A typical test cycle involves 12 tools and roughly 2.5 hours of overhead per cycle: Jira, TestRail, IDE, CI pipeline, VPN, log readers (based on AskUI customer research). When that much time goes to toolchain friction, actual UI validation gets less attention than it should.
The next step was extending the instinct further. Testing interfaces that live outside the browser requires more than screenshot comparison. HMI systems, embedded OS, physical device environments: these need an agent that can observe, reason, and act, not just compare.
An agentic approach observes the screen, reasons about what it sees, and decides how to act, the same way a human tester would. Instead of targeting DOM selectors or hardcoded coordinates, it uses every available interface: selectors and DOM where available, screen-based reasoning where not, across web, desktop, HMI, and embedded systems. That's why it handles unexpected screens, slow loads, and popups without breaking, even in environments with no DOM to inspect.
When a test fails, you're not chasing logs across multiple tools. Every run produces a structured execution trace with every observation, every decision, every action, and timestamps. You have the forensic record of what the agent saw and did.
AskUI: agentic testing infrastructure
AskUI is the execution layer for this kind of testing. It runs across web, desktop, HMI, embedded systems, and physical device environments, making agentic testing production-ready at scale. AskUI includes a built-in HTML reporter that captures step-by-step execution details and screenshots at each interaction. Tests are written in plain language: no Selenium, Cypress, or Python skills required.
FAQ
What types of interfaces does agentic testing support?
Agentic testing works across any interface an agent can observe and interact with. That includes web applications, desktop applications on Windows, macOS, and Linux, mobile apps on Android and iOS, HMI systems in automotive and industrial environments, embedded OS interfaces, and physical test environments like HIL and SIL benches. Unlike browser-based visual testing tools, it is not limited to environments that expose a DOM or run in a cloud sandbox.
What is agentic testing?
Agentic testing is an approach where an AI agent operates a user interface the way a human tester would. It observes the screen, reasons about what it sees, and takes action. Unlike script-based automation, it evaluates what users actually see rather than what the code reports, catching layout defects and visual regressions that functional tests miss.
How is agentic testing different from visual testing?
Visual testing verifies appearance. Agentic testing verifies behavior through screen-based interaction. An agent can notice visual problems while executing a flow, but it is designed to complete tasks, not just compare screens.
Does AskUI do visual testing?
Not exactly. AskUI is agentic testing infrastructure. The agent captures screenshots to observe the interface and reason about what it sees, rather than comparing them against a static baseline. It runs across web, desktop, HMI, and physical device environments, covering the interfaces that browser-based visual testing can't reach. AskUI includes a built-in HTML reporter that captures step-by-step execution details and screenshots at each interaction, so when something breaks, you have the full forensic record of what the agent saw and did.
Do I need to write code to use AskUI?
No. Tests are written in plain language: natural language instructions that describe what to test, step by step. Anyone who can write a Jira ticket can write a test for AskUI.
How does AskUI report test results?
Every test run automatically produces three structured artifacts: an Execution Report with every observation, decision, and action the agent took; a Test Report with per-test status, warnings, and screenshots; and a Summary Report with aggregated pass/fail results. These are generated automatically, no manual write-up required, and are audit-ready by default, meeting requirements under CRA, ISO 26262, and IEC 62304.