Agentic AI Testing

tldr: Agentic AI testing uses autonomous AI agents that receive a goal, navigate your application on their own, adapt when things change, and report what broke. Unlike traditional automation that follows a script, agentic systems figure out how to test based on what you want verified.

Scripts are the wrong abstraction

Here's what a typical Playwright test looks like:

await page.goto('/login');
await page.fill('#email', 'user@example.com');
await page.fill('#password', 'password123');
await page.click('[data-testid="login-btn"]');
await page.waitForURL('/dashboard');

Five lines. Five assumptions. The login page is at /login. The email field has an ID of #email. The button has a specific test ID. The redirect goes to /dashboard. Change any one of those and the test breaks.

This is script-based testing. You tell the computer exactly what to do, step by step. It works until something moves.

Agentic AI testing takes a different approach. You give the agent a goal: "Log in as a test user and verify the dashboard loads." The agent figures out the rest. It finds the login page, identifies the email and password fields (regardless of their HTML attributes), submits the form, and confirms the dashboard appeared.

The difference isn't cosmetic. It's architectural.

What "agentic" actually means

The word "agentic" comes from AI research. An agent is a system that can:

1. Perceive its environment (read the page, understand the UI)
2. Plan a sequence of actions to achieve a goal
3. Act on those plans (click, type, navigate)
4. Observe the results (did the page change? did an error appear?)
5. Adapt when something unexpected happens

Traditional test scripts do steps 3 and 4. They act and observe. But they don't perceive, plan, or adapt. Every action is predetermined. Every expected result is hardcoded.

An AI testing agent operates more like a human tester. A human tester doesn't memorize CSS selectors. They look at the screen, find the login form, type credentials, and check what happens. If the form moved to a different part of the page, they still find it. If the button text changed from "Log In" to "Sign In," they don't freeze.

Agentic AI testing replicates this flexibility in software.

Three generations of test automation

Understanding agentic testing is easier when you see how we got here.

Generation 1: Script-based automation

Selenium, Playwright, Cypress. You write code. The code runs in a browser. Tests are fast, repeatable, and completely rigid. A single selector change can break dozens of tests. Maintenance is the biggest cost.

This has been the standard for 15+ years. Most teams still use it. And most teams still complain about flaky tests.

Generation 2: AI-assisted automation

Tools like Testim, Mabl, and Healenium added intelligence on top of existing frameworks. Self-healing locators detect when an element moves and find it again. AI generates test scripts from recordings. Smart waits reduce flakiness from timing issues.

These tools made test maintenance easier. But the fundamental model didn't change. You still have scripts. You still have step-by-step instructions. The AI just patches the cracks.

Generation 3: Agentic automation

This is the current shift. Instead of scripts with AI patches, you have AI agents with no scripts at all. The agent receives an objective. It explores the application. It decides which actions to take. It validates outcomes.

No selectors. No hardcoded paths. No step-by-step instructions.

The agent uses a combination of techniques: visual understanding of the page, accessibility tree analysis, natural language processing of page content, and learned patterns from previous runs. When your UI changes, the agent doesn't need to "heal" a broken locator. It never had a locator to begin with. It finds the element fresh every time.

How an agentic test actually runs

Here's a simplified view of what happens when you run an agentic test:

You write: "Verify that a user can add an item to their cart and proceed to checkout."

The agent:

1. Opens the application's homepage.
2. Scans the page to understand the layout. Identifies navigation, product listings, and interactive elements.
3. Clicks on a product category or searches for an item.
4. Selects a product and adds it to the cart.
5. Navigates to the cart page.
6. Verifies the item appears with the correct name and price.
7. Clicks the checkout button.
8. Confirms the checkout page loaded successfully.

At each step, the agent makes decisions. If the product listing is paginated, it may scroll or click "next." If an "Add to Cart" confirmation modal appears, it handles it. If the cart icon is in the header instead of a dedicated page, it clicks there instead.

None of these decisions were scripted. The agent figured them out by understanding the UI.

Where agentic testing outperforms scripts

Dynamic UIs

Single-page applications with dynamic content are nightmares for traditional automation. Elements load asynchronously. Components re-render. IDs change between sessions. An agentic test doesn't care. It evaluates the current state of the page every time it needs to act.

Multi-step flows with branching

A checkout flow might have conditional steps: guest checkout vs. logged-in checkout. Promo code field that appears only with certain cart values. Shipping options that depend on the delivery address.

Script-based tests need separate test files for each branch. An agentic test adapts to whichever path appears during execution.

Post-deployment verification

After a deployment, you want to verify critical flows work in production. But production might have different data, different feature flags, or slightly different UI than staging. Agentic tests handle these variations because they navigate by intent, not by selector.

Exploratory-style regression

Traditional regression tests verify known paths. Agentic tests can explore beyond the defined path. If the agent notices a broken image, a missing link, or a console error while navigating to the checkout, it can flag these as side findings. This is closer to how a human tester works during exploratory testing.

Limitations worth knowing

Agentic AI testing is promising. It's also young. A few realities:

Speed. Agentic tests are slower than script-based tests. An AI agent analyzing the page, planning its next action, and executing takes more time than a Playwright script that knows exactly what to click. Expect 2-5x slower execution for individual tests.

Determinism. Script-based tests produce the same result every time (when not flaky). Agentic tests can take slightly different paths each run. The outcome should be the same, but the exact steps might vary. This makes debugging harder when a test fails.

Complex assertions. Verifying that a specific number appears in a specific table cell is straightforward in Playwright. Telling an agent "verify the total is $49.99 including tax" requires the agent to understand where totals are displayed and which number represents what. This works, but it's less precise than a direct selector assertion.

Not a fit for everything. API tests, database validation, and pure data checks don't benefit from agentic testing. The value is in UI interaction and navigation, where the visual, dynamic nature of web apps creates the problems scripts can't handle well.

Agentic testing vs. AI-augmented testing

People sometimes confuse these. They're different.

AI-augmented testing makes the old model more durable. Agentic testing replaces the model entirely.

Who uses agentic testing today

Agentic AI testing is still early. Most teams are in generation 1 or 2. But adoption is growing, especially among:

• Teams without QA engineers. Startups and small engineering teams that can't write or maintain Selenium suites. They describe tests in plain English and let agents handle execution.
• Fast-shipping teams. Companies deploying multiple times per day need tests that keep up without constant maintenance. Agentic tests adapt to every deployment's UI state.
• Teams burned by maintenance. If you've lived through a UI redesign that broke 200 tests, you understand the appeal of tests that don't depend on selectors.

Bug0 is one example of agentic testing in production. Its AI agents navigate web applications, test outcomes rather than steps, and self-heal when the UI changes. Available as a self-serve platform (Bug0 Studio) or fully managed with forward-deployed engineers (Bug0 Managed).

What to ask when evaluating agentic tools

• How does the agent understand the page? Vision models, accessibility trees, DOM analysis, or a combination?
• What happens when the agent gets stuck? Does it retry, escalate, or silently fail?
• Can you constrain the agent? Sometimes you need specific steps (like entering a particular promo code). Can you mix agentic goals with explicit instructions?
• How do you debug failures? Agent-based tests need good observability: recordings, step logs, and decision traces.
• What's the cost per test run? AI inference adds cost. Understand how pricing scales with test volume.

FAQs

What is agentic AI testing?

Agentic AI testing uses autonomous AI agents that receive a testing goal, navigate your application independently, adapt to UI changes, and report results. Unlike script-based automation, the agent decides how to test based on what you want verified.

How is agentic testing different from AI-assisted testing?

AI-assisted testing adds features like self-healing locators to traditional scripts. The script still defines every step. Agentic testing has no script. The AI agent plans and executes its own steps based on a goal. It's the difference between fixing a script when it breaks vs. never having a script to break.

Is agentic testing ready for production use?

Yes, for E2E browser testing of web applications. Tools like Bug0 run agentic tests in production today. But the technology is still maturing. Expect improvements in speed, determinism, and complex assertion handling over the next 1-2 years.

Does agentic testing replace Playwright or Selenium?

For E2E UI tests, it can. For lower-level tests (component tests, API tests, unit tests), traditional frameworks are still the right choice. Many teams use agentic testing for high-level flows and Playwright for specific, precise validations.

How fast are agentic tests compared to script-based tests?

Individual agentic tests run 2-5x slower because the AI needs to analyze the page at each step. But you save time on test creation (minutes vs. hours) and maintenance (near zero vs. 40-60% of QA time). The net time savings is significant.

Can I mix agentic and scripted tests?

Yes. Most AI automation testing platforms let you combine AI-driven navigation with explicit assertions. For example, let the agent navigate to checkout, then explicitly verify the total matches an expected value.

What types of applications work best with agentic testing?

Web applications with complex user flows, dynamic UIs, and frequent design changes benefit the most. E-commerce checkouts, SaaS dashboards, onboarding flows, and multi-step forms are common use cases. Testsigma extends this to mobile apps (native, hybrid, and mobile web) on 2,000+ real devices, making it useful for mobile-first teams.

What's the biggest risk of adopting agentic testing?

Vendor lock-in. Most agentic platforms don't export tests as standalone scripts. Your test definitions live inside the platform. Evaluate the vendor's stability and your exit options before committing.