Q9 of 21 · AI for testing

What are agentic testing tools and how do they differ from traditional automation frameworks?

AI for testingMidai-for-testingagentic-testingautonomous-testingai-agentsbrowser-automation

Short answer

Short answer: Agentic testing tools use an AI agent to plan actions, execute them against a live browser or API, and evaluate outcomes — without a pre-scripted sequence of steps. Traditional frameworks execute a fixed, deterministic script; agentic tools explore and decide what to do next based on the current state.

Detail

A traditional Playwright test executes exactly the steps you wrote: click this button, fill that field, assert this text. An agentic tool receives a higher-level goal ("log in and add an item to the cart") and lets an AI model decide how to achieve it by observing the current page state.

The plan-act-verify loop is the core cycle: plan the next action, execute it, observe the result, repeat.

The trade-offs are significant. Agentic tests are slower (each action involves an LLM call), non-deterministic (the agent may take a different path each run), expensive (cost per token at scale), and harder to debug. They shine for exploratory coverage of frequently-changing UIs where maintaining a scripted test is too expensive — not for regression of critical deterministic flows like payment processing.

See Agentic testing landscape, Agentic testing case studies, and Agent cost and latency for current tool comparisons and cost trade-offs.

// WHAT INTERVIEWERS LOOK FOR

Clear distinction: scripted (fixed sequence) vs agentic (plan-act-verify, dynamic). Honest trade-offs: slower, non-deterministic, expensive. Knowing when agentic makes sense and when it does not.