Agentic Testing
Agentic testing uses autonomous AI agents that can plan, execute, and adapt a multi-step testing process with minimal human direction — exploring an application, deciding what to test next based on what they observe, and adjusting when something unexpected happens. It differs from scripted automation, which only ever does exactly what it was explicitly told to do.
Traditional test automation executes a fixed script: click here, assert that, move to the next line. An agentic test agent is given a goal instead — "verify the checkout flow works end to end" — and figures out the steps itself: navigating the UI, reading what's on screen, deciding which button represents "checkout," and adapting if the layout doesn't match what it expected.
This matters most for exploratory testing and coverage of unscripted paths, where writing an explicit script for every possible user journey isn't practical. An agent can wander a new build the way a human exploratory tester would, surfacing issues nobody thought to write a test case for.
The tradeoff is the same one that shows up across all agentic AI: non-deterministic output. The same agent given the same goal twice may take a different path, which makes agentic test agents genuinely useful for finding unknown issues, but a poor replacement for the reliable, repeatable pass/fail signal a regression suite needs to provide.
