Agentic AI testing
Agentic workflows describe a broader pattern: an agent given a goal works out its own sequence of steps, calling tools and adjusting based on what each step returns, instead of following a fixed script written in advance. Agentic AI testing is that same pattern applied specifically to QA, an agent reads what changed in the application, decides what needs testing, executes it, reads the result, and decides whether to retry, escalate as a likely defect, or move on, rather than working down a checklist a person wrote in advance.
A scripted test runs a fixed sequence regardless of what it finds along the way. Earlier AI-assisted automation might generate that script from a plain-language description, but the generated script still runs the same fixed way once it exists. Agentic AI testing keeps reasoning active through the whole process, the agent's next action depends on what the previous one actually found, which is what makes it capable of handling a situation nobody explicitly anticipated when the test was set up, closely related to the capability described in AI test execution.
That flexibility is also why it's harder to fully predict and audit than a fixed script, two runs can genuinely take different paths depending on what each one observes. That's part of why most teams build trust in an agentic system gradually, a theme that overlaps directly with where a team lands on the broader autonomous testing spectrum.
What makes it agentic
- An agent reads what actually changed in the application rather than following a fixed, pre-written plan.
- It decides what needs testing based on that change, rather than a person specifying every test to run.
- It executes the test and observes the real result, not a predicted one.
- It decides the next step, retry, escalate, or move on, based on what it actually found.
- The same loop repeats, with each decision shaped by the one before it, rather than a fixed sequence.
Where it shows up
- An agent noticing a change to a shared component and deciding on its own to re-test every flow that depends on it.
- A failure getting investigated by the same agent that found it, which retries once before deciding whether to escalate it as a likely defect.
- A testing agent adjusting its approach mid-run when a page doesn't match what it expected, instead of simply failing.
- A team gradually trusting an agent with more of this loop as its decisions prove reliable over time.
Benefits and challenges
| Benefit | Challenge |
|---|---|
| Handles a situation nobody explicitly anticipated when the test was originally set up, since the agent reasons in the moment rather than following a fixed script. | Two runs can genuinely take different paths depending on what each one observes, which makes an exact failure harder to reproduce. |
| Reduces how much a person has to manually trigger and decide, since the agent plans its own next step based on real results. | Auditing why an agent made a particular decision takes different tooling than reading a script's fixed logic. |
| Adapts to an application that's actively changing, rather than breaking the moment reality no longer matches what a script expected. | Trust has to be built gradually, handing over too much of the loop before an agent's judgment is proven risks a missed real defect. |
Frequently asked questions
Is agentic AI testing the same as AI test automation?
Related but more specific. AI test automation broadly covers using AI to generate, run, and maintain tests. Agentic AI testing describes one particular pattern within that, an agent that plans, acts, observes, and decides its next step in a loop, rather than running a fixed, pre-generated script.
How does agentic AI testing relate to autonomous testing?
Agentic AI testing is usually the technical mechanism underneath a highly autonomous testing system. Autonomous testing describes how much human involvement remains in the overall process, while agentic AI testing describes the specific plan, act, observe, decide pattern that makes a high level of autonomy possible.
Can agentic AI testing be trusted to run without review?
Over time, for routine cases, many teams get there. Most start by reviewing an agent's decisions closely, extending trust as its judgment proves reliable, rather than handing over the full loop unsupervised from day one, especially for anything high-stakes.
How Klarent helps
Klarent's AI agents follow this exact pattern, planning what to check based on what actually changed, executing it, reading the result, and deciding the next step, whether that's retrying, escalating, or moving on, instead of working down a script that can't adjust to what it actually finds.