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Agentic QA expands the test target: teams must verify not only whether a task reached its intended outcome, but also whether the AI agent chose effective, permitted actions along the way. Keep deterministic tests for stable requirements; add agent-focused checks when a system interprets goals, uses tools, or adapts its route.
How agentic QA differs from traditional automation
Traditional automation generally executes authored steps and evaluates known assertions. An agentic test can instead interpret a goal, inspect the current state, select and invoke tools, and adjust its route when an interface changes. Amazon Science describes this shift as “moving from fixed script replay to agent driven execution and judgement” in its 2026 CIGE publication. That is an editorial framing, not an industry-wide standard definition.
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The term agentic QA covers a range of tool-assisted, semi-autonomous, and agent-driven approaches. It does not mean that agents have replaced deterministic suites. It means the test needs to assess more than a script’s final pass or fail.
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| Dimension | Traditional automation | Agentic execution |
|---|---|---|
| Execution model | Runs authored steps and assertions. | Interprets a goal and takes tool-mediated actions. |
| Response to change | Often depends on a specified sequence or selectors. | May recover from small changes; adaptability must be measured, not presumed. |
| Evidence to inspect | Step results and assertion outcomes. | Plan, tool choice and arguments, intermediate state, rule compliance, and final outcome. |
| Repeatability | Designed for rerunning the same checks. | May vary across runs; successful scenarios can be converted into conventional regression checks in some implementations. |
What a useful agent test must verify
A task that ends correctly can still conceal a bad route: the agent might have used an invalid argument, violated a rule, or taken an unnecessary action. Evaluate the trajectory as well as the outcome.
- Plan sufficiency: Was the plan adequate for the requested goal?
- Tool selection and arguments: Did the agent call an appropriate tool with valid inputs?
- Intermediate results: Did it interpret tool outputs and changing state correctly?
- Rule compliance: Did it stay within explicit behavior constraints and permissions?
- Outcome: Did the intended, observable state actually result?
Microsoft Research’s Agent-Pex demonstrates one evaluation pattern: treat prompts and traces as partial specifications, extract checkable rules, score trace compliance, compare models, and generate adversarial tests by inverting rules. Its project page reports evaluation of more than 5,000 Tau² traces across four models and three domains; the page does not state the year for that figure (accessed 2026). This is a research example, not evidence that every team needs the same scoring method.
How to handle variability and regression
Unlike a fixed script, an agent may take different tool-call sequences in response to similar prompts. IBM also warns that errors early in a multi-step run can surface later, and that agents may regress or drift over time. A single successful run is therefore weak evidence of reliable behavior.
- Preserve traces. Record plans, tool calls and arguments, intermediate outputs, and the final state so a run can be inspected.
- Evaluate across runs and versions. Compare behavior against defined rules rather than relying on one pass.
- Keep deterministic checks for stable requirements. They provide repeatable regression coverage where exact expected behavior is known.
- Turn suitable successes into rerunnable tests. AMD’s published blueprint shows Gherkin scenarios generating a downloadable Pytest module for independent reruns.
How to investigate a failed agent run
A red status alone does not explain whether the cause was a weak plan, a faulty tool call, a misleading tool result, or a failure to reach the required state. Inspect the trace and find the step where the run first went off course.
Microsoft Research’s AgentRx focuses on locating a critical failure step in agent trajectories. Its 2026 announcement describes a benchmark containing 115 manually annotated failed trajectories. That benchmark is a research resource; the figure is not a production failure rate or proof of universal diagnostic accuracy.
What an implementation can look like
AMD documents one agentic testing blueprint rather than a comparative benchmark. It accepts Given-When-Then scenarios in a Streamlit interface. A Python orchestrator connects an LLM service to browser tools exposed by a Playwright MCP server; the interface displays live progress, and successful scenarios can generate a Pytest module for later execution. The documentation also describes an OpenAI-compatible endpoint option, an MCP server using SSE transport, and deployment through Helm charts on Kubernetes.
This example illustrates how flexible execution can connect to familiar test artifacts. It does not establish production effectiveness or prove that this architecture is the right choice for every team.
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Where human oversight and permissions fit
Tool access makes authorization part of test quality. Define which actions are allowed, check that the agent follows those rules, and require approval or human review when actions have consequential effects. Validate the resulting state independently rather than treating the agent’s own report as proof.
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When to use each approach
- Use deterministic automation when requirements and expected states are stable and repeatable checks matter.
- Consider agentic execution when the system must interpret goals, work through tools, or adapt to modest interface changes.
- Use both when adaptive exploration is valuable but critical requirements still need predictable regression coverage.
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