Agentic QA: How AI Agents Are Changing Software Testing

Software development is becoming more automated, and software testing is beginning to follow the same path. The next step is not simply generating more test cases with artificial intelligence. It is allowing AI systems to participate in larger testing workflows.

This idea is often described as agentic QA.

Instead of waiting for a human to specify every individual action, an AI agent can receive a goal, interact with an application or testing system, execute actions, inspect the results, and determine what to do next.

That does not mean quality assurance suddenly becomes fully autonomous. Human judgment remains essential for defining requirements, identifying risks, deciding what good software should actually do, and reviewing important results.

But AI QA agents are changing how much of the repetitive execution and investigation work can be delegated to software.

What Is Agentic QA?

Agentic QA is an approach to software quality assurance in which AI agents can perform multi-step testing tasks toward a defined objective.

A conventional automated workflow generally follows predetermined instructions:

1. Execute step A.
2. Execute step B.
3. Check condition C.
4. Report whether the test passed.

An AI agent can operate differently. Instead of receiving only a fixed sequence of instructions, it may receive a higher-level goal, such as:

“Verify that a customer can find a product, add it to the cart, complete checkout, and receive confirmation.”

The agent can then determine how to interact with the application, observe the current state, select appropriate actions, evaluate responses, and continue working toward that goal.

The important distinction is decision-making during execution.

Agentic systems may use context from previous actions to decide what to do next rather than simply replaying a rigid sequence.

This concept is closely related to the broader use of AI in software quality. Teams interested in the underlying technologies can also explore testRigor’s guide to AI in software testing.

How AI Agents Change the Testing Workflow

Traditional test automation usually requires humans to define both the objective and much of the execution logic.

With agentic testing, the human can increasingly focus on describing the intended result.

A typical AI testing workflow may look like this:

• A QA engineer provides a goal or acceptance criteria.
• An AI agent determines which actions may be necessary.
• The agent interacts with the application or testing platform.
• Test execution produces screenshots, logs, errors, or other evidence.
• The agent evaluates the results.
• If something fails, it can investigate the failure.
• It may adjust its approach or gather additional information.
• The final result is returned to the human for review.

The testing process, therefore, becomes more iterative.

Instead of automation being limited to “execute these instructions,” an AI testing agent may participate in planning, execution, observation, analysis, and follow-up.

Agentic QA vs Conventional Test Automation

The biggest difference is not simply the presence of AI.

Many testing systems already use machine learning or generative AI for isolated tasks such as generating tests. That does not necessarily make the entire workflow agentic.

Conventional automation is usually deterministic. Humans define most of the path before execution begins.

Agentic test automation can introduce another layer:

Goal → reasoning → action → observation → additional action → result

For example, a conventional test could be instructed to click a specific button.

An agent may instead receive the goal of reaching a particular part of an application. It can inspect the interface, identify an appropriate path, interact with the system, observe what happened, and continue.

That flexibility is particularly useful when application states vary or when testing requires more exploration.

However, deterministic automation still has advantages. Highly controlled checks may be easier to reproduce, audit, and understand when every action is explicitly defined.

The two approaches can therefore coexist.

What an AI QA Agent Can Do

The capabilities of AI QA agents vary significantly between platforms, but an agentic workflow can potentially include several activities.

An agent may:

• interpret a testing objective;
• create test cases from requirements;
• explore an application;
• execute tests;
• interact with user interfaces;
• inspect results and execution evidence;
• identify failed scenarios;
• investigate possible causes;
• rerun tests;
• perform exploratory testing;
• report defects or unexpected behavior;
• communicate with development tools through APIs or protocols such as MCP.

The distinguishing feature is the ability to connect several actions into a continuing workflow.

A single AI-generated test is not necessarily agentic. An AI system that generates a test, runs it, examines the failure, modifies its approach, reruns the test, and reports its findings is much closer to an agentic model.

Where Humans Remain Essential

Agentic QA does not eliminate the need for QA professionals.

In fact, as execution becomes easier to automate, human expertise may shift toward decisions that require product knowledge and judgment.

Humans still need to answer questions such as:

• What behavior actually matters to customers?
• Which workflows present the greatest business risk?
• What should happen in an ambiguous situation?
• Is a technically successful result actually a good user experience?
• Is a reported anomaly a real defect?
• Are the agent’s assumptions correct?
• Should a release be blocked because of a particular problem?

Human QA professionals also understand context that an agent may not have, including business rules, regulatory requirements, unusual customer behavior, historical defects, and organizational priorities.

Even testRigor’s documentation for AI coding-agent workflows explicitly notes that users remain responsible for test design, business knowledge, reviewing proposed changes, and confirming that tests validate the intended application behavior.

Agentic QA therefore works best as delegation, not abdication.

Emerging Agentic QA Platforms

A growing group of testing products is exploring different versions of autonomous QA and agent-assisted testing.

testRigor

testRigor combines natural-language test automation with newer agentic workflows.

Tests can be created and maintained using plain-English instructions rather than requiring users to express every scenario as conventional automation code. Its documentation describes support for web, mobile web, native and hybrid mobile applications, desktop applications, mainframe systems, APIs, SMS, phone calls, 2FA, and other end-to-end scenarios.

The agentic layer becomes especially relevant through testRigor’s MCP server.

MCP, or Model Context Protocol, allows compatible AI tools to interact with external systems. Through testRigor’s implementation, an AI assistant can perform supported operations such as listing test suites and test cases, running individual tests or complete suites, retrieving execution results, identifying failures, and managing running tasks.

This creates an AI testing workflow in which a user can give an AI assistant a goal, allow it to work with testRigor, inspect the outcome, investigate problems, and continue refining the test.

The distinction between autonomy and human direction matters. The agent operates within available permissions and instructions. Humans still define the goal, control access, review proposed actions, and validate the result.

QA.tech

QA.tech describes its platform around autonomous QA agents that perform end-to-end, regression, and exploratory testing from goals expressed in natural language.

Its agents can run testing around pull requests, merges, scheduled workflows, and production environments. The platform also emphasizes goal-oriented execution in which agents respond to the application state instead of depending entirely on fixed test steps.

That makes it an example of agentic QA centered on autonomous application interaction and continuous validation.

Spur

Spur approaches agentic testing through AI agents designed to execute user journeys and adapt to what they encounter.

Its platform supports plain-English test descriptions, while agents can respond dynamically to conditions such as pop-ups, unavailable items, or changes in an application flow.

Spur also provides MCP capabilities that allow compatible AI assistants to work with test information, create tests, execute regressions, and inspect execution artifacts such as screenshots and logs.

Ranger

Ranger connects QA closely with AI-assisted software development.

Its platform describes a workflow in which a coding agent can invoke Ranger to launch a browser, verify a feature, and return testing results while the development agent continues working.

This reflects an emerging pattern where testing becomes a tool that another AI agent can call during software creation.

BugBrain

BugBrain focuses heavily on autonomous exploration.

Its AI agents can explore a web application, interact with flows, find potential defects and regressions, and return evidence such as screenshots and reproduction steps. The platform also supports repeatable tests written in plain English as well as PR-oriented automation.

Its documentation additionally lists MCP tools, illustrating how QA capabilities are increasingly being exposed to other AI systems.

Agentic QA and AI Coding Agents

One of the most important applications of agentic QA may be its relationship with AI coding agents.

When software can be generated or modified rapidly, verification also needs to become faster.

A useful architecture is:

Requirement → coding agent → application change → QA agent → test result → coding agent

The QA system becomes a feedback mechanism.

For example, a coding agent may implement a feature. An AI QA agent then runs relevant end-to-end scenarios and identifies a failure. The coding agent receives that feedback, changes the implementation, and submits it for verification again.

testRigor’s MCP workflow is one example of this pattern because compatible AI agents can create or modify tests, execute them, inspect results, and continue refining their work.

Instead of treating testing as a final phase, quality checks can become part of the AI-assisted development loop.

Risks and Limitations

Agentic systems introduce their own risks.

AI agents can misunderstand requirements, choose an incorrect path, overlook an important condition, or interpret ambiguous evidence incorrectly.

Other concerns include:

• hallucinated assumptions;
• inconsistent decisions;
• insufficient test coverage;
• permissions that provide more access than necessary;
• unexpected actions in sensitive environments;
• difficulty reproducing exploratory behavior;
• cost associated with large numbers of AI interactions;
• overconfidence in apparently autonomous results.

The solution is not simply “more AI.”

Teams need clear objectives, controlled permissions, observable execution, reliable test evidence, and appropriate human review.

Agentic systems should make important actions inspectable rather than turning QA into a black box.

Future of Agentic Software Testing

The future of AI-powered quality assurance is likely to involve more collaboration between specialized agents.

A coding agent may build software. A testing agent may validate it. Another system may analyze security or performance. The agents could exchange results and continue working until predefined acceptance criteria are satisfied.

QA professionals would increasingly define those criteria, design testing strategies, identify risks, investigate unusual failures, and supervise automated workflows.

This changes the role of automation.

The objective is no longer simply to automate individual test steps. It is to automate more of the testing workflow while keeping human judgment in the places where judgment matters most.

Conclusion

Agentic QA represents a shift from automated execution toward more goal-driven testing workflows.

AI agents can increasingly interpret objectives, interact with applications, execute tests, inspect evidence, investigate failures, and continue working without requiring a human to manually initiate every individual action.

Platforms such as testRigor, QA.tech, Spur, Ranger, and BugBrain demonstrate different approaches to this evolution, from MCP-connected testing workflows and natural-language automation to autonomous exploration and validation of software changes.

As these technologies continue to develop, understanding the broader AI concepts behind autonomous agents, generative AI, and intelligent workflows will also become more important for QA and engineering teams. Resources such as NeuroBits AI can help professionals learn more about AI and better understand the technologies shaping the next generation of software development and testing.

The most important change may therefore be organizational rather than purely technical.

Humans decide what quality means.

Agents can increasingly help execute the work required to verify it.

FAQ

What is agentic QA?

Agentic QA is software testing in which AI agents can work toward testing goals by performing multiple actions, observing results, making decisions, and continuing the workflow with less step-by-step human direction.

What is an AI QA agent?

An AI QA agent is an AI system capable of performing testing-related actions such as generating tests, interacting with applications, executing test scenarios, analyzing results, investigating failures, or performing exploratory testing.

Is agentic QA the same as AI test automation?

Not necessarily. AI test automation can include isolated AI features such as test generation. Agentic QA generally refers to broader workflows in which AI can take actions, evaluate their outcomes, and determine subsequent actions.

Can agentic QA replace human testers?

Agentic QA can automate more execution and investigation work, but humans remain important for defining expected behavior, assessing risk, designing testing strategies, reviewing ambiguous results, and making business decisions.

How does MCP relate to agentic QA?

Model Context Protocol allows compatible AI systems to interact with external tools. In QA, an MCP server can expose testing operations to an AI agent so it can perform actions such as accessing tests, triggering executions, retrieving results, or investigating failures.

Why is agentic QA important for AI-generated software?

AI coding systems can generate and modify software quickly. Agentic testing provides a way to build verification into the same workflow, allowing testing systems to provide feedback that development agents and humans can use before software reaches production.