What Agentic AI Really Looks Like in API Testing

AI is quickly becoming part of API testing. But not every AI-powered workflow works the same way.

Some approaches assist testers with individual tasks. Others collaborate across multiple steps. Increasingly, agentic workflows can take a testing objective and autonomously use trusted and proven testing capabilities to accomplish it.

What does that progression look like in practice?

In this session, we explore three approaches to AI in API testing: assisted, collaborative, and agentic. You’ll see how each level changes the way testers work and where AI can reduce the manual effort involved in creating, configuring, and executing API tests.

We demonstrate how collaborative and agentic workflows turn testing intent into executable API tests, reducing the manual steps and handoffs traditionally required to move from requirements to verification.

You’ll leave with a practical understanding of where AI assistance ends, where agentic testing begins, and how teams can introduce greater autonomy into API testing while maintaining control over the process.

Why testing needs to keep pace

The webinar opens with a basic challenge: AI can speed up development, but it does not automatically make the resulting software reliable. APIs still need to be validated, test data has to be prepared, and dependencies need to be available. Someone also has to decide whether the results show that the software works as intended.

The speakers share figures suggesting that AI-generated code is becoming a much larger part of development, while many developers remain unsure whether that code is functionally correct. That creates pressure on QA teams, especially when release schedules are tight and testing capacity stays the same.

API verification involves more than sending a request and checking for a successful response. Testers need to understand the requirement, plan scenarios, create assertions, prepare data, and account for services the API depends on. They must then run the tests, investigate failures, and update the assets as the application changes. When development speeds up, those steps can become a bottleneck.

Three ways AI can take part

The presenters describe three broad approaches to using AI in testing:

An assisted workflow is familiar to anyone who has asked a chatbot for help. The AI may explain how to create a test or generate a starting point, but the user remains responsible for completing the task.

In a collaborative workflow, an agent can use connected tools to carry out several steps. The human is still involved, often approving actions or correcting the agent along the way. This can be useful for teams that want to try AI without handing over the whole process.

Orchestrated workflows take the idea further. Agents work through a loop toward a goal, rather than waiting for a person to direct every action. That can reduce manual handoffs, but it also makes clear rules, permissions, and oversight important.

Turning a requirement into an API test

The live demonstration shows a collaborative workflow for testing a deposit process. Instead of manually building each test action, the presenter gives an AI assistant the requirement and asks it to create and execute a scenario.

Through an MCP connection, the assistant inspects the API definition to understand the available endpoints. It builds a sequence that includes a login step, a deposit request, and a verification step to check that the deposit is reflected by the service. It also adds data handling and assertions, rather than stopping at a response code.

The agent prepares the test environment with a clean data state, then asks for permission to run the test. After execution, it reports what it created and whether the scenario passed. The presenter then asks it to add a data source, so the same workflow can be tested with different inputs. When the updated test fails, the agent examines the result, makes a change, and runs it again.

That back-and-forth is an important part of the example. The AI can do useful work, but the test remains visible and adjustable. The team can inspect the actions, data, and results instead of treating the output as a black box. Negative testing is possible too: a test can check that an API returns the expected error status or message when given invalid input.

From a demo to an agentic testing loop

For a more autonomous workflow, the presenters outline five stages: observe, plan, act, verify, and decide.

1. Observe: Gather requirements, application information, existing test results, and quality measures.
2. Plan: Define what the agent should deliver and what the next step needs as input.
3. Act: Create or update tests and run them against the application.
4. Verify: Collect results and compare them with agreed quality thresholds.
5. Decide: Stop if the criteria are met, or send the work through another round if they are not.

This pattern can connect testing with other parts of the software lifecycle. For example, an agent might use a requirement from a task-tracking system, create API tests, review execution results, and report evidence of whether the feature met its criteria. Agents could also help create virtual services to stand in for unavailable or unstable dependencies.

The webinar does not demonstrate the full orchestrated loop, but the outline makes the goal clear: let agents handle more of the steps needed to verify a change, while keeping the result measurable and reviewable.

Autonomy does not mean giving up control

The presenters stress that teams do not have to jump straight to fully autonomous workflows. A sensible path may start with AI suggestions, move to collaborative tasks, and increase autonomy as the team gains experience and confidence.

Humans still define the testing objective, decide what data and tools the agent can access, set the quality thresholds, and choose where approvals are required. They also remain accountable for decisions such as whether a change is ready to merge.

The central point is simple: AI can accelerate software creation, so testing has to scale too. Agentic API testing is not about adding AI for its own sake. It is about reducing repetitive work and helping teams verify changes faster, while keeping clear oversight of the tests and the decisions that follow.