AI Agents Are Great at Exploratory Testing. Regression Needs Repeatable Assets.

AI agents excel at exploratory testing during the early stages of feature evaluation. Given a task like "check whether a user can create a project," an agent can navigate the product, interact with forms and modals, and examine results. It can also adapt to unexpected situations by analyzing the page, code, logs, and network activity to determine the next course of action. This capability is particularly valuable when a feature is new, ambiguous, or rapidly evolving.

However, relying solely on AI agents for regression testing is not advisable. While they can successfully complete workflows, they lack the consistency and reproducibility required for regression testing. Regression testing demands a standardized sequence of actions, explicit assertions, reliable test data, and clear ownership in case of failures.

When considering AI-assisted regression testing, four key factors are typically missing in an agent-driven approach:

1. A stable path: Regression testing necessitates a consistent sequence of actions and preconditions that can be reviewed and modified as needed.

2. Explicit assertions: Verifying that the workflow succeeded requires explicit checks, such as confirming the existence of a record, changes in status, adherence to permission boundaries, or the presence of expected errors.

3. Reusable test data and preconditions: Repeatable assets should include mechanisms to generate test data, avoid collisions, and establish a controlled setup, ensuring reliable results across multiple runs.

4. Evidence and ownership upon failure: In the event of a test failure, the team should have clear information about the failed case, expected vs. observed behavior, and the responsible party for the next steps.

To optimize the testing process, a practical approach involves a handoff between AI-assisted exploration and traditional regression testing. Initially, AI agents can aid in exploratory testing of new features or ambiguous requirements, generating observations, risks, and bug reports. Once a workflow becomes crucial and repeatable, it should be formally designed and captured, including named steps, assertions, test data rules, and ownership.

This captured workflow can then be used for deterministic replay during every release or relevant change. If failures or significant UI changes occur, AI agents can assist in investigating the root cause, proposing updates, and identifying new risks.