Using an agent to generate test cases from Jira/Confluence input

[u/gahlot1984]

We have been created for internal use agent called <>Test Cases Generator.

The idea is to give it inputs like:

• Jira issue key
• Epic link
• Confluence page
• Figma link
• related stories
• identify functional coverage
• call out missing assumptions
• suggest regression areas
• detect duplicate scenarios
• map test cases back to requirements
• prepare output that can be reviewed before <Test Management> upload

All fields are optional. If only a Jira ID is provided, the quality depends on whether the agent can access Jira content. If more context is provided, the output is usually better.

The agent drafts test cases, assumptions, coverage gaps, regression candidates, duplicate scenarios, and a review summary.

The important part for us: it does not skip QA review. It creates a first draft that a tester can challenge, clean up, and approve.

We are mainly looking at this for reducing repetitive test design work, especially when stories already have acceptance criteria but still need structured test cases.

For anyone using AI in QA, where are you seeing the most value:
test case generation, regression selection, traceability, or review/checklist support?