🚀 How I Built an AI Agent That Generates Structured Test Cases in Minutes

I’ve been working in software for more than 10 years 🖥️. I’ve always been obsessed with making life smoother and easier — both in personal routines and in big projects.

After reading Atomic Habits 📚, my life’s credo became:

"Optimize it all — from daily routines to big projects. That’s how you achieve the biggest success."

I wouldn’t say I lived differently before reading the book, but it confirmed that my way of thinking was on the right track. Optimization has always been my strength.

When AI started entering our lives — and while some people worried about layoffs — I saw it differently. I realized AI could give me more power 💡.

As a QA Manager leading a team of 7 talented testers, I’ve always believed that process optimization is the backbone of quality assurance. Over the past few years, I’ve combined my management experience with AI-powered solutions and multi-step agents to build workflows that dramatically improved my team’s efficiency.

🎯 The Goal: Kill the Manual Test Case Grind
If you’ve ever set up a Jira API integration, you know it’s not exactly “click and done” 🛠️.
For me, it was a mix of OAuth headaches 😵, endless API docs 📑, and too many “why is this not returning anything?” moments.

But once I got it working, the payoff was huge:
My team now has a GPT-powered Structured Test Case Generator that transforms Jira tickets into ready-to-publish, standardized test cases in minutes.

Before this project, test case creation looked like this:
🔍 Open Jira, find the ticket.
📖 Read through the user story, acceptance criteria, and sometimes even comments.
✍️ Manually write the test case in our preferred structure.
📥 Copy it into our test management tool (Testomat.io).
It was repetitive, slow, and prone to inconsistencies.

I wanted an AI agent that could:
🔗 Read a Jira ticket directly via API.
📄 Extract the summary, description, and acceptance criteria.
🏗 Generate structured test cases in our exact QA format.
✏️ Offer edits, add negatives, or merge cases before publishing.
🚀 Push them straight to Testomat.io once approved.

🛠 The Build: GPT + Jira API + Testomat.io
I created the Structured Test Case Generator here in GPT with:

• Model: GPT-4o ⚡ (fast + accurate)
• Capabilities: Web search, Canvas, Image Generation, Code Interpreter for formatting/validation
• Actions: api.atlassian.com → Jira ticket retrieval app.testomat.io → publishing test cases

What it does:

• Accepts a Jira ticket ID like TEST-123.
• Calls fetchJiraTicket to get details.
• Generates structured test cases with: Title Preconditions Steps to Reproduce Expected Results

Aaaand

• Prompts the user to review, edit, or add more cases.
• Publishes to Testomat.io if approved.

Here's a schema process of how it works:

🧩 The Jira API Challenge
Getting GPT to talk to Jira wasn’t just a copy-paste job. My main roadblocks:
🔐 OAuth 2.0 Setup — aligning the callback URL with GPT’s environment took trial and error. It was hard 🥵🥵🥵
🛑 Permissions — one missing API scope = hours of confusion.
📦 Data Formatting — Jira’s JSON is… verbose. I had to filter just the essentials.
⚠️ Edge Cases — missing fields or messy acceptance criteria meant adding fallbacks to avoid bad outputs.

🏆 The Win for the QA Team
❤️ Before: 45–60 minutes to manually write & format test cases for a complex ticket.
💚 After: 3–5 minutes to review AI-generated cases, tweak if needed, and hit “Publish.”

We’ve:
✅ Reduced test case writing time by ~50%
✅ Standardized all formats across the team
✅ Increased coverage with negative and edge cases
✅ Eliminated copy-paste fatigue with direct Testomat.io integration

🔄 Example Flow

Then the agent asks:
💬 "Would you like to edit, add negatives, merge, or publish?"

One click later → it’s in Testomat.io, ready for execution ✅.

📚 Lessons Learned

🔧 APIs are the glue — but OAuth setup inside GPT requires patience.
🗂 Prompt clarity = output quality — be specific about formatting.
🤝 Integration matters — direct publishing to Testomat.io is the real game-changer.
💡 Life hack: Add project-related docs to the Agent’s Knowledge section to avoid irrelevant or generic test cases.

💭 Final Thoughts
This project proved something important: AI isn’t replacing QA engineers — it’s making us faster, more consistent, and more focused on what matters most: testing quality.

If you’re a QA lead or engineer, try building your own GPT agent + API workflow. The setup might test your patience (hello, Jira OAuth 👋), but the payoff is worth it.

💬 Your turn:
Have you tried connecting GPT to Jira or your own test management tool? What challenges did you face?