Lately, we’ve been highlighting our YAKSS sessions, but now it’s time to introduce you to another concept that has been running in IBORN for years, the Lightning Talks. The Lightning Talks are a shorter, punchier format designed for sharing quick insights, tools or experiments.
While YAKSS sessions run around 30-40 minutes with in-depth discussions, Lightning Talks are 15-20 minutes long, giving our team a chance to present new ideas, innovative tools or clever hacks that make everyday work easier, all in a rapid, high energy format.
In the latest session, our QA engineers Anastasija Bogdanovska and Stefan Popovic presented their QA Agent, a forward-looking approach to quality assurance that leverages AI to support testers in their daily work.
What is a QA Agent?
QA Agent is an intelligent system that assists QA teams by automating repetitive tasks, analyzing large datasets and providing actionable insights. It helps testers:
- Focus on high impact areas instead of routine work;
- Detect potential issues earlier in the development cycle;
- Maintain consistency and thoroughness across tests.
By blending AI assistance with human expertise, QA Agents transform traditional QA workflows into more efficient, data driven and outcome focused processes.
What Value Does It Bring
🕰️ Time Savings: Reduces hours of manual test setup, saves dozens of QA hours monthly;
⚙️ Better Coverage and Consistency: Provides consistent test quality, fewer regressions and traceable coverage reports;
🚀 Faster Feedback Loop: Prevents broken releases, catches integration bugs before production, and reduces support issues;
📈 Data-Driven QA Insights: Delivers QA analytics that support prioritization, decision-making, and continuous process improvement.
Why This Matters
The session emphasized that AI isn’t replacing QA professionals - it’s enhancing them. By using QA Agents, teams can:
- Reduce repetitive workload;
- Focus on tests that truly drive quality;
- Increase collaboration across QA, development and product teams;
- Turn QA into a strategic function that contributes directly to product success.
Anastasija B and Stefan P sparked a lively discussion on the future of testing, showing how even small AI-powered interventions can have a big impact on workflow, efficiency and product quality.
