LLM & Agent Infrastructure Demo for QA Automation.

Instead of only describing my experience, I wanted to show it. This demo reflects the way I work: taking an idea, building the automation infrastructure around it, using AI agents where they create real efficiency, and connecting everything into a working flow that a team can actually use.

AI Agent Workflow Agents help convert product context into practical QA scenarios.

Playwright Execution Generated scenarios are connected to real browser automation.

MCP & Tool Integration MCP servers and tools support a smarter automation workflow.

What to look for

Access instructions

The demo is protected because it runs real automation capabilities behind the scenes. Access is granted only to approved reviewer emails.

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A little about me

For me, strong automation is not only about writing tests. It is about building systems that help engineers move faster, understand risk earlier, and release with more confidence.

This is the mindset I bring into automation roles: practical ownership, technical depth, and the ability to connect QA, development and product needs into one scalable workflow.

I build working tools, connect technologies, validate ideas quickly, and turn automation into something visible and valuable for the team.

The goal is to help engineers design better tests, reduce repetitive work, find gaps faster and make smarter QA decisions.

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What I recommend looking at during the demo

The UI is intentionally simple. The important part is the engineering thinking behind the flow: how requirements become scenarios, how scenarios become execution, and how results become insight.

Live technical demonstration

STEP 01 Start from product behavior The flow begins from a real user or product scenario, not from random test code.

STEP 02 Generate testing intent AI helps structure the scenario, expected behavior and automation direction.

STEP 03 Execute with Playwright The scenario is connected to real browser automation instead of staying as documentation.

STEP 04 Explain the result The output is meant to help people understand what happened and what should be improved next.

These are the areas I would be happy to expand on during the interview.

ARCHITECTURE How I would scale this in a real company Repository structure, test ownership, CI integration, logs, reports and maintainability.

TEAM IMPACT How I make automation usable for developers Simple patterns, clear documentation, reusable helpers and mentoring across BE and FE teams.

AI STRATEGY Where AI helps — and where it should not decide alone Using AI for acceleration while keeping engineering judgment, review and reliability in the loop.

QUALITY MINDSET How I connect quality to release confidence Risk visibility, coverage thinking, faster feedback loops and data-driven QA decisions.

This demo is a compact example of how I approach automation work: I identify a real bottleneck, design the infrastructure, use AI as a practical accelerator, and build something that makes quality work easier for the entire team.