2026-03-06 –, Tutorial on Automation with Image Recognition Libraries - SikuliLibrary (and ImageHorizonLibrary)
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Can one engineer design, implement, and validate a full feature in real time with AI? In this hands-on session, you’ll see exactly how: prompt an AI coding agent, drive development with Robot Framework tests, and ship a Like feature across backend + frontend on local machines. No hype, no black box, just practical patterns for becoming an AI-ready engineer who moves faster while keeping quality under control.
AI coding tools are everywhere, but most teams still struggle with the same question: how do we use them for real engineering work without losing quality, control, or trust?
This session is a practical, hands-on walkthrough of an AI-aided development workflow that engineers can actually apply on Monday. We’ll use a local
fullstack project and build a real feature together while keeping quality gates in place from start to finish.
What you’ll see
- How to frame prompts so AI produces useful, reviewable code
- How to run test-first development with Robot Framework as the safety net
- How to implement a feature incrementally across backend and frontend
- How to validate each step instead of “hoping” generated code is correct
- How to keep human engineering judgment at the center of the process
Core idea
AI does not replace engineering discipline. It amplifies disciplined engineers. In this talk, AI is treated as a coding collaborator: fast, helpful, and fallible. Robot Framework tests are the contract that keeps implementation honest. Together, they create a workflow where speed and quality reinforce each other instead of competing.
Why this matters
Many teams experiment with AI coding but get stuck in one of two extremes:
- blind trust (“it compiles, ship it”)
- total skepticism (“AI output is unusable”)
We’ll show a middle path: high-velocity delivery with explicit quality controls.
You’ll leave with:
- A concrete prompt pattern for AI-assisted implementation
- A repeatable test-first loop for validating generated code
- Practical techniques for reducing risk and rework
- A clearer model of what it means to be an AI-ready engineer
If you care about modern software delivery, this session is about building the skills that matter now: collaborating with AI effectively while keeping engineering standards high.
Categorize / Tags:ATDD, AI
Is this suitable for ..?: Beginner RF User, Intermediate RF User, Advanced RF User Describe your intended audience:This talk is for everyone in a software development organization — from developers and testers to tech leads and C-level decision makers.
AI-assisted development is reshaping how software is built, tested, and deployed. When used right, it’s a superpower that boosts productivity without compromising quality.
Whether you write code, design processes, or shape strategy, you’ll learn how Acceptance Test-Driven Development (ATDD) with Robot Framework creates the structure and guard rails that make AI tools safe, reliable, and truly useful.
Ismo Aro is a partner and CTO at NorthCode. His professional focus is in modernizing the ways of how companies are working.