Vasyl Borovyk

About the program

A hands-on live workshop on making AI agent code trustworthy. The problem is not the model but the missing verification between agent steps: without a pass/fail signal, the developer remains the only reviewer. Loop Engineering is a system of checks the agent runs on its own. Format: one day, two parts. Part one is theory: where feedback loops come from, context as a resource, a deterministic loop around the model, the six verification phases, subagents, TDD with agents, Definition of Done and the self-improvement loop. Part two is live practice on the url-shortener training project: the housekeeper loop, the Ralph loop with safety brakes, a feature built through TDD, eval grading of skills via agent-as-judge and deployment monitoring. Total volume: about 6 hours including participants' Q&A. Participants received access to the recording, the project repository with ready-made verification scripts and environment setup instructions.

Instruction language: Ukrainian

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Skills

• Artificial Intelligence (AI)
• Test-Driven Development (TDD)
• Software Quality Assurance
• Test Automation
• Claude Code
• AI Agents
• Continuous Integration (CI)

Curriculum

1. 01Part 1 - Theory

   • Why agents are hard to trust and where feedback loops come from
   • Evolution of approaches: autocomplete, prompt, harness, loop
   • Context as a resource: lost in the middle and context rot
   • A deterministic loop around the model: Ralph loop, Goal, model-as-judge
   • The six phases of the verification loop
   • Subagents and agent teams inside the loop
   • TDD with three agents
   • Definition of Done and the self-improvement loop

2. 02Part 2 - Practice on the url-shortener project

   • Environment setup: doctor, verify, dev
   • The housekeeper loop: zero-token checks
   • The Ralph loop with safety brakes
   • Implementing a feature through TDD
   • Eval grading of skill behaviour: agent-as-judge
   • Deployment monitoring: Playwright, logs, metrics
   • Q&A: participants' questions