AI Poland 2026 · Speaker
Greg Radzio
CTO, Lowgular | Poland
Talk
Your Agents Are Fast. Your Company Isn't.
Most companies now have an AI strategy, but few can explain how it becomes measurable business value. After almost a year implementing AI agents inside a large enterprise digital-platform project, I have seen how making each specialist faster merely moves the bottleneck to the next human. Without a surrounding system, agents produce plausible-looking slop, growing review queues and more decisions than an organisation can safely process. This talk follows our progression from traces and reusable skills to purpose-built tools, deterministic verification and governed workflows, while examining how engineering, design, QA, product management must change around them. The difference between ten times more output and a genuine tenfold improvement is not the model, but the harness and operating system built around it.
About Greg Radzio
After 20 years in engineering — including CTO roles where I scaled teams, rescued legacy codebases, and watched good developers drown in process — I stopped asking “how do we ship faster” and started asking “what if the system shipped for us.”
That’s what I’m building now.
At Lowgular I design agentic engineering systems — not AI tools bolted onto existing workflows, but end-to-end pipelines where agents prototype, implement, test, and validate features while developers govern the system that does it.
The architecture looks like this:
This is the shift I’ve seen coming since Cobiro — where I took a codebase from maintainability rank D to A in 6 months while scaling the team from 3 to 30. Back then the governance layer was a human bottleneck. Now it’s the thing that makes autonomous delivery safe.
I work as Fractional CTO — embedded in your team for 90 days, designing and shipping the system, then handing it over to run.
If your team has AI tools but delivery hasn’t changed — that’s the problem I solve.
That’s what I’m building now.
At Lowgular I design agentic engineering systems — not AI tools bolted onto existing workflows, but end-to-end pipelines where agents prototype, implement, test, and validate features while developers govern the system that does it.
The architecture looks like this:
- Knowledge bases that give agents context: design system, architecture decisions, coding standards, test scenarios
- Agent workflows that execute feature delivery from prototype to PR
- Deterministic governance layers that catch drift before it reaches production: sensors, evals, visual regression, static analysis
- Feedback loops that make the system smarter over time — not the developer writing more code
This is the shift I’ve seen coming since Cobiro — where I took a codebase from maintainability rank D to A in 6 months while scaling the team from 3 to 30. Back then the governance layer was a human bottleneck. Now it’s the thing that makes autonomous delivery safe.
I work as Fractional CTO — embedded in your team for 90 days, designing and shipping the system, then handing it over to run.
If your team has AI tools but delivery hasn’t changed — that’s the problem I solve.
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