A meeting point for engineering leaders who are directly responsible for AI adoption at system level. The agenda combines two expert talks with moderated discussions in small groups. Senior-level conversations, off-recording discussions.
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Registration, drinks & snacks
Software engineering today requires AI-native architectural practices: evolvable contexts, structured collaboration between humans and AI agents, and governance that keeps systems reliable as they learn and change.
This session explores what happens to engineering systems when AI becomes part of the architecture itself.
We’ll look at what could be a natural next step: AI-native architecture as an engineering capability.
Ivan Padabed
Cloud Platform Architect
Catered food, drinks, networking
Catered food, drinks, networking
Six practitioners will share short inputs, which will be followed by small-group discussions where participants exchange experience and compare approaches.
AI adoption doesn’t always come from top-down initiatives, it might grow organically through experimentation. Based on internal vibecoding sessions, Andrei will share what actually stuck and what failed.
👉 What patterns of AI usage have naturally emerged in your team?
When agents generate a growing share of code, teams may lose the ability to understand and explain their own systems.
👉 Should we treat this knowledge debt as a critical risk to manage, or accept it as a black box and compensate with stronger testing?
Code generation became cheap, so the main bottleneck now is decision-making. Unclear priorities, fragmented context, and slow decisions are exposed under speed.
👉 Can your current decision-making model operate effectively in a world of fast and cheap execution?
Executives see AI as a magic wand, while engineers see slop-generating crap. Both perspectives are valid, but this tension stalls adoption.
👉 How do you align expectations and operate effectively if you’re the tech manager caught in the middle?
The first step in AI adoption was teaching developers how to use new tools. The next step is more structural: centralizing AI-related skills, embedding AI into workflows, and making reasoning itself part of the process.
👉 How do you turn AI usage from an individual habit into a system-level capability?
Andrei Lapanik, Egor Miasnikov, Dmitry Volokh, Vadzim Zhuk, Aleksey Shafransky
More time to connect over food and drinks.
Talk to peers and get more clarity on how to design AI-native systems, how to adopt it in organizations, and learn how others are doing it.
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TechSpot is organized by On The Spot Development. We build offshore development centers in Poland for global tech companies and offer local engineers the opportunity to join them. See open positions below.
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