AI has already become part of the modern engineering landscape, but things are much more complicated in high-load production systems. GenAI works well in demos and copilots, but is it ready for real-time processing, heavy workloads, and critical production scenarios? In this panel discussion, we’ll talk about why AI has yet to become the standard for high-load architectures, where the line between ML and GenAI lies, why inference is expensive, and why FinOps is becoming a new headache for engineering teams. We’ll discuss on-prem vs. cloud for AI workloads and real-world production constraints.

A real-world-inspired architecture talk about embedding an AI agent into the operational workflow of a highload data pipeline. We walk through a cascade failure scenario: corrupted data enters the pipeline, Kafka queues get stuck, storage pressure grows, thousands of Kubernetes pods start failing and rescheduling, etcd degrades, and PostgreSQL becomes a secondary pressure point. Then we show how an agent built with AWS Bedrock AgentCore, LangChain, and MCP/Gateway could detect early signals, isolate corrupted messages, suggest human-approved fixes, protect cluster stability, and turn noisy telemetry into actionable recovery steps.

Just yesterday, AI was seen as a simple “assistant.” Today, it’s already reshaping hiring, salaries, career growth, and the role of the developer itself. Junior positions are disappearing, code generation is becoming cheaper, and companies are increasingly valuing adaptability and AI skills over years of experience. During this panel discussion, we’ll talk without rose-colored glasses: is AI really taking jobs, why senior-level experience no longer guarantees an advantage, who is winning the new AI race — engineers or prompt-native specialists — and whether software engineering itself is turning into a completely different profession. We’ll discuss what skills will actually matter for developers in the next 2–3 years, whether middle engineers will become the new juniors, and whether the Ukrainian IT market is adapting to AI-driven changes faster than the rest of the world.

What tasks should be delegated to AI right now, and what still requires human intervention? Using a streaming product as an example, we’ll discuss how AI copilots can help QA teams streamline technical routines, scale testing, accelerate releases, and free up time for people to focus on product research, UX, and complex user scenarios

Recently, Superhuman (formerly Grammarly) launched Superhuman Go, an AI assistant that works alongside you on every platform. To build it, we needed a scalable solution that supports an unlimited number of agents that dynamically shapes the user interface and looks similar across all supported desktop and mobile platforms. Join me to find out how we discovered solutions for this innovative new product.

Topics include: - Which growth challenges will define 2026. - How AI is changing the speed of MVP launches and product experiments — and why speed without strategic focus does not create sustainable growth. - Why CRO and performance marketing alone are no longer enough for scaling. - How to use AI for research, prototyping, product drafts, and faster solution launches.

This talk is about taming AI: the journey from a simple Plan Mode in Cursor/Claude to structured specification systems. We'll break down why GitHub Spec Kit turned out to be too heavyweight, how ADR (Architecture Decision Records) helps agents retain context across sessions, and why OpenSpec by Y Combinator (Fission-AI, W26) became sweet spot. The central thesis: code quality on output equals specification quality on input. Together we'll reflect on the transformation of the developer's role — from "coder" to "spec architect."

What if an engineer receives not a task, but a ready PR with context and a proposed solution? At Wix, we’re building remote agents — autonomous agents triggered by external events (Jira, Slack) that execute tasks in the background without human involvement and return results as context for developers. In this talk, I’ll cover: what remote agents are, how they’re architected, and how to integrate one into your own system. I’ll share real numbers — success and failure cases from our experience. Beyond that, the non-obvious parts: where agents break, why a spec-driven approach is critical for them to work, and what changes in team processes when part of the work is done by an agent. This talk is for those already working with AI tooling and thinking about the next step — from copilot to autonomy.

Vibecoding is one of the most effective tools for a product manager today. It not only enables the use of ready-made services to speed up workflows, but also allows you to build custom tools tailored to specific tasks, taking into account the product context and processes. This reduces unnecessary actions and repetitive manual work. In large teams, it’s about personal efficiency and workflow automation. At the R&D stage, it’s about the ability to independently build an MVP, get initial user feedback, and only then hand the solution over to development. In the talk, Maksym will explain how he uses vibecoding in his work as a product manager — from MVP development to building custom tools.