This talk demonstrates practical approaches to unified observability, where metrics, logs, traces, and profiles are integrated for rapid diagnostics in distributed systems. We will cover data correlation techniques using trace IDs and labels to enable instant navigation from errors to specific spans, setting up continuous profiling for preview environments, using flame charts for performance analysis, and leveraging dependency maps and service graphs to visualize architecture. Special attention is given to AI-specific aspects: applying AI assistants to automate root cause analysis and implementing AI Evals for systematic evaluation of the quality, correctness, and reliability of AI systems.

This session explores the evolution from simple LLM chains to robust cognitive architectures, focusing on deploying stateful agents within your own perimeter (On-Premise). We will compare modern orchestration frameworks, contrasting Google ADK (Agent Development Kit) for structured, model-agnostic agent design against LangGraph for granular state control and CrewAI for multi-agent role-playing. A key focus will be the Model Context Protocol (MCP) — the new standard for connecting agents to internal data and tools without vendor lock-in. We will demonstrate how to build a flexible runtime using Vercel AI SDK Core (deployed in Docker) to serve these agents, ensuring full independence from cloud providers. Finally, we will cover the AgentOps stack, detailing how to implement self-hosted observability (via Langfuse) and access policies to safely manage autonomous systems in production.

Updating core dependencies is often postponed for years due to the fear of “breaking everything,” causing the risk to grow exponentially. Andrii will present a workflow for jumping across multiple versions: AI analyzes breaking changes and generates transformation rules, AST-based tools handle the routine work, and technical debt becomes a manageable process.

When a system consists of dozens of independent repositories, a classic monorepo is not always the right fit. Dmytro will explain how a meta-repo built with Git submodules and AI-powered tools makes it possible to preserve service autonomy while working with them as a single ecosystem. You will see real-world scenarios where AI: • connects code, contracts, and documentation • assists with automated testing and integrations • simplifies collaboration for developers, QA, and product teams • accelerates prototyping and delivery

Turbo-guide to the hottest Chrome DevTools updates for 2024–2025 that radically transform the frontend development workflow. We will dive into the integration of Gemini AI, which now acts as your personal super-analyst inside the Performance Panel. Beyond AI, we will explore enhanced performance analysis with expanded Core Web Vitals support and calibrated CPU throttling presets, helping you turn the battle with the “slippery” LCP into a clear detective-style investigation. Along with hardcore debugging, we will cover new styling and usability features such as scroll-driven animations and improved work with CSS Nesting, Autofill, and many other perks. We’ll take a closer look at MCP, because if you don’t know what MCP is — you won’t get paid in 2026.

In technology companies, AI is already going beyond the trend and influencing products, engineering, and daily business processes. Along with opportunities come challenges: working with sensitive data, security requirements, and the complexity of integration into existing systems. In such conditions, it is important to understand where AI really creates value and where it just adds noise. During the discussion, participants share their practical experience: how they implement AI in their products, adapt engineering processes, and restructure operations for new tools. This is a conversation about real pain points, opportunities, and solutions that help teams move forward.

How to build your own environment for generative AI in a matter of hours — from idea to working solution? During the presentation, the De Novo team will share their practical experience in creating a controlled environment for GenAI, talk about architecture, security approach, and real-life use cases in Ukrainian companies.

The AI revolution is in full swing... everyone understands that. But how can AI be used effectively? What does it actually offer right now? I will explain why Temabit is moving towards building AI-assisted SDLC, rather than “buy an AI license and AI will build everything,” as we see the interaction between humans, AI agents, and various AI tools. I will show the development of AI-assisted SDLC using the example of GitHub Spec Kit, which we are actively implementing in various projects. Why are we talking about a 10-20-30-40% development boost? Is this realistic? The main thing is that what we see today is definitely not the end point, everything is moving fast... The IT we have known for the last 20 years is already dead, but what is coming instead? Let's talk about this using the example of Temabit. See you at the conference!

In the age of AI, anyone can generate code — even your team lead or the baker next door. But who ensures its quality? This talk will show why TDD is more relevant than ever, how to tell a good test from a bad one, make AI write proper code from the start, and why discipline and architectural thinking save both your nerves and your projects.