Queue-free analytics isn’t about AI simply generating charts faster. It’s a new way of interacting with business data, where insights become accessible through conversation, dashboards are created on demand, and AI agents do more than just show what happened — they independently explore the data, explain the reasons behind it, and help make better decisions.

“Yep, thank you — you’re absolutely right! ? I’ll rewrite it so it actually sounds like a human wrote it. Here’s the updated version: natural, conversational, and free of corporate jargon.” Tired of reading stuff like this? Same. Fluff, fluff, and more fluff. Answers to questions nobody asked. You read it and can’t tell whether you’re missing something or AI wrote the whole thing. In this talk — written entirely by me — I’ll break down the AI policies we introduced in our organization and explain how we use AI to make communication simpler, not more complicated.

Nowadays, everyone is creating AI Assistants to help us in our day-to-day activities and "vibe coding" nice and shiny demo apps. However, when you try to bring AI Assistants to life in production—especially to be used by the citizens of a Gov Portal—you realize that creating the AI Agent itself is only 10% of the effort required. The other 90% is spent on satisfying cross-cutting concerns and different stakeholder concerns. This talk will be based on my own experience of putting citizen AI Assistants for a Gov Portal (built on top of an AI Agentic Platform) into production. I'll talk about the challenges we faced, concerns we received from different groups of stakeholders, important NFRs and quality attributes of the solution, and possible solutions for these challenges. This talk will give useful tips, provide a wider view on real-world challenges, and improve critical thinking. This talk will be interesting for diff Roles to get a wider view on real world challenges and to improve critical thinking. Amongst other topics I want to talk about (not in order for now): - Naive implementation of Chatbot -vs- scalable and mature implementation - Benefits and drawbacks of using AI Agentic Platform -vs- custom implementation - Choice of Public LLM vs Private LLM - Performance and quality challenges - Infrastructure - Costs - Error handling - UI/UX - Guardrails and protection against misuse and DDOS attacks - Observability - Multilingual support - Testing and evaluation framework - PII data handling - and more...

Today, AI can answer questions, search for information, and complete tasks. But how do you turn it from just another chatbot into a true extension of a brand? Using Mushroom Hennadiiovych, Silpo’s AI assistant, as a case study, I’ll share how we built a product that brings together modern LLMs, product thinking, and a distinctive brand personality. We’ll explore the journey from idea to launch, the solution architecture, the trade-offs, the mistakes we made, and the lessons that can help anyone building AI products.

I want to talk about real AI adoption — the untold problems we rarely hear in LinkedIn success stories — drawn from Temabit’s experience rolling out AI across the delivery process. First, the bottlenecks and friction on the ground: how teams push back on change, what finally leads to acceptance, and what that actually costs in time, process, and culture. Then, why giving developers a Claude Code license isn’t enough — and why AI SDLC matters when the goal is adoption that scales beyond one power user across the full delivery chain. Finally, Temabit’s journey from chaotic agents to SDD and then to AI SDLC, with key problems at each stage — and what comes next, including the problems still being worked through.

We already live in a world where AI agents write code, analyze documents, and perform increasingly complex tasks. But can they do more than answer questions? Can they actually save us time in our everyday lives? In this talk, I'll show how we designed the architecture of an MCP server that enables AI agents to securely interact with Silpo's services: finding products, building a shopping cart, and performing authorized actions on behalf of a customer. We'll explore: - how MCP for real-world consumer scenarios differs from MCP for developers; - how to provide secure access to personal data through AI; - the architectural challenges of running MCP in production; - whether websites and mobile apps will remain the primary way users interact with digital services; - whether natural language will become the next interface for e-commerce. Using Silpo MCP as a real-world case study, we'll see how AI agents move beyond answering questions and start taking meaningful actions.

With Agentic PDLC, the agents no longer just writes or reviews code — it forms hypotheses and tests them in production. How do you re-architect for autonomous agentic loops that run 24/7 on systems serving millions of users? Drawing on enterprise experience, we'll break down how to move from human control to event-driven governance and explicit guardrails for agents.

Agentic engineering is maturing. We are moving beyond “ask an agent to write some code” toward factories — pipelines where a team of AI agents takes a project through the entire lifecycle: requirements → specifications → tests → implementation → adversarial review → quality evidence. In this talk, we’ll break down what such a factory actually consists of: an orchestrator and specialized sub-agents, deterministic quality gates, end-to-end traceability from requirements to tests, and verification loops that replace “it seems to work” with measurable evidence. The main challenge of autonomous development is not getting agents to write code. It is preventing the factory from confidently reporting success when the result is actually wrong. We’ll examine common mechanisms of pipeline self-deception: “soft” rules in prompts that disappear under the pressure of a real-world project; checks that silently pass when required evidence is missing; and requirements whose acceptance criteria were never implemented as executable checks. I’ll illustrate these problems with real-world artifacts, including a case where the factory passed every gate “green” yet produced a product that failed to meet a key requirement — and what it took to make the exact same run correctly turn red. A separate part of the talk will focus on self-improvement: how to build a loop where every discovered failure becomes a new gate that future projects can no longer bypass. We’ll cover three-state verification semantics (PASS / NOT-EARNED / FAIL), a process-defect registry, and a library of lessons inherited by every new project. Everything will be grounded in real artifacts: commits, metrics, and measurable results. What we’ll cover: • Anatomy of an agentic factory: orchestrator, sub-agents, deterministic gates, and end-to-end traceability • Pipeline self-deception mechanisms — and the forensics of a real “false done” • Maker ≠ checker in practice: adversarial review that actually catches defects • The self-improvement loop: correction → retrospective → new gate → lesson for the next project • What it costs: tokens, time, and where the factory still loses to humans Who is this talk for? Engineers and tech leads who already use AI agents in software development and want to move from “it works sometimes” to a predictable, evidence-driven engineering pipeline with verifiable quality.

Not everyone supports the new strategy. The AI initiative is being sabotaged. The architect is asking for two more quarters. Marketing wants to launch as early as next week. Sound familiar? The problem is that we often expect other people to follow the same logic we do. But in the product business, different “species” work side by side: those who champion people; those who champion experiments; those who champion stability; those who champion results. And until we learn to see these differences, any strategy risks remaining nothing more than a pretty slide. Using examples from product teams and AI transformations, we’ll explore how the framework of competing values works and how to use it for influence, negotiation, and change management. 📌 Key takeaways: Every stakeholder defends a value that is rational for them. “Everyone is right—but only partially.” How to read motives behind words and objections. Why the strongest products know how to switch between languages of influence. How to reduce resistance to change without pressure. How to build support around product decisions.

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.