When you give an LLM satellite imagery, object detection can look impressive on high-quality images — and completely fall apart when you switch to freely available satellite data, where the entire object may be just 20 pixels across. I’ll share our journey of detecting illegal landfills in Sentinel-2 imagery and explain why the path to a working solution wasn’t simply “get more data and throw a bigger model at it.” We started by moving away from LLMs and toward specialized object detectors, and eventually ended up with an architecture where each individual feature of an object is detected by a separate agent, while the final probability is calculated using explicit mathematical rules.

No product team can keep up with every need and use case of the Defense Forces. At the same time, military units are already adapting existing digital products on their own — building companion apps and custom tools to address specific needs across their end-to-end combat workflows and accelerate enemy destruction. How can we turn this demand into a full-fledged platform capability? How can we give users a safe, standardized way to adapt digital products to their own battlefield needs? In this talk, we’ll share our approach to this challenge, walk through real-world cases, and demonstrate the core capabilities that are already in use today. We’ll also explore where this approach can go next: from adapting user interfaces to working with backend logic, data, and AI/agent capabilities — ultimately enabling users to build their own end-to-end workflows on top of a shared platform.

What is really happening to outsourcing companies in the AI era? Clients have stopped asking, “How long will this take?” — they now come in with their own estimates. They have already reassessed what we should be capable of, without us making any promises or even agreeing with those expectations. Long-term planning has broken down, and with it, the financial model and the traditional role hierarchy. This talk is about how we are navigating that shift, through two specific cases. The first: a client asked for a 10× productivity increase starting with the very next sprint. We never promised that manual work would disappear — another vendor did. The client removed 6 out of 10 engineers from our team and replaced them with a “swarm of agents.” We’ll look at how that swarm was set up, why features still failed to make it to production, and why engineers are now being brought back. The second: we made the same bet ourselves — pushing AI adoption as far as possible in an internal experiment. The result: 2.5 months instead of one, one out of four features rewritten after release, and hundreds of bugs. But there were valuable lessons. We discovered that the real delivery bottleneck was pull requests, built a PR classifier around that insight, and defined an order of operations between Claude, the classifier, and developers — along with what we call the 80% rule. And finally, we’ll address the question every outsourcing company now has to face: the same feature takes fewer billable hours at roughly the same rate. The client pays less, while we have less room to be wrong.

Building an AI agent is only half the job. The real challenge is knowing whether it performs reliably and continues to meet expectations as models, prompts, tools, and data evolve. In this session, we’ll use a practical example to show how to build effective Evals for AI agents: creating representative test cases, defining meaningful quality criteria and metrics, combining automated checks with LLM-as-a-Judge and human evaluation, comparing different agent versions, and making Evals a core part of the development and testing lifecycle.

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.