Implementing AI into a government service with 23+ million users is a journey of continuous product discoveries and challenges. In this talk, I will share the real-world experience of how the "Diia" ecosystem is transitioning from a classic Digital State (where users search for the required services themselves) to an Agentic State (where AI proactively fulfills the user's intent). What we will cover: - Product Discovery and Paradigm Shift: The transition from Digital State to Agentic State. Why traditional interfaces have reached their limits and how we validated the need for proactive AI solutions. - AI in Support as the First Big Step: How we automated 90% of requests without a drop in quality (CSAT). Soft AI UX: why people struggle with prompting and how we guide them using hybrid interfaces. - The Upskill Case and Team Transformation: We didn't fire a single operator. How we built internal AI tools for the team, turning yesterday's support agents into AI trainers. - Deep Dive into Diia.AI on the Portal: The launch of the world's first agentic service at the government level. How our RAG architecture works, how we architecturally protect personal data (PII) from entering the LLM, and how we repel jailbreak attempts.

What happens when a large language model becomes the entry point to government services that operate under real-world load and in the context of an information war? In such an architecture, any request may be not only incorrect but also intentionally manipulative — and standard AI safety solutions prove far less reliable than they appear in laboratory benchmarks. In this talk, I will share how we built a custom guardrail module for Diia.AI after encountering the limitations of off-the-shelf filters and the high cost of the LLM-as-a-Judge approach. Instead of validating every request with a large model, we designed a cascade security architecture: fast ML classifiers filter out most of the traffic, while the LLM is invoked only where deeper contextual analysis is truly required. This talk is not about perfect models, but about trade-offs, constraints, and practical decisions that must be made when an AI system operates not on a laptop, but within a national-scale service.

This presentation is designed for engineers, architects, and technical leaders who want not only to use large language models, but also to understand how they work, how to interact with them through APIs, what challenges arise when building RAG systems, and how to solve them.

AI development hits everyone, so hit us. Everybody wants AI agents to replace regular UIs. In this talk, I will describe the evolution of our multitool AI agent, built with Node.js on top of Google Vertex AI. I’ll dive into our journey of choosing the right models and scaling development through CI/CD, TDD, and performance monitoring. Is it even possible to achieve stable results for AI projects that can hallucinate and return various responses? Interestingly, we eventually decided to remove MCP servers and Zod schema validation—technologies often considered the "standard" for these tasks. Want to know why we moved away from them? Join my session to get these insights and ask your questions live!

We will analyze what actually works in the field of autonomous LLM agents, the existing limitations and their causes, research directions aimed at extending the duration of fully autonomous task execution, explore potential architectures of fully autonomous systems, economic autonomy of agents, and where we currently stand on the path toward achieving it.

In this topic, we will go through the step by step guide how to build an AI application using PHP and large language models. We will understand how your bot can respond like ChatGPT and manage your private data without training or extensive expertise in Data Science. We will also review existing libraries and how these approaches can be utilized in your applications.

In this talk, I'll give a quick introduction to LLM and how to use it in a PHP application. I'll show some examples using the LLPhant project including a retrieval-augmented generation (RAG) system using a local LLM (Llama 3) and Elasticsearch as a vector database.