A language model is fluent, and sometimes it is wrong and sure. In a chat that costs one bad answer. In an agent with a memory it costs more: the agent stores the mistake, reasons from it, and repeats it tomorrow. Retrieval cannot tell which of two contradictory notes is right, and a second model asked to judge only predicts a plausible verdict. The fix is not new. Before deep learning, expert systems such as MYCIN and XCON kept knowledge as rules, applied them the same way every time, and answered "why?" and "how?". They failed at one thing: getting knowledge in, because every rule was typed by hand. LLMs are very good at exactly that. Neurosymbolic AI puts the two halves back together. The talk follows that reunion through one running example: an assistant whose LLM "remembers" a meeting in Berlin while its calendar has the same person in Lisbon at the same hour. We cover deduction, induction and abduction, and why LLMs are good at the last; reasoning over a knowledge graph with Prolog and Datalog; and why higher-order logic programming (λProlog) fits agents well, with types that reject malformed LLM output, relations as values, scoped what-if assumptions, rules about rules, and proofs that a small kernel can check. We end with a working agent whose LLM writes λProlog behind six guards, and with honest numbers on what the logic layer catches and what it cannot. For AI and agent engineers who know Python and LLMs. No background in logic or mathematics is assumed.

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.

During the talk, we will explore why a simple RAG approach is no longer sufficient for organizations with multiple data sources and how modern AI systems are evolving — from classic RAG to Deep Search, hybrid search, and Knowledge Graphs as a layer of corporate memory. We will also look at how to combine unstructured documents, tabular data, databases, internal wikis, chats, and business entities into a unified system where AI can find relevant sources, build a search path, explain relationships between facts, and provide more accurate and verifiable answers.

We'll explore the key challenges of developing RAG-systems for the Ukrainian language. We'll discuss evaluation methods for such systems in specific domain areas, like consulting citizens on public services, and uncover practical insights and common "gotchas" that arise during implementation.