Nov 24-27

AI is transforming industries—and document processing is no exception. Modern AI systems take extraction to a new level of precision, drastically reduce training effort, and enable idea-to-production cycles within a single business day.
In this session, Eduard Shlepetskyy shows how organizations can move beyond templates and rule-based systems toward adaptive, learning-driven document intelligence. Through a live demonstration, attendees will see how structured data can be extracted from complex, unstructured documents in minutes—without lengthy setup or heavy IT projects.
The talk also addresses enterprise essentials such as accuracy, governance, and human validation, providing a practical framework for scaling AI-powered document processing sustainably.

This session examines the emergence of autonomous AI agents that manage executive tasks such as scheduling, coordination, and routine decision-making. In this session, Aistis Raudys explains how these AI assistants can enhance organizational efficiency and redefine leadership workflows.

As AI agents become more capable, many engineering teams embed increasingly complex business logic directly into system prompts. While effective in prototypes, this approach creates invisible technical debt: nondeterministic failures, brittle model upgrades, and regression cycles that are difficult to reason about or validate.
In this technical case study, Stanko Kuveljić present the refactoring of a production scheduling agent from prompt-encoded logic to a deterministic, state-driven architecture. By separating model reasoning from application state transitions, the team introduced explicit invariant enforcement, structured observability, and behavioral testing gates across the continuous integration and continuous delivery (CI/CD) pipeline.
Key topics include:
Designing state graphs that enforce deterministic routing for business-critical operations
Implementing behavioral validation pipelines that test agent tool calls and state mutations instead of conversational phrasing
Reducing prompt surface area to improve traceability, reproducibility, and model upgrade safety
Introducing deployment gates that reduced manual regression validation effort in production
Attendees will leave with a reference architecture, a behavioral testing checklist, and a step-by-step approach for migrating prompt-driven agent systems toward bounded, production-grade reliability.