Anton Arhipov from JetBrains introduces Koog, a lightweight, Kotlin-native framework for building tool-using LLM agents. This session covers the rationale for using Kotlin in AI, the architecture of Koog agents, and how its graph-based DSL enables the creation of structured, type-safe, and reproducible agent workflows, moving beyond simple prompt-chaining to sophisticated orchestration.
An in-depth guide to DSPy, a framework for programming with language models, not just prompting them. Learn its core concepts—Signatures, Modules, Adapters, and Optimizers—and see real-world examples of building robust, testable, and transferable AI applications for the enterprise.
Martin Keen explains the evolution of prompt engineering from an art to a software engineering discipline. He introduces LangChain and Prompt Declaration Language (PDL) as tools to manage the probabilistic nature of LLMs, ensuring reliable, structured JSON output through concepts like contracts, control loops, and observability.