From Conventional LLMs to Reasoning Models to Agents

Title: From Conventional LLMs to Reasoning Models to Agents Date: August 19, 2026 Duration: 1HR ABSTRACT Many of us have been using and experimenting with large language models (LLMs) for several years. Then, in 2025, new techniques for training reasoning models gave LLMs a noticeable boost in problem-solving capabilities. Since then, newer generations of LLMs have been substantially more useful for complex tasks such as math and coding. For example, in software development, the progression over the years has been from simple code completion to planning, implementing, testing, and debugging entire features and code bases. Today, reasoning models, which can be viewed as an evolution of conventional LLMs, are also at the center of many agent frameworks. This talk will explain how reasoning models differ from conventional LLMs, with a particular focus on inference scaling and training techniques. It will also introduce the main components of AI agents and show how reasoning models serve as the engine that enables agents to plan, use tools, evaluate intermediate results, and recover from mistakes. SPEAKER Sebastian Raschka Founder, Principal AI & LLM Research Engineer, RAIR Labs MODERATOR Marlene Mhangami Senior Developer Advocate, Microsoft