Kyo, a cutting-edge effect system in Scala 3, introduces a novel approach to composability through algebraic effects. This talk will present a type-safe method for structuring LLM interactions based on Kyo effects, introducing an algebra of thoughts that simplifies complex AI workflows. We’ll demonstrate how this approach enables explicit modeling of AI capabilities, simpler management of LLM context, proper resource management, and seamless integration with Kyo's comprehensive suite of effects. Additionally, we will explore how Kyo’s effect tracking can serve as a sandboxing mechanism, facilitating safe and controlled execution of code generated by LLMs. Join us to see how Kyo’s algebraic design can transform how we think about and build reliable, scalable systems for next-generation AI.