Apr 9
Loading video player...
Many explanations of agentic AI focus on the core components: models, memory, orchestration, and tools. But understanding the components is only part of the picture. In this video, we step into the next layer of understanding: the framework ecosystems that bring these components together into real systems. Rather than implementing systems directly, this video provides a structured overview of the main frameworks used today, including LangChain, CrewAI, AutoGen, LlamaIndex, Semantic Kernel, and Haystack. The goal is not to compare features superficially, but to understand the deeper question: what model of intelligence each framework assumes. Some frameworks think in graphs. Others in teams, conversations, data, plans, or pipelines. These are not just implementation choices. They are fundamentally different ways of structuring intelligent systems. In this video, we explore: - The core idea behind each framework - Their strengths and trade-offs - Real-world use cases across industries - How to approach selecting the right ecosystem depending on the system we want to build If you are working with AI agents, copilots, or autonomous systems, understanding these ecosystems is essential. It shapes not only how systems are built, but what they are capable of becoming. The key question is not which framework is best. It is what kind of intelligence you are trying to build.