Agentic AI Class 3: The PECAR Loop Architecture Explained (Perception, Reasoning, Action) скачать в хорошем качестве

Agentic AI Class 3: The PECAR Loop Architecture Explained (Perception, Reasoning, Action)

Agentic AI Class 3: The PECAR Loop Architecture Explained (Perception, Reasoning, Action) Welcome to the most important theoretical video in our Agentic AI series! In Class 3, we move from Python fundamentals to the foundations of Agentic AI. We define what an Autonomous Agent is, how it differs from a simple LLM wrapper, and—most crucially—we break down the Core Agent Architecture. Understanding this structure (the PECAR Loop) is the key to building robust, self-correcting agents. If you want to understand the why behind the code, this is the video. Key Concepts Covered: What is Agentic AI? Defining Autonomy, Goal-Driven behavior, and Environment Interaction. Agent vs. LLM: A clear breakdown of the difference in Logic, Action, and the ability for Reflection. The Core PECAR Loop: The continuous cycle of Perception, Environment, Context, Action, and Reflection that drives every modern agent. Key Architectural Components: The role of Short-term Memory (Context Window), Long-term Memory (Vector Databases), and Episodic Memory. Tool Use (Function Calling): A recap on how the Agent uses your defined Python functions to interact with the external world (APIs).