LangGraph : Build your first Reflection Agent (Theory)

Description

🚀 Welcome to one of the MOST IMPORTANT lectures in the LangGraph series! In this lecture, we move beyond static and deterministic workflows and introduce the real power of AI agents by integrating LLMs (Large Language Models) into LangGraph workflows. Until now, our workflows were rule-based systems where every routing logic, evaluation logic, and decision-making process was manually designed by humans. Even though the workflows were complex, they were still deterministic. 🔥 In this session, everything changes. We introduce: ✅ Intelligent Workflows ✅ AI-based Reasoning ✅ Reflection Agents ✅ Self-Improving Systems ✅ Iterative Reasoning Workflows You will understand how LangGraph evolves from: ➡️ Static Workflow → Intelligent Agent Workflow ➡️ Deterministic Logic → Probabilistic Reasoning ➡️ Human Rules → LLM-driven Decision Making 📌 Topics Covered: * What is a deterministic workflow? * Why traditional workflows are not intelligent * Introduction to reflection agents * How LLMs introduce reasoning into workflows * Generate → Evaluate → Improve cycle * Self-reflection and iterative improvement * Designing intelligent agents using LangGraph * Understanding probabilistic workflows * Building nodes with `LLM.invoke()` * Generate Node * Evaluate Node * Improve Node * Decision Functions in LangGraph 🧠 Reflection Agent Concept: The agent generates an answer, evaluates its own response, reflects on mistakes, improves the answer, and iterates until a better output is achieved. This lecture lays the foundation for building advanced AI agents using LangGraph. 📌 In the next lecture, we will implement our FIRST Reflection Agent step-by-step inside Jupyter Notebook using LangGraph and LLMs. #LangGraph #AIAgents #LLM #GenerativeAI #ArtificialIntelligence #LangChain #MachineLearning #Python #ReflectionAgent #AgenticAI #AIEngineering #WorkflowAutomation