Feb 9
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Welcome to Class 16 of Fusionpact’s AI/ML Daily Sessions. In this session, we explore LangGraph and understand how it enables stateful, reliable, and production-ready AI agent workflows. Traditional LLM pipelines like simple prompt → response or linear LangChain flows struggle with complex multi-step reasoning, retries, branching logic, and state management. As workflows grow, debugging becomes difficult and systems become unreliable. LangGraph solves these problems by introducing graph-based orchestration for AI agents. Instead of linear chains, we design workflows as graphs where: - Nodes represent actions - Edges represent execution flow - State tracks progress - Loops, retries, and branches are supported natively This makes LangGraph ideal for building enterprise-grade AI systems. Topics covered in this class: - What is LangGraph - Why traditional LangChain pipelines fail for complex agents - Linear vs graph-based workflows - Core LangGraph concepts: - Nodes - Edges - State - Stateful execution and memory handling - Looping, retries, and conditional branching - Deterministic execution paths - Think → Act → Observe → Retry agent loops - Observability and debugging benefits - Reliable multi-step reasoning systems - Production-ready orchestration - Real-world applications: - Search assistants - Data analysis agents - Enterprise workflow automation - Rack-based AI systems - Complex decision-making agents Key takeaway: LangGraph is not optional for complex AI agents. It is essential for building scalable, fault-tolerant, and production-grade AI workflows. This session is perfect for: - AI engineers - LangChain developers - Agentic AI builders - Backend developers - Anyone building multi-step LLM systems Subscribe to Fusionpact for daily AI/ML sessions focused on real-world AI architecture and production systems. #LangGraph #AIAgents #AgenticAi #LangChain #llm #AIEngineering #Fusionpact