Agentic AI represents a paradigm shift from simple prompt-response systems to autonomous agents capable of planning, reasoning, and executing multi-step tasks. LangGraph provides a powerful framework for building these systems using directed graphs with persistent state.

Why Agents Need State

Traditional LLM calls are stateless — each request is independent. But real-world tasks require memory, backtracking, and iterative refinement. LangGraph solves this by modeling agent workflows as state machines.

Key Architecture Patterns

• ReAct Loop: The agent reasons about the next action, executes it, observes the result, and repeats
• Plan-and-Execute: A planner creates a high-level plan, then an executor carries out each step
• Multi-Agent Collaboration: Multiple specialized agents coordinate through a shared message bus

Getting Started

Define your graph nodes (tools, LLM calls, conditionals) and edges (transitions). LangGraph handles checkpointing, human-in-the-loop interrupts, and streaming out of the box.

The future of AI isn’t just smarter models — it’s smarter architectures that let models reason and act autonomously.