Introduction to Agentic AI

⏱️ Estimated reading time: 5 minutes

Agentic AI systems are applications that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike traditional programs that follow a fixed set of instructions, agentic systems exhibit a degree of autonomy and can adapt their behavior based on interactions and new information.

Key characteristics of agentic AI systems include: - Goal-Oriented: They are designed to achieve specific objectives. - Interactive: They can communicate with users or other systems and respond to inputs. - Autonomous: They can operate without constant human intervention, making decisions and taking actions independently. - Perceptive: They can process information from their environment (e.g., user queries, tool outputs, data sources). - Adaptive: They can learn from interactions and modify their behavior over time (though this tutorial focuses more on explicit state management for adaptability).

Building robust agentic AI requires a combination of powerful language models, tools to interact with the external world, and a framework to orchestrate complex workflows. This is where LangChain and LangGraph come into play.

• LangChain provides the foundational building blocks for creating applications powered by language models. It offers components for managing models, prompts, tools, memory, and creating chains of operations. We\'ll use LangChain to define the individual capabilities of our agent (e.g., what tools it can use, how it processes information).
• LangGraph is built on top of LangChain and allows you to construct sophisticated, stateful agentic systems as graphs. It excels at managing complex flows of control, enabling cycles, human-in-the-loop interactions, and persistent state. We\'ll use LangGraph to define the overall decision-making process and workflow of our agent.

This tutorial will guide you through designing agentic AI systems by leveraging the strengths of both LangChain and LangGraph.