What Makes an AI Agent Actually Agentic? Building Beyond the Basics with LangGraph

The article "What Makes an AI Agent Actually Agentic? Building Beyond the Basics with LangGraph" discusses how to transform a basic workflow into a more autonomous agent using the LangGraph framework. Here's a summary of key points:

1. From Workflow to Agent:

   • A simple workflow follows predefined steps and requires users to manage context.
   • An agentic system allows for natural conversations, handles context internally, and can adapt its responses based on previous interactions.

2. Key Concepts in Building an Agentic System:

   • Autonomy: The ability of the agent to choose from a set of tools without explicit routing instructions.
   • Memory: Persistent storage that allows the system to recall past conversations and maintain context over time.
   • Resilience: Handling errors gracefully by retrying or providing intelligent error messages.

3. Implementing Autonomy:

   • Define a set of tools (functions) that the agent can call based on user input.
   • Use LangGraph's create_react_agent to allow the LLM to choose appropriate actions from these predefined tools without needing explicit routing logic.

4. Memory Mechanism:

   • Utilize LangGraph's checkpointer feature to

Read the full article at Towards AI - Medium

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