How to Build a Single LLM AI Agent with Kong AI Gateway and LangGraph

In part two of a series on implementing AI Agents with Kong AI Gateway, the discussion focuses on LangGraph fundamentals and the potential of integrating frameworks to enhance AI Agent complexity, security, and integration with external systems. The post explores LangGraph, a low-level, controllable framework for AI Agents, and compares it to LangChain, which provides a standard interface for interacting with LLMs. A basic LangGraph application is demonstrated, highlighting how it structures agent workflows using nodes and edges within a graph. The text explains the process of setting up an observability layer with tools like Loki, Prometheus, and Grafana to monitor AI Gateway activities. Additionally, the concept of Tools and Function Calling is introduced, allowing AI Agents to invoke external functions and APIs, exemplified through OpenAI's built-in tools. The guide further explains how to enhance AI Agents with reasoning loops using LangGraph, while integrating external functions protected by Kong AI Gateway. The post concludes by noting the use of Kong's plugins for better security and management of API Keys, and introduces the next step in the series, which will cover Semantic Routing across multiple LLM infrastructures.

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