How to Build AI Agents with Tool Calling and Memory Using Spring AI

Large Language Models can generate text but cannot interact with live systems or databases on their own, limiting their usefulness in real applications. AI agents bridge this gap by enabling LLMs to decide which actions to take and invoke application functions — such as checking order status or cancelling a transaction — through a mechanism called tool calling. Unlike basic retrieval-augmented generation (RAG), which fetches static information, tool calling allows models to execute live operations across databases, REST APIs, payment systems, and internal workflows. Spring AI provides abstractions that simplify exposing these application capabilities to language models, enabling developers to build autonomous, multi-step workflows. The article outlines how this architecture transforms an LLM from a text generator into an intelligent interface for enterprise applications.