Build a Multi-Agent AI Planner Using Spring Boot, Java & Spring AI
Learn How to Build an LLM-Based Planner Agent That Orchestrates Multiple AI Agents Artificial Intelligence is rapidly evolving from simple chatbots to Agentic AI systems capable of reasoning, planning, and executing complex tasks. Instead of asking a…
Chapter 3: Extend Your AI Agent with the Model Context Protocol (MCP)
At this point, our AI agent has become much more capable. It can: However, it still has one important limitation. It cannot interact with external systems. For example, consider the following requests: All of this information exists outside the LLM—in…
Chapter 2- Giving Your AI Agent a Personality and Memory with Spring AI
Give Your Agent a Personality A language model responds differently depending on the instructions it receives. Instead of allowing the model to answer every question generically, we can define its role using a System Prompt. A system prompt acts like a…
Chapter 1- Build Production-Ready AI Agents with Spring AI, and Java
Introduction Artificial Intelligence has evolved rapidly over the past few years. Initially, AI applications were limited to answering questions, summarizing text, translating languages, or generating content. These applications are commonly referred to…