AI Agent Architecture in Java: Build Autonomous Systems

As generative AI evolves, the demand for software systems that can reason, plan, and execute tasks autonomously is skyrocketing. This text-based course guides Java developers through the architectural principles and coding practices needed to build intelligent, goal-oriented AI agents. You will transition from writing standard deterministic code to designing dynamic systems where software agents make decisions, call external APIs, and manage their own state. What you'll learn: - Understand the foundational architecture of autonomous AI agents, including prompt loops, planning, memory, and actions. - Configure Large Language Models (LLMs) inside Java applications using modern integration frameworks. - Implement reasoning patterns like ReAct (Reason and Act) to allow agents to solve multi-step problems. - Design tool-calling mechanisms that enable your Java agents to interact safely with external databases and web APIs. - Manage agent memory structures, including short-term context and long-term vector database storage. - Apply robust software engineering patterns to ensure your agentic workflows are testable and maintainable. The course begins with core definitions of agentic workflows before guiding you through the implementation of planning, memory, and tool integration. You will read detailed architectural explanations and study modular, clean Java code snippets designed for modern runtime environments. This course is designed for Java developers and software architects looking to enter the world of generative AI; no prior machine learning experience is required. Start reading today to build the next generation of intelligent Java applications.