A recent technical article presents a comprehensive implementation of a Hermes-style AI agent using Java and Spring AI 2.0. The architecture integrates six key capabilities: file-based short and long-term memory, JobRunr for long-running task scheduling, dynamic Skills hot-plugging, MCP protocol for external tool access, agent-sandbox for containerized code execution, and a Code Interpreter. This approach is particularly relevant for developers building production-grade agents that require memory persistence, scheduled tasks, and safe execution of untrusted code. The use of MCP (Model Context Protocol) allows the agent to seamlessly connect with external tools and services, while the sandboxed environment ensures security. The article serves as a practical blueprint for Java developers looking to move beyond simple chatbot implementations and create agents capable of complex, multi-step workflows.
A detailed guide on implementing a Hermes-style AI agent using Java and Spring AI, covering memory, scheduling, MCP integration, and sandboxed code execution.