AWS 发布教程：用 Amazon Bedrock 与 Spring AI 构建 Java 自主智能体
Build autonomous AI agents with Amazon Bedrock and Spring AI
Java 团队一直被智能体教程偏 Python 这件事卡着，这篇直接给了 Spring AI 加 Bedrock 的完整路线，从工具调用一路讲到多智能体编排和 AgentCore 部署。手里有 Java 存量系统的后端开发者可以直接照着搭。
AWS 机器学习博客发布一篇面向 Java 开发者的教程，演示如何用 Spring AI 框架结合 Amazon Bedrock 构建生产级自主智能体。教程覆盖四个核心环节：工具调用、对话记忆、韧性模式和多智能体编排，最后将智能体部署到 Amazon Bedrock AgentCore 上运行。
文章的技术路径是让 Spring AI 提供 Java 侧的抽象层，负责管理模型调用与会话状态，工具调用部分把外部 API 封装成模型可调用的函数；韧性模式则处理超时、重试与降级，避免生产环境中的请求失败。
对大量存量 Java 企业系统来说，这是不切换 Python 技术栈也能搭智能体的方案，同时可借助 Bedrock 托管的模型能力，降低自建基础设施的运维负担。
Build autonomous AI agents with Amazon Bedrock and Spring AI
Learn how Java developers can build production-ready autonomous AI agents using Amazon Bedrock and Spring AI. This post walks through tool calling, conversation memory, resilience patterns, and multi-agent orchestration, then deploys the agent to AWS with Amazon Bedrock AgentCore.
This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include Amazon Bedrock, Spring AI, AI Agent, Java.
Primary source verification: published directly by AWS Machine Learning Blog with first-party authority.