Java remains one of the most widely used programming languages, powering millions of applications from startups to enterprises. This repository contains the source code, infrastructure, and samples supporting a family of hands-on workshops that cover cloud-native Java on containers and AI agent development on AWS.
catalog.workshops.aws/java-on-aws
Learn how to build, containerize, optimize, and operate Java applications on Amazon EKS and Amazon ECS — from first container to production-grade deployment.
A core focus of the workshop is reducing startup time, image size, and resource consumption. You'll work through a progression of techniques, measuring the impact of each:
- Jib — build images directly to registry without a Dockerfile
- Custom JRE — create minimal Java runtimes with jlink
- SOCI — lazy-load container images, reducing pull times by up to 70%
- Class Data Sharing (CDS) — pre-load classes for faster startup
- AOT compilation cache (Java 25+) — ahead-of-time compiled code for reduced warmup
- GraalVM native image — compile to native executables with instant startup
- CRaC — checkpoint and restore a warmed JVM for sub-second startup
- Pod Resize — boost CPU during startup, scale down after (EKS)
The workshop includes an AI-driven performance analysis module that uses Amazon Bedrock to automate JVM diagnostics:
- Collect and analyze thread dumps automatically from running containers
- Generate flamegraphs with async-profiler
- Get AI-powered performance recommendations based on thread state, lock contention, and resource usage
- Identify bottlenecks and optimization opportunities without manual analysis
- Observability — CloudWatch Application Signals, OpenTelemetry instrumentation, service maps, logs, metrics, and traces
- Graviton/ARM64 — build multi-architecture images and deploy to AWS Graviton for up to 40% better price-performance
catalog.workshops.aws/java-spring-ai-agents
Build AI agents with Spring AI and Amazon Bedrock. This workshop covers the full journey from a simple chat application to a production-ready agent with memory, knowledge bases, tool calling, and MCP integration.
- Integrate foundation models into Java applications using Spring AI
- Implement conversation memory for stateful interactions
- Ground model responses in your own data using knowledge bases
- Enable tool calling for real-time information access
- Integrate external APIs using Model Context Protocol (MCP)
- Deploy AI agents to AWS infrastructure
catalog.workshops.aws/java-spring-ai-agentcore
Extends the Spring AI workshop with Amazon Bedrock AgentCore — an agentic platform for deploying and operating AI agents at scale. Deploy to AgentCore Runtime (serverless), add persistent memory, browser automation, sandboxed code execution, and API gateway integration.
- Deploy agents to AgentCore Runtime with session isolation and fast cold starts
- Add short-term and long-term memory with AgentCore Memory
- Automate web interactions with AgentCore Browser
- Execute code safely with AgentCore Code Interpreter
- Convert APIs into MCP-compatible tools with AgentCore Gateway
github.com/spring-ai-community/spring-ai-agentcore
An AWS-initiated, community-maintained set of Spring Boot starters that integrate Amazon Bedrock AgentCore services with Spring AI. Each module provides auto-configuration — add the dependency and configure properties, and the corresponding beans are ready to use.
spring-ai-agentcore-runtime-starter— serverless deployment to AgentCore Runtimespring-ai-agentcore-memory— conversation memory with short-term and long-term advisorsspring-ai-agentcore-browser— browser automation tools as aToolCallbackProviderspring-ai-agentcore-code-interpreter— sandboxed code execution tools as aToolCallbackProvider
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.