Based on 20 public job descriptions each from mainland China, the United States, and Tokyo, this study maps the hiring stack for AI Application, Agent, and RAG engineers and gives a Tokyo-focused learning and portfolio roadmap.
The Landscape at a Glance If you are a Java backend engineer trying to understand the current standardization landscape for AI agents quickly, use the following correspondences: Communication/Configuration Type AI Agent Standard Java Analogy Originator Governing Body Adoption Status Agent ↔ tools/data MCP JDBC Anthropic AAIF (Linux Foundation) ✅ De facto standard Agent ↔ Agent A2A RMI / gRPC Google Linux Foundation ✅ Rapidly being adopted Project-rule configuration AGENTS.md application.yml OpenAI AAIF (Linux Foundation) ✅ De facto standard Reusable capability package SKILL.md Maven Plugin Anthropic agentskills.io (open standard) ✅ De facto standard Application framework Goose / Claude Agent SDK / ADK Spring Boot Various vendors Some governed by AAIF 🔶 Multiple competitors Microservice governance Harness Engineering system Spring Cloud — — 🔴 No standard Testing/evaluation Agent evaluation framework JUnit — — 🔴 No standard Code-quality governance Entropy management SonarQube — — 🔴 No standard The upper half marked ✅ has reached industry consensus or de facto standard status. The lower half marked 🔶 or 🔴 remains a frontier under exploration. This article primarily explains the complete picture of the upper half, then considers how the lower half may evolve. ...