Core Insights - Eclipse Foundation has launched the Agent Definition Language (ADL) within its open-source platform Eclipse LMOS, allowing users to define AI behaviors without coding [2] - LMOS aims to reconstruct the development and operation chain of enterprise-level AI agents in a unified and open manner, challenging proprietary platforms and Python-centric enterprise AI tech stacks [2][4] - The project follows a "land first, open source later" approach, initially developed from Deutsche Telekom's production-level practices in traditional cloud-native architecture [2][6] Group 1: Project Overview - ADL is a structured, model-agnostic description method that simplifies the definition of AI behaviors [2] - LMOS is designed to run natively on Kubernetes/Istio, targeting the JVM ecosystem and facilitating the integration of AI capabilities into existing infrastructures [2][4] - The project was led by Arun Joseph, who aimed to deploy AI capabilities across 10 European countries for Deutsche Telekom [6] Group 2: Technical Implementation - The platform utilizes Kubernetes as its foundation, deploying agents as microservices and enhancing them with custom resources for declarative management and observability [7] - Eclipse LMOS integrates seamlessly with existing DevOps processes and tools, allowing for minimal migration costs when introducing AI agents into production systems [7][8] - The initial deployment of agents has resulted in significant operational efficiencies, including a 38% reduction in human handovers and processing approximately 4.5 million conversations monthly [9][10] Group 3: Development Efficiency - The development cycle for creating new agents has been significantly reduced, with initial deployments taking one month, later decreasing to as little as one to two days [10] - A small team consisting of one data scientist and one engineer can rapidly iterate from idea to production deployment, showcasing cost advantages [10][12] - The dual strategy of LMOS includes both the open-source platform and the ADL, which allows business and engineering teams to collaboratively define agent behaviors [12][17] Group 4: Market Positioning - Eclipse LMOS positions itself between the agile, open-source Python ecosystem and the robust, mature JVM world, aiming to bring AI agents into familiar enterprise infrastructures [22] - The platform is designed to enable organizations to build scalable, intelligent, and transparent agent systems without the need to overhaul existing technologies [22] - Eclipse Foundation's executive director emphasizes the need for open-source solutions to replace proprietary products in the agentic AI space [22]

近期，Eclipse 基金会宣布在其开源平台 Eclipse LMOS 中推出"代理定义语言"（ADL）。这是一种结构化、与模型无关的描述方式，允许用户无需编写代 码即可定义 AI 行为。 据 Eclipse 表示，ADL 将成为智能体计算平台 LMOS 的核心组件。LMOS 这个项目从一开始就瞄准在 Kubernetes / Istio 上原生运行，服务 JVM 生态，旨 在用统一、开放的方式重构企业级 AI 代理的开发与运维链路。同时也对标专有平台与以 Python 为主的企业 AI 技术栈；这也意味着对长期主导企业 AI 的 闭源替代方案发起正面挑战。 值得注意的是，LMOS 这个项目采取"先落地、后开源"的路径：其前身是德国电信在传统云原生架构中的生产级实践，之后才在 Eclipse 基金会中完成孵 化。 完整项目开源地址：https://github.com/eclipse-lmos 1 技术收敛：在熟悉的技能栈上做 AI 过去十年，企业在云端应用上已形成一套行之有效的工程范式：不再编写单体应用，而是尽量拆分为微服务，让不同团队负责不同部分，并且便于装配组 合。同时，将一切打包进容器，方便跨环境迁移 ...