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]