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Recently, Volc Engine officially announced the launch of the enterprise AI mid-platform HiAgent 2.0, aiming to address real-world business needs and pain points in the development of Agent applications, helping enterprises achieve efficient delivery of intelligent bodies from development to operations. HiAgent 2.0 is positioned as an enterprise AI mid-platform, and compared to its predecessor HiAgent 1.0, it has expanded in four dimensions of functionality. Upwardly, HiAgent 2.0 provides more industry scenario templates and a rich plugin market, significantly lowering the barrier to building intelligent bodies; towards

A recent report from Google’s 2024 DevOps State of the Union reveals that over 75% of developers and IT professionals rely on artificial intelligence (AI) in their daily work. The survey, conducted by Google Cloud's DevOps Research and Assessment (DORA) team, involved nearly 3,000 global tech practitioners and highlights the rapid adoption of AI in the tech industry. Image source note: the image was generated by AI and provided by the authorization service Midjourney, with 76% of respondents indicating this in the report.

["Ant Group CodeFuse announces the open-source DevOps-ChatBot end-to-end AI intelligent assistant","DevOps-ChatBot is an open-source end-to-end intelligent AI assistant designed specifically for the entire lifecycle of software development","The project supports private deployment to ensure data security and high availability for specific tasks","By integrating technologies such as knowledge bases and knowledge graph enhancements, it ensures the accuracy and timeliness of generated content","The project also supports interactive code modification, compilation, and execution"]

Ant Group, in collaboration with Peking University, has released a benchmark for evaluating large language models in the DevOps field. This benchmark includes a total of 4850 multiple-choice questions across 8 categories such as planning, coding, building, testing, and releasing. The benchmark also provides detailed evaluations for AIOps tasks, showing that the score differences among various models are minimal.