原帖引用 · AI 翻译
@OfirEhrlich 我们的数据基础设施是为人类设计的，而智能体集群势必会让它不堪重负。 这次发布太精彩了。 MCP 上下文能直接接入任何智能体客户端吗，还是有推荐的配置方式？
我目前在整个行业看到的情况是，AI 应用的最大瓶颈不是模型能力，而是数据基础设施。
今天，我们推出 Eon Yukon，扩展 Eon Data Foundation。Eon Yukon 是一款专为改变这一现状而打造的新型无服务器数据引擎。
AI 智能体需要新鲜且具有上下文信息的数据才能采取行动。
专用模型需要持续获取数据和反馈才能改进。但两者仍依赖一套为仪表板而建的技术栈、架构和方法：
-按峰值负载配置的 Spark 和 Flink 集群，以及为保持数据表可用而运行的压实作业。
-为每个数据使用方分别建立一条流水线和一份数据副本，数据依次经过青铜层、白银层和黄金层。
-由人工编写、通过编排器串联，并由专家维护运行的作业。
结果是：交付数据复杂、有风险且成本高昂。虽然编码智能体能在几小时内交付应用，但它们很少构建或运行数据流水线。数据变化太快，犯错的代价也太高。
借助 Yukon，数据在一个连续流程中完成摄取、处理和服务，无需传统上用于连接这些环节的基础设施和流水线。
事件数据在数秒内就会变成经过压实、排序和去重的 Iceberg 表。
实时聚合在数据流中直接运行，状态由系统全面托管。智能体和应用程序可以通过兼容 Redis 的 API，或通过我们兼容 SQL 和 Spark 的查询引擎，以低于 1 毫秒的延迟访问新鲜数据。
除了传输和提供数据，Yukon 还让数据持续保持可理解：可通过 MCP 访问的实时列级统计信息，让智能体了解数据。内置的监控、采样和数据最小化功能有助于团队保持控制。
在每秒 100 万个事件且数据新鲜度相同的条件下，Yukon 使用的计算资源比 Spark 少 4 倍，比 Flink 少 7 倍。基准测试已公开：你可以使用 Eon 的 ingest-bench 自行运行。
这是朝着我们为 AI 时代打造自主数据基础的愿景迈出的又一重要一步，旨在既保护企业数据，又充分释放其潜力。
原文
What I'm seeing across the industry right now is that the biggest bottleneck to AI adoption isn't model capabilities. It's data infrastructure. Today, we're expanding the Eon Data Foundation with the launch of Eon Yukon, a new serverless data engine purposely built to change that. AI agents need fresh, contextualized data to act. Specialized models need a continuous feed of data and feedback to improve. Both are still served by a stack, an architecture, and methods built for dashboards: -Spark and Flink clusters sized for peak, plus compaction jobs to keep tables usable. -A pipeline and a copy for every consumer, staged bronze to silver to gold. -Hand-written jobs, wired together by an orchestrator and kept alive by specialists. The result: shipping data is complicated, risky, and expensive. While coding agents ship apps in hours, they rarely build or run data pipelines. The data is too volatile, and a mistake costs too much. With Yukon, data moves from ingestion to processing to serving in one continuous flow, without the infrastructure and pipelines traditionally required to connect them. Events become compacted, sorted, deduplicated Iceberg tables within seconds. Real-time aggregations run inline, with fully managed state. Agents and applications can access fresh data with sub-millisecond latency through a Redis-compatible API or through our SQL and spark compatible query engine. Beyond moving and serving data, Yukon makes it continuously understandable - Live column-level statistics, accessible through MCP, give agents visibility into the data. Built-in monitoring, sampling, and data minimization help teams maintain control. At 1M events/sec and equal freshness, Yukon used 4x less compute than Spark and 7x less than Flink. The benchmark is public: run it yourself with Eon’s ingest-bench. This is another major step toward our vision of an Autonomous Data Foundation for the AI era, built to both protect enterprise data and unlock its full potential.