[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-a4f1f3a9-2900-4ed0-9dff-016535a6f707":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":20,"created_at":21,"modified_at":22,"is_published":23,"publish_type":24,"image_url":13,"view_count":25},"a4f1f3a9-2900-4ed0-9dff-016535a6f707","Google I\u002FO 推出 Managed Agents API：一条调用完成Agent部署，代价是放弃执行层控制","Google在I\u002FO大会上发布了Managed Agents in Gemini API，这个服务的核心卖点很简单——把数周的Agent工程工作压缩成一次API调用。配合新推出的Antigravity CLI，Google显然希望在Agent执行层也实现端到端控制。\n\n这背后反映的是一场正在发生的架构分歧：Agent管理应该放在模型层（Anthropic的方式），还是基础设施层（Google的方式）？\n\nAnthropic的Managed Agents将编排能力嵌入模型层，优点是企业保有执行控制权，模型负责推理和规划。而Google则更进一步，把模型、harness、沙箱三层当作一个整体来优化，全部跑在Google托管的安全环境里。Ramp的René Sultan评价很直接：有了这套东西，开发者可以专注打磨Agent的领域行为，迭代速度完全不一样。\n\n从技术上看，这种做法确实能解决部署Agent的最大痛点——前期那些\"无聊的工作\"：搭执行环境、配沙箱、接工具调用基础设施。有客户说自己\"以前需要两周的工作，现在一次调用就搞定了\"。\n\n但把执行层交给平台意味着把控制权也交了出去。XYO创始人Arie Trouw提醒了一个风险：开发者会把确定性服务换成概率性服务，一旦出问题，数据损坏或服务降级会比传统方案更难追踪和修复。\n\n我的观点：Google这套方案对于需要快速原型验证的企业很有吸引力，但生产级使用时，平台锁定和可观测性不足的风险不可忽视。选这条路的企业，最好提前想清楚自己在监控和故障恢复上能接受多大程度的\"黑箱化\"。","https:\u002F\u002Fventurebeat.com\u002Forchestration\u002Fgoogles-managed-agents-api-promises-one-call-deployment-at-the-cost-of-execution-layer-control","17ff6400-4413-4b16-86fb-99951dbbd08d",[10,14,17],{"id":11,"name":12,"slug":12,"description":13,"color":13},"6ad31a14-c0da-42df-81fd-564281f768db","agentic-ai",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"8cf7490f-2449-4ba7-be19-61befa0d92b4","google",{"id":18,"name":19,"slug":19,"description":13,"color":13},"0a93ec8e-ea39-4693-81de-563ca8c173f7","inference","2026-05-25T04:10:00Z","2026-05-25T04:10:55.644805Z","2026-05-25T04:10:55.644815Z",true,"agent",12]