Platform · 2026-07-07

Expanding Managed Agents in Gemini API: background tasks, remote MCP and more

Google expands Managed Agents in the Gemini Interactions API: long-running background execution (start with background:true, poll/stream/reconnect by ID), direct connection to remote MCP servers alongside built-in sandbox tools, custom function calling, and credential refreshing across interactions.

Google AI Blog2026-07-07About 2 min readWorth tracking

Marked important, but published more than 30 days ago, so it moved down to this band.

AI agentsWorkflowAgent runtimes & protocols
On this page11 sections

The editors' notes below — why it matters, the context, the learning path — are written in Chinese.

Record

The signal

#

Google announced new capabilities for Managed Agents in the Gemini Interactions API. With managed agents, a single endpoint call has Gemini handle reasoning, code execution, package installation, file management and web information inside an isolated cloud sandbox. New in this update: long-running background execution (pass background:true to run asynchronously on the server; the API returns an ID for polling, streaming progress, or reconnecting later), remote MCP server integration (connect managed agents directly to remote Model Context Protocol servers, mixing remote tools with built-in capabilities like Google Search and code execution), custom function calling, and credential refreshing across interactions.

Why now

Why it matters

#

托管式长时程智能体正在成为大厂平台的标配:执行沙箱、后台任务、工具接入都向平台侧收敛,MCP 又拿下一个主流平台的官方支持。团队需要重新评估「自建智能体 runtime」还是「用平台托管」——这条边界正在快速移动。

Background

Technical context

#

后台执行解决的是长时程任务的连接脆弱性:同步 HTTP 模式下任务时长受连接寿命限制,异步模式把状态管理移到服务端(凭 ID 轮询/重连),这是长时程智能体状态管理的平台化实现。远程 MCP 直连则把「模型 ↔ 私有工具」的集成从自建代理中间件简化为协议标准件——与内置工具(搜索、代码执行)在同一沙箱内混用。

At your level

Have AI explain this signal at your level

Pick your level of experience and AI writes an explanation for it on the spot.

Audience

Who should care

#
构建生产级智能体的开发团队评估 MCP 生态与工具接入方案的架构师对比各家智能体平台能力的技术选型者
Areas it may affect
智能体平台MCP 生态开发者工具

Next

Learning path

#
  1. 先读「工具使用与函数调用」与 MCP 的基本概念,理解托管接入与自建代理的差异
  2. 对照 ChatGPT Work 的产品形态,理解长时程后台执行为什么是标配能力
  3. 评估自己产品里哪些工具接入可以从自建中间件迁移到 MCP 标准件

Getting started

Have AI draft a learning path

From the related knowledge and skills on this page, AI drafts a path from the basics to applying it.

Network

Where it sits in the network

#

How this signal connects to neighbouring technologies, skills and background knowledge. Follow any link to keep exploring.

Comparison

Compare with another technology

Pick another published technology and AI writes a comparison of what they share, how they differ and when to use each.

Further thought

Follow-up questions

#
  • 远程 MCP 接入的鉴权与权限边界如何配置?沙箱内的爆炸半径怎么控制?
  • 后台任务的时长上限、计费模型与并发配额是什么?
  • 与自建智能体 runtime 相比,托管形态在可观测性上让渡了什么?

Publication record

Featured in

#

The briefings that carried this signal, newest first; the last row is the weekly review of its week. The tag is the section it ran in.

  1. 技术简报 - 2026-07-28

    Immediate attention

Sources

Google AI Blog

GoogleBig tech2026-07-07
Open original sourcehttps://blog.google/innovation-and-ai/technology/developers-tools/expanding-managed-agents-gemini-api/