Google expands Managed Agents in the Gemini API with background tasks and MCP

Google is expanding Managed Agents in the Gemini API with new features for developers. The additions include background task execution, remote MCP support and other options for deploying agents more reliably in production. Managed Agents let developers build autonomous AI systems without having to manage the infrastructure themselves. Google positions the tools as a competitor to other AI agent development solutions, including OpenAI Swarms and the Claude API.

AI-processed from Google AI Blog; edited by Hamidun News
Google expands Managed Agents in the Gemini API with background tasks and MCP
Source: Google AI Blog. Collage: Hamidun News.
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Article Fact Sheet & Key Figures
ParameterDetails
Primary sourceGoogle AI Blog ↗
Editor-in-Chief
Key metric #1Background tasks — agents can run long-lived operations outside the main thread
Key metric #2Remote MCP — support for the Model Context Protocol to integrate external tools
Key metric #3Improved reliability — built-in error handling and retries

Google has announced a significant expansion of its Managed Agents offering in the Gemini API. The new functionality makes it easier for developers to build reliable, production-ready AI agents, putting Google in competition with other platforms for agentic AI.

New capabilities

  • Background tasks — agents can run long-lived operations outside the main thread
  • Remote MCP — support for the Model Context Protocol to integrate external tools
  • Improved reliability — built-in error handling and retries
  • Scalability — the managed infrastructure copes with growing load

Competitive positioning

The move puts Google in direct competition with:

  • OpenAI Swarms (a framework for multi-agent systems)
  • The Claude API with agentified capabilities
  • Microsoft AutoGen (Microsoft's multi-agent framework)

What this means

A full-featured agent platform in the Gemini API means developers no longer have to choose between the simplicity of a managed service and the control of building it themselves. Google is pitching the best of both worlds. This should speed up the adoption of AI agents in enterprise applications, from business process automation to complex analytics systems.

Story

Key Questions Answered

What are the key details on New capabilities?

Background tasks — agents can run long-lived operations outside the main thread Remote MCP — support for the Model Context Protocol to integrate external tools Improved reliability — built-in error handling and retries Scalability — the managed infrastructure copes with growing load

What are the key details on Competitive positioning?

The move puts Google in direct competition with: OpenAI Swarms (a framework for multi-agent systems) The Claude API with agentified capabilities Microsoft AutoGen (Microsoft's multi-agent framework)

What this means?

A full-featured agent platform in the Gemini API means developers no longer have to choose between the simplicity of a managed service and the control of building it themselves. Google is pitching the best of both worlds. This should speed up the adoption of AI agents in enterprise applications, from business process automation to complex analytics systems.

ZK
Zhemal Khamidun — CPO at Alpina Digital, product manager of AlpinaGPT. Building AI products since 2019. Hamidun News is a pet project that grew into a production aggregator with 6,000+ readers.
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