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

Developers can now build AI agents that handle complex, real-world workflows asynchronously and integrate seamlessly with external tools, reducing the gap between experimentation and production deployment.

Google AI Blog··1 min readrelease
releaseExpanding Managed Agents in Gemini API: background tasks, remote MCP and more
blog.google

What happened

Google has announced new capabilities for Managed Agents within the Gemini API, focusing on making it easier to build production-ready agents. The key additions include support for background tasks, which allow agents to handle long-running operations asynchronously without blocking the main thread. Additionally, remote MCP (Model Context Protocol) integration enables agents to connect to external tools and data sources, expanding their utility in complex workflows. These updates are part of Google's broader strategy to provide developers with a reliable infrastructure for deploying autonomous agents. For developers building AI workflows, this means they can now create agents that operate more like independent services, capable of executing tasks in the background and interacting with third-party systems through a standardized protocol. The enhancements aim to reduce the friction in moving agents from prototype to production, addressing common pain points such as timeout limits and integration complexity.

Key takeaways

  • Background tasks support enables agents to run long operations asynchronously.
  • Remote MCP integration allows agents to connect external tools and data sources.
  • Updates are designed to make Managed Agents more production-ready and reliable.
  • Standardizes interaction with third-party services via the Model Context Protocol.

Why it matters

Developers can now build AI agents that handle complex, real-world workflows asynchronously and integrate seamlessly with external tools, reducing the gap between experimentation and production deployment.

This is an original editorial digest by AI Workflow Center. Full reporting at the source:

Read the original on Google AI Blog
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