Google is extending the managed agent capabilities of the Gemini API to help developers build more stable AI agents ready for production environments. This update focuses on strengthening the execution management, long-running task handling, and external tool connectivity required for developing Gemini API-based agents. Managed agents reduce the burden of configuring infrastructure directly for developers and help implement agent-based applications requiring model calls, tool usage, and state management in a more consistent manner.<\/p>\n
The key change is support for background tasks. AI agents often need to perform tasks that are difficult to respond to immediately, such as research, document generation, data analysis, and multi-step task processing. With this new feature, developers can handle tasks requiring long execution times asynchronously, allowing users to continue with other workflows while the work is in progress. This is useful for implementing agent experiences required in real-world service environments, such as customer support automation, business report generation, code analysis, and complex search-based tasks.<\/p>\n
Google has also expanded support for remote MCP. MCP stands for Model Context Protocol, a standard method for connecting AI models with external tools and data sources. By utilizing remote MCP, Gemini API managed agents can more easily integrate with external systems, internal data, and business tools. This update focuses on enhancing agent development productivity and operational stability by centering on Gemini API, AI agents, MCP, and background tasks. Through this, Google supports developers in moving beyond experimental chatbots to create production agents that perform actual tasks.<\/p>","protected":false},"excerpt":{"rendered":"
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