Gemini Managed Agents: Developer Guide

2026-05-26 · Source: philschmid.de - RSS feed · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

Google's Gemini Managed Agents offer a serverless platform for building and deploying AI agents that operate within secure, isolated Linux sandboxes. These agents, powered by Gemini 3.5 Flash, can execute code (Python, Node.js, Bash), install packages, manage files, and browse the web, all accessible via a single Interactions API call. Developers can utilize the pre-built Antigravity agent or create custom agents with specific instructions, skills, and data. The platform supports multi-turn conversations by persisting sandbox state and conversation history, and offers streaming responses for real-time updates. Data can be loaded into sandboxes from Git repositories, Google Cloud Storage, or inline content, with the ability to download the full environment as a tar archive. Secure networking features include domain allowlists and an egress proxy for credential injection, ensuring secrets remain outside the sandbox. During preview, environment compute is not billed, only Gemini 3.5 Flash tokens.

Key takeaway

For AI Engineers building autonomous agents, Gemini Managed Agents simplify deployment by abstracting infrastructure. You can rapidly prototype and deploy agents that execute code and manage files in secure, isolated environments without managing servers. Consider utilizing the platform's multi-turn conversation and data loading features to build complex, stateful applications, while employing network allowlists to enhance security for sensitive operations. This approach allows you to focus on agent logic rather than operational overhead.

Key insights

Gemini Managed Agents provide a serverless platform for deploying AI agents with code execution and secure sandbox capabilities via a single API.

Principles

• Serverless agent deployment simplifies orchestration.
• Isolated sandboxes enhance security for code execution.
• Declarative agent configuration enables reusability.

Method

Create agents via the Interactions API, specifying base agents, system instructions, and data sources. Manage state with "environment_id" for sandbox persistence and "previous_interaction_id" for conversation history.

In practice

• Use "pip install google-genai" to start.
• Mount Git repos or GCS buckets into sandboxes.
• Restrict agent network access with allowlists.

Topics

• Gemini Managed Agents
• AI Agents
• Linux Sandbox
• Interactions API
• Code Execution
• Serverless AI
• Network Security

Code references

• my-org/data-templates
• my-org/analysis-templates
• my-org/backend
• google-gemini/gemini-api-cli

Best for: AI Engineer, Machine Learning Engineer, Software Engineer

Related on AIssential

• How Google just revamped Gemini Enterprise for the agentic era - here's what's new — Google's new Agent Platform streamlines secure, end-to-end agent development and deployment for enterprise AI workflows.
• Gemini Interactions API Quick Start — The Gemini Interactions API unifies agentic application development with server-side state, tool orchestration, and multimodal capabilities.
• AWS Bedrock Agents: The Beginner’s Guide — AWS Bedrock Agents provide a managed platform for building and deploying AI agents with integrated orchestration and security.
• 🤖 AI Agents Weekly: Gemini 3.5 Flash, Antigravity 2.0, Codex Thursday, Cohere Command A+, Qwen3.7-Max, and More — Google's new Gemini 3.5 Flash and Managed Agents significantly advance AI agent capabilities and deployment ease.
• Databricks and Stripe Projects: Infrastructure Built for Agents — AI agents can now autonomously provision and manage production-ready Neon Postgres databases using Stripe Projects.
• Subagents have arrived in Gemini CLI — Gemini CLI subagents delegate complex tasks to specialized, isolated agents, preserving primary context and enabling parallel execution.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by philschmid.de - RSS feed.