Gemini API Managed Agents: 3.6 Flash, hooks, and more

Google's Gemini API Managed Agents now default to Gemini 3.6 Flash, introduce environment hooks for custom pre- and post-tool execution scripts, and offer free tier access, enabling a single API call to coordinate reasoning, code execution, and web retrieval in an isolated cloud sandbox.

Gemini API Managed Agents: 3.6 Flash, hooks, and more Managed Agents in Gemini API https://ai.google.dev/gemini-api/docs/agents are getting environment hooks https://ai.google.dev/gemini-api/docs/agent-hooks , model selection https://ai.google.dev/gemini-api/docs/antigravity-agent model-selection , and free tier access https://ai.google.dev/gemini-api/docs/pricing pricing-for-agents . These capabilities build on our previous release introducing background tasks and remote MCP server integration https://blog.google/innovation-and-ai/technology/developers-tools/expanding-managed-agents-gemini-api/ . With managed agents in the Gemini Interactions API https://ai.google.dev/gemini-api/docs/interactions-overview , a single API call coordinates, reasoning, code execution, package installation, file management, and web retrieval inside an isolated cloud sandbox. If you're using an AI coding assistant, drop this in your terminal to give it access to the Interactions API skill: npx skills add google-gemini/gemini-skills --skill gemini-interactions-api. Below are examples using the @google/genai TypeScript/JavaScript SDK. For Python or cURL, check out the Antigravity agent documentation https://ai.google.dev/gemini-api/docs/antigravity-agent . npm install @google/genai Gemini 3.6 Flash is now the default The antigravity-preview-05-2026 agent now runs Gemini 3.6 Flash https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-6-flash-3-5-flash-lite-3-5-flash-cyber/ by default. No code changes are required. Your next interaction picks it up automatically. You can also explicitly select models by passing agent config.model when creating an interaction or managed agent. Use Gemini 3.5 Flash-Lite for lower cost, or pin to your model of preference. js import { GoogleGenAI } from "@google/genai"; const client = new GoogleGenAI {} ; const interaction = await client.interactions.create { agent: "antigravity-preview-05-2026", input: "Audit all dependencies in package.json, upgrade outdated packages, and verify the build by running npm test.", environment: "remote", agent config: { type: "antigravity", model: "gemini-3.5-flash-lite", }, } ; console.log interaction.output text ; Supported models include: Gemini 3.6 Flash gemini-3.6-flash , default : Balanced model for reasoning, coding, and tool use. Gemini 3.5 Flash gemini-3.5-flash : Previous generation for general agentic workflows. Gemini 3.5 Flash-Lite gemini-3.5-flash-lite : Lowest latency and cost on the Gemini 3.5 family. Environment hooks: block, lint, and audit tool calls inside the sandbox Environment hooks https://ai.google.dev/gemini-api/docs/agent-hooks let you run your custom scripts before or after every tool call the agent makes inside its sandbox. Add a . agents/hooks.json into your environment and the runtime executes your handlers on pre tool execution or post tool execution events. The matcher field supports regular expressions, allowing you to target multiple tools with | or catch everything with : { "security-gate": { "pre tool execution": { "matcher": "code execution|write file", "hooks": { "type": "command", "command": "python3 /.agents/hooks-scripts/gate.py", "timeout": 10 } } }, "auto-format": { "post tool execution": { "matcher": " ", "hooks": { "type": "command", "command": "python3 /.agents/hooks-scripts/auto lint.py", "timeout": 15 } } } } In this configuration: - The security-gate group runs gate.py before every code execution or write file call. If the script returns {"decision": "deny", "reason": "..."} , the tool call is skipped and the rejection reason is passed into the model’s context. - The auto-format group runs auto lint.py after every tool finishes to enforce code styling. - Hooks also support http type handlers that POST directly to an external endpoint. For complete HTTP hook definitions and failure handling semantics, refer to the hooks documentation https://ai.google.dev/gemini-api/docs/agent-hooks . Teams are already using hooks to build production-grade validation pipelines. For example, AI-native investment bank Offdeal uses post tool execution hooks to run automated image verification inside the remote sandbox. "OffDeal is an AI-native investment bank, and Archie is the AI analyst our bankers use every day. A requirement for banker-ready decks is company logos: buyer tables, sponsor columns, tombstone grids, often 30+ logos in a single deck, every one of which must be the right company, the appropriate size and aspect ratio, contain the name, have a transparent background, and have a high contrast when placed on a white slide. Before agent hooks, we couldn’t do this on Gemini’s managed agents: the sandbox is remote, so our validation code had nowhere to run. With hooks, a post tool execution hook triggers our pipeline inside the sandbox the moment Archie writes its company list, fetching candidates, enforcing pixel-level quality checks, verifying each logo with Gemini vision, and publishing a manifest of approved files that are the only images allowed into the deck." - Alston Lin, Founder & CTO of OffDeal Cost control and automation features Free tier availability Managed agents are now available on free tier projects https://ai.google.dev/gemini-api/docs/pricing pricing-for-agents . Developers can experiment with agentic workflows using an API key from a project without active billing https://ai.google.dev/gemini-api/docs/api-key . Budget controls Because managed agents execute multi-turn autonomous loops, complex tasks can consume significant token budgets. To prevent runaway tasks, you can pass max total tokens inside agent config to cap total consumption input + output + thinking . When the agent reaches the limit, execution safely pauses and the interaction returns status: "incomplete" . The environment state is preserved, enabling you to continue where it stopped https://ai.google.dev/gemini-api/docs/antigravity-agent continuing-incomplete-interaction by passing previous interaction id with a fresh budget. js const interaction = await client.interactions.create { agent: "antigravity-preview-05-2026", input: "Audit all modules in this repo and generate a migration report.", agent config: { type: "antigravity", max total tokens: 10000, }, environment: "remote", } ; Scheduled execution with triggers Automate recurring agent tasks with scheduled triggers https://ai.google.dev/gemini-api/docs/antigravity-agent triggers . A trigger binds an agent, environment, prompt, and cron schedule into a persistent resource that fires without manual intervention. Each run reuses the same sandbox, so files persist across executions. Environments API The Environments API https://ai.google.dev/gemini-api/docs/agent-environment environments-api lets you list, inspect, and delete sandbox sessions from code. Recover environment IDs after a disconnect, or clean up sandboxes when your pipeline finishes instead of waiting for the 7-day TTL. Get started with managed agents These updates turn managed agents into cost-controlled, scheduled workers that operate autonomously inside real development environments without breaking your budget or requiring external orchestration. Check out the Gemini Interactions API overview https://ai.google.dev/gemini-api/docs/interactions-overview and the managed agents quickstart https://ai.google.dev/gemini-api/docs/managed-agents-quickstart to explore custom agent definitions, environment configurations, network rules, and advanced streaming patterns.