# Gemini Managed Agents: Deploy an AI Agent with One API Call > Source: > Published: 2026-06-13 18:09:14+00:00 Google shipped Managed Agents in the Gemini API at I/O 2026, and the pitch is blunt: call an API, get a working Linux sandbox where an AI agent reasons, runs code, browses the web, and manages files — all in one call. No VMs to spin up, no orchestration loop to write, no tool registry to maintain. The first call to `client.interactions.create()` replaces what used to be a multi-service setup. Whether or not it holds up in production, the developer experience argument is hard to argue with. ## What Managed Agents Actually Do The core of this is the **Interactions API** (currently in Beta), which replaces `generateContent` for agentic work. You pass a task to the Antigravity agent — `antigravity-preview-05-2026` , Google’s general-purpose managed agent — and Google provisions a remote Linux sandbox, runs the agent loop, and returns the result. Inside that sandbox, the agent can execute Python, Node.js, and Bash; install packages; read and write files; and browse the web. That last combination — code execution *and* web browsing in the same isolated environment — is what separates this from OpenAI’s code interpreter, which keeps those capabilities more siloed. The Antigravity agent runs on [Gemini 3.5 Flash](https://blog.google/innovation-and-ai/technology/developers-tools/google-io-2026-developer-highlights/), fine-tuned specifically for tool calls and shell command output. Google reports Terminal-Bench 2.1 at 76.2% and MCP Atlas at 83.6%, outperforming Gemini 3.1 Pro on the agentic benchmarks that matter for this use case. ## The Code: Start in Under Five Minutes Install the SDK (`pip install google-genai` ), set your `GEMINI_API_KEY` , and you’re ready. Here’s the basic pattern: ``` python from google import genai client = genai.Client() # One call provisions a fresh Linux sandbox interaction = client.interactions.create( agent="antigravity-preview-05-2026", environment="remote", input="Research the top Python web frameworks in 2026. Write a markdown comparison and save it as report.md" ) print(interaction.output_text) # Final response print(interaction.steps) # Every reasoning step and tool call # Continue in the same sandbox — files and packages persist follow_up = client.interactions.create( agent="antigravity-preview-05-2026", environment=interaction.environment_id, previous_interaction_id=interaction.id, input="Add benchmark data and convert report.md to HTML" ) ``` The `interaction.steps` list is worth examining when debugging. It surfaces every reasoning step, tool invocation, and code execution so you can trace exactly what the agent did — significantly more useful than a black-box response. ## State: Two Independent Dimensions The state model is what makes iterative workflows practical. The [Interactions API](https://ai.google.dev/gemini-api/docs/interactions/interactions-overview) tracks two independent dimensions: **Conversation context**— tracked via`previous_interaction_id` . Pass the last`interaction.id` and the agent carries forward its full chat history, reasoning trace, and tool logs.**Environment state**— tracked via`environment_id` . Reuse the same sandbox and your installed packages, generated files, and file system state persist. Sandboxes have a 7-day TTL. These two are independent by design. You can reset conversation history while keeping the environment — useful for running different prompts in the same prepared sandbox — or vice versa. Reusing `environment_id` is the better practice for iterative tasks: it avoids re-provisioning latency and keeps the packages you installed in step one alive for step three. ## What It Costs Right Now During the public preview, sandbox compute is free. You pay only for tokens at standard Gemini 3.5 Flash rates: **$1.50 per million input tokens** and **$9.00 per million output tokens**. That’s 25% cheaper than Gemini 3.1 Pro ($2.00/$12.00), which is now outclassed on agentic benchmarks anyway. When Managed Agents move to GA on the [Enterprise Agent Platform](https://cloud.google.com/products/gemini-enterprise-agent-platform/pricing), Google Cloud pricing applies. If you’re evaluating this for a real use case, the preview window is the right time to benchmark it. ## Where This Fits (Honest Take) Managed Agents are not the right tool for every agent workflow. Here’s where they earn their place and where they don’t. **Pick Managed Agents when:** you want a working prototype in an afternoon, you’re already on GCP or deep in Google Workspace, or your use case is self-contained tasks — research, code execution, data processing — that don’t require tight integration with external services you control. **Stick with Claude Agent SDK or OpenAI when:** you need fine-grained control over the orchestration loop, complex multi-agent topologies with MCP integration, or production-grade reliability guarantees that a preview product can’t provide. The Claude SDK gives you composable agents with explicit tool registration and transparent state management — more setup, more control. The honest comparison: Managed Agents are to agent development what Vercel was to deployment — they remove the infra work so you can focus on what your agent actually does. That’s genuinely valuable. Not everyone needs more control than that. ## Get Started The [official quickstart](https://ai.google.dev/gemini-api/docs/managed-agents-quickstart) covers your first interaction in Python, JavaScript, and REST. The [Antigravity agent reference](https://ai.google.dev/gemini-api/docs/antigravity-agent) has the full capability list. If you want to build a custom managed agent with your own tool set and system prompt, the documentation is at [Building Managed Agents](https://ai.google.dev/gemini-api/docs/custom-agents). The preview is live now. Sandbox compute is free. If you’ve been meaning to prototype an agent workflow, this is the lowest-friction entry point available — with the understanding that preview means preview.