With the recent launch of Managed Agents in the Gemini API, Google has collapsed that entire stack into a single API call. Whether you are a developer looking to spin up an autonomous research assistant or an enterprise building secure, multi-agent workflows, the Gemini ecosystem now offers a complete toolkit for agentic development.
Here is a look at how you can build and deploy agents using the Gemini API today.
The “Hello World” of Autonomous Agents: Antigravity
At the core of the new Managed Agents ecosystem is the Interactions API and a general-purpose agent called Antigravity. Powered by models like Gemini 3.6 Flash, Antigravity doesn’t just return text — it actively works on your behalf in a secure, ephemeral Linux environment hosted by Google.
With a few lines of Python, you can ask the agent to fetch live data, write a script to process it, and generate an output file:
from google import genai
client = genai.Client()
interaction = client.interactions.create(
agent="antigravity-preview-05-2026",
input="Plot the growth of solar energy generation globally and make some slides in HTML.",
environment="remote", # Provisions a remote Linux sandbox
)
print(interaction.output_text)
In this single call, the agent reasons about the task, browses the web for the latest solar data, writes the necessary Python code to plot the graph, and outputs HTML slides — all without you needing to manage the underlying infrastructure.
Building Custom Agents Declaratively
While Antigravity is powerful out of the box, production use cases require custom behavior, domain-specific skills, and proprietary tools. This is where the Agents API comes in.
Instead of writing complex Python orchestration code, the Gemini API allows you to define agents declaratively. You can bundle system instructions, data sources, and specific tools into markdown files (like AGENTS.md and SKILL.md). Once registered, you have a durable, named agent that you can invoke repeatedly, guaranteeing that every run starts from an identical, clean environment.
# Create a new custom agent from an existing environment snapshot
agent = client.agents.create(
name="my-data-analyst",
base_agent="antigravity-preview-05-2026",
instructions="You are a data analyst that creates slide presentations.",
# Link to your environment and tools
)
This approach drastically reduces the attack surface and eliminates state contamination between agent invocations. You can even securely inject credentials server-side via header transforms, ensuring API tokens are never exposed inside the sandbox environment itself.
Bringing Your Own Open-Source Framework
If you prefer to orchestrate multi-agent systems using open-source tools, Gemini models natively integrate with the most popular frameworks in the ecosystem:
- LangGraph: Ideal for complex, stateful workflows where you need to represent an agent’s reasoning process as a graph. Gemini’s advanced function calling enables iterative reflection at each node.
- CrewAI: Perfect for building teams of autonomous agents. You can assign Gemini-powered agents specific roles (e.g., “Senior Researcher” and “Copywriter”) and let them collaborate to achieve a complex goal.
- LlamaIndex: The go-to framework for creating knowledge agents. By connecting Gemini to your private data, you can build agents that excel at RAG (Retrieval-Augmented Generation) over both text and images.
- Composio: Simplifies the process of hooking up external APIs and real-world tools, allowing Gemini’s function calling to execute actions across hundreds of third-party apps.
Scaling to the Enterprise
For large organizations that need strict governance, Google recently introduced the Gemini Enterprise Agent Platform. It provides a single destination for technical teams to build, scale, govern, and optimize agents. With granular controls over data sources, network allowlists (to restrict outbound traffic to permitted domains), and seamless deployment to employees via the Gemini Enterprise app, it bridges the gap between experimental AI and enterprise-grade automation.
The Takeaway
We are moving past the era of chatbots and into the era of digital coworkers. Whether you are using the single-call simplicity of the new Managed Agents API, orchestrating a team of agents with CrewAI, or deploying secure workflows on the Enterprise Agent Platform, the Gemini API provides the reasoning engine and infrastructure needed to turn complex tasks into autonomous actions.
The hardest part is no longer building the agent — it’s deciding what you want it to do first.