How to Build an Autonomous Agent with Gemini 3 Pro & Antigravity

Struggling to move beyond simple chatbots? The landscape of AI development is undergoing a seismic shift with the November 2025 release of Gemini 3 Pro and Google Antigravity. These tools represent Google’s most ambitious step yet toward true autonomous agents—AI systems that can independently plan, execute, and verify complex developer workflows. As of late November 2025, developers now have access to sophisticated agentic capabilities that transform AI from a conversational assistant into a proactive engineering partner.

What makes Gemini 3 Pro different for agentic workflows

Gemini 3 Pro, released on November 18, 2025, introduces fundamental architectural improvements specifically designed for autonomous operation. Unlike traditional AI models that simply respond to prompts, Gemini 3 Pro incorporates “thought signatures”—encrypted representations of internal reasoning that maintain context across multi-step executions. This breakthrough addresses the critical challenge of “reasoning drift,” where agents lose track of their objectives during complex workflows.

The model’s agentic capabilities are demonstrated by significant performance improvements. According to Google’s November 2025 benchmarks, Gemini 3 Pro achieves 85.4% accuracy on the 12-bench tool-use test, compared to Gemini 2.5 Pro’s 54.9%. In practical coding scenarios, it demonstrates 35% higher accuracy on genuine software engineering tasks and reaches 1487 Elo on WebDev Arena. These improvements translate directly to more reliable autonomous agents that can handle complex development workflows without constant human supervision.

Introducing Google Antigravity: The agentic development platform

Released on November 20, 2025, Google Antigravity represents a fundamental rethinking of development environments. Unlike traditional IDEs that focus on helping developers write code faster, Antigravity operates at the task level, allowing you to delegate entire workflows to autonomous agents. The platform introduces two distinct interfaces: the familiar Editor View for hands-on coding, and the revolutionary Manager Surface for orchestrating multiple agents working asynchronously.

Antigravity’s key innovation is its artifact-based verification system. Instead of requiring developers to parse through raw tool calls and logs, agents generate tangible deliverables like task lists, implementation plans, screenshots, and browser recordings. This approach transforms agent supervision from a tedious debugging exercise into an intuitive review process. As Google stated in their November 2025 announcement, “Antigravity isn’t just an editor—it’s a development platform that combines a familiar, AI-powered coding experience with a new agent-first interface.”

Step-by-step: Building your first autonomous agent

Creating an autonomous agent with Gemini 3 Pro and Antigravity follows a structured approach that leverages the core components of agentic systems:

1. Set up your development environment: Install Google Antigravity from antigravity.google/download and obtain your Gemini API key from AI Studio
2. Define your agent’s tools: Create the functions your agent will use, such as file operations, terminal commands, and browser automation
3. Configure the reasoning engine: Set up Gemini 3 Pro with appropriate thinking levels and tool definitions
4. Implement the agent loop: Create the observe-think-act-verify cycle that enables autonomous operation
5. Test and refine: Delegate progressively complex tasks while monitoring agent performance through artifacts

The core of any autonomous agent is surprisingly simple: it’s a Large Language Model running in a loop, equipped with tools it can choose to use. As Phil Schmid demonstrated in his November 2025 guide, building a functional agent requires understanding four key components:

• The Model (Brain): Gemini 3 Pro serves as the reasoning engine that plans steps and makes decisions
• Tools (Hands and Eyes): Functions that allow the agent to interact with the development environment
• Context/Memory (Workspace): Information the agent maintains across execution cycles
• The Loop (Life): The iterative process that enables autonomous task completion

Practical code example: Basic agent implementation

Here’s a simplified Python implementation showing how to create an autonomous agent using the Google Generative AI SDK:

from google import genai
from google.genai import types

class AutonomousAgent:
    def __init__(self, model="gemini-3-pro-preview"):
        self.model = model
        self.client = genai.Client()
        self.contents = []
        self.tools = self._setup_tools()
    
    def _setup_tools(self):
        # Define file operations, terminal commands, etc.
        return {
            "read_file": {"function": self._read_file},
            "write_file": {"function": self._write_file},
            "execute_terminal": {"function": self._execute_terminal}
        }
    
    def run_autonomous_task(self, task_description):
        # Autonomous agent loop implementation
        config = types.GenerateContentConfig(
            tools=[self._create_tool_declarations()],
            thinking_level="high"  # Enable deep reasoning for complex tasks
        )
        
        response = self.client.models.generate_content(
            model=self.model, 
            contents=self.contents, 
            config=config
        )
        
        # Handle tool calls and continue the loop
        return self._process_agent_response(response)

Real-world use cases for autonomous development agents

The combination of Gemini 3 Pro’s advanced reasoning and Antigravity’s agentic platform enables practical applications that transform development workflows:

• Multi-step feature development: Agents can autonomously plan, code, test, and deploy new features by coordinating across editor, terminal, and browser interfaces
• Automated bug fixing: Delegate entire bug resolution workflows where agents reproduce issues, generate test cases, implement fixes, and verify solutions
• UI iteration and testing: Request UI changes and have agents modify codebases while providing visual artifacts for review
• Legacy code migration: Use agents to analyze, refactor, and migrate legacy systems with consistent reasoning across long-running tasks

According to Google’s November 2025 documentation, Antigravity agents excel at tasks that previously required constant context switching. The platform allows developers to “operate at a higher, task-oriented level by requesting UI changes” where agents autonomously modify codebases and communicate progress through artifacts.

Best practices for reliable autonomous agents

Building successful autonomous agents requires more than just technical implementation. Based on industry experience with Gemini 3 Pro and Antigravity as of late 2025, follow these best practices:

• Start with clear tool definitions: Use descriptive names and precise documentation for each tool function
• Implement proper error handling: Return meaningful error messages that help agents self-correct rather than generic failure codes
• Use appropriate thinking levels: Set thinking_level to “high” for complex planning tasks and “low” for high-throughput operations
• Maintain context effectively: Leverage thought signatures to preserve reasoning context across long agent sessions
• Implement safety guardrails: Include maximum iteration limits and human confirmation for sensitive operations

Google’s Antigravity team emphasizes that successful agent deployment requires trust built through transparent operation. The artifact system is specifically designed to make agent reasoning visible and verifiable, addressing one of the key challenges in autonomous system adoption.

The future of agentic development

The November 2025 releases of Gemini 3 Pro and Google Antigravity mark a significant milestone in AI-assisted development. As these technologies mature through 2026, we can expect to see increasingly sophisticated autonomous agents handling complex software engineering tasks with minimal human intervention.

The key differentiator of this new generation of agents is their ability to operate across multiple tools and interfaces while maintaining consistent reasoning. Unlike previous AI coding assistants that focused on individual code suggestions, Gemini 3 Pro-powered agents can orchestrate entire development workflows—from planning and implementation to testing and deployment.

As development teams adopt these technologies, the role of software engineers will evolve from writing code to orchestrating autonomous agents. The combination of Gemini 3 Pro’s advanced reasoning capabilities and Antigravity’s agent-first platform represents the most significant step yet toward this future.

To get started with autonomous agent development, download Google Antigravity and explore the Gemini 3 Pro API documentation. The public preview available as of November 2025 provides generous rate limits and comprehensive documentation to help developers transition from traditional coding to agentic development workflows.