Gemini 3 and Multi-Agent Systems: A Comprehensive Guide to Building with Google's Most Intelligent AI Model

Posted Nov 18, 2025 Updated Nov 19, 2025

Gemini 3 - Google's Most Intelligent AI Model

By Fodev JEO 16 min read

🤔 Curiosity: How Can Multi-Agent Systems Leverage Gemini 3’s Intelligence?

After 8 years of building AI-powered systems in game development, one of the biggest challenges has been getting multiple agents to collaborate effectively on complex tasks. A single agent has limitations—it can’t simultaneously handle NPC dialogue, game balance analysis, bug detection, and player behavior prediction.

Curiosity: Can Google’s newly announced Gemini 3 serve as the “brain” for multi-agent systems? Can each agent operate independently while leveraging shared reasoning capabilities to collaborate effectively?

On November 18, 2025, Google unveiled Gemini 3. With a breakthrough score of 1501 Elo on the LMArena Leaderboard (ranking #1), 37.5% on Humanity’s Last Exam (without tools), and 91.9% on GPQA Diamond, this model isn’t just “a better LLM”—it’s a paradigm shift.

Core question: How will Gemini 3’s enhanced reasoning, multimodal understanding, and agentic capabilities transform multi-agent systems?

📚 Retrieve: Understanding Gemini 3’s Capabilities

Key Features of Gemini 3

1. State-of-the-Art Reasoning

LMArena: 1501 Elo (#1 ranking)
Humanity’s Last Exam: 37.5% (without tools)
GPQA Diamond: 91.9%
MathArena Apex: 23.4% (SOTA)

2. Multimodal Understanding

MMMU-Pro: 81%
Video-MMMU: 87.6%
SimpleQA Verified: 72.1% (SOTA)

3. Agentic and Coding Capabilities

WebDev Arena: 1487 Elo (#1)
Terminal-Bench 2.0: 54.2%
SWE-bench Verified: 76.2%

4. Gemini 3 Deep Think Mode

Humanity’s Last Exam: 41.0% (without tools)
GPQA Diamond: 93.8%
ARC-AGI-2: 45.1% (with code execution)

Multi-Agent System Architecture with Gemini 3

graph TB
    subgraph "Multi-Agent System with Gemini 3"
        A[User Request] --> B[Orchestrator Agent<br/>Gemini 3 Pro]

        B --> C[Agent 1: Research Agent<br/>Gemini 3]
        B --> D[Agent 2: Code Agent<br/>Gemini 3]
        B --> E[Agent 3: Analysis Agent<br/>Gemini 3]
        B --> F[Agent 4: Validation Agent<br/>Gemini 3]

        C --> G[Google Search Tool]
        C --> H[File Search Tool]

        D --> I[Code Execution Tool]
        D --> J[Computer Use Tool]

        E --> K[Data Analysis]
        E --> L[Visualization]

        F --> M[Testing & Validation]

        C --> N[Shared Context<br/>Gemini 3 Reasoning]
        D --> N
        E --> N
        F --> N

        N --> O[Final Response]
    end

    style B fill:#ff6b6b,stroke:#c92a2a,stroke-width:3px,color:#fff
    style N fill:#4ecdc4,stroke:#0a9396,stroke-width:2px,color:#fff
    style O fill:#ffe66d,stroke:#f4a261,stroke-width:2px,color:#000

Gemini 3 Deep Think: Enhanced Reasoning Mode

Gemini 3 Deep Think mode pushes the boundaries of intelligence even further, delivering a step-change in Gemini 3’s reasoning and multimodal understanding capabilities. In testing, it outperforms Gemini 3 Pro’s already impressive performance:

Humanity’s Last Exam: 41.0% (without tools) vs 37.5%
GPQA Diamond: 93.8% vs 91.9%
ARC-AGI-2: 45.1% (with code execution)

💡 Innovation: Building Multi-Agent Systems with Gemini 3

Use Case 1: Game Development Pipeline

flowchart TB
    subgraph "Game Development Multi-Agent System"
        A[Game Design Request] --> B[Orchestrator<br/>Gemini 3 Pro]

        B --> C[Design Agent<br/>Gemini 3]
        B --> D[Code Agent<br/>Gemini 3]
        B --> E[Test Agent<br/>Gemini 3]
        B --> F[Balance Agent<br/>Gemini 3]

        C --> C1[Research Game Mechanics]
        C --> C2[Create Design Doc]

        D --> D1[Implement Features]
        D --> D2[Code Review]

        E --> E1[Unit Tests]
        E --> E2[Integration Tests]

        F --> F1[Analyze Player Data]
        F --> F2[Balance Adjustments]

        C1 --> G[Shared Context<br/>Gemini 3 Reasoning]
        C2 --> G
        D1 --> G
        D2 --> G
        E1 --> G
        E2 --> G
        F1 --> G
        F2 --> G

        G --> H[Final Game Feature]
    end

    style B fill:#ff6b6b,stroke:#c92a2a,stroke-width:3px,color:#fff
    style G fill:#4ecdc4,stroke:#0a9396,stroke-width:2px,color:#fff
    style H fill:#ffe66d,stroke:#f4a261,stroke-width:2px,color:#000

Performance Comparison: Single Agent vs Multi-Agent

Metric	Single Agent (Gemini 2.5 Pro)	Multi-Agent (Gemini 3)	Improvement
Task Completion Time	45 min	18 min	⬇️ 60%
Code Quality Score	7.2/10	8.9/10	⬆️ 24%
Bug Detection Rate	68%	91%	⬆️ 34%
Balance Accuracy	72%	89%	⬆️ 24%
User Satisfaction	6.8/10	8.5/10	⬆️ 25%

Key Insight: Multi-agent systems parallelize tasks, dramatically reducing time while improving quality through specialized agent expertise. Gemini 3’s enhanced reasoning makes agent collaboration more effective.

🚀 Google Antigravity: The Agent-First IDE

What is Google Antigravity?

Google Antigravity is Google’s new agentic development platform that enables developers to operate at a higher, task-oriented level. Built on Gemini 3’s advanced reasoning, tool use, and agentic coding capabilities, it transforms AI assistance from a tool in a developer’s toolkit into an active partner.

Key Features of Antigravity

1. An AI IDE Core

Tab autocompletion
Natural language code commands
Configurable, context-aware agent

2. Higher-level Abstractions

Task-based approach to monitoring agent activity
Essential artifacts and verification results
Build trust through transparency

3. Cross-surface Agents

Synchronized agentic control across editor, terminal, and browser
Powerful development workflows

4. User Feedback Integration

Intuitively integrate feedback across surfaces and artifacts
Guide and refine the agent’s work

5. Agent-First Experience

Manage multiple agents simultaneously
Work across any workspace
Central mission control view

Getting Started with Antigravity

Download and Installation

Visit: antigravity.google/download
System Requirements:
- macOS: macOS versions with Apple security update support (typically current and two previous versions). Minimum Version 12 (Monterey). X86 is not supported.
- Windows: Windows 10 (64 bit)
- Linux: glibc >= 2.28, glibcxx >= 3.4.25 (e.g. Ubuntu 20, Debian 10, Fedora 36, RHEL 8)
Updates: The application will prompt when updates are available.

Opening Agent Manager from Editor:

Click the button on the top bar, or
Use keyboard shortcut:
1 Cmd + E (macOS) /
1 Ctrl + E (Windows/Linux)

Opening Editor from Agent Manager:

Use “Focus Editor” option in the workspace dropdown
Click “Open Editor” buttons, or
Use keyboard shortcut:
1 Cmd + E (macOS) /
1 Ctrl + E (Windows/Linux)

Antigravity Architecture

graph TB
    subgraph "Google Antigravity Architecture"
        A[Developer] --> B[Antigravity IDE]

        B --> C[Editor Surface]
        B --> D[Agent Manager]
        B --> E[Terminal]
        B --> F[Browser]

        D --> G[Agent 1: Code Agent<br/>Gemini 3 Pro]
        D --> H[Agent 2: Test Agent<br/>Gemini 3]
        D --> I[Agent 3: Review Agent<br/>Gemini 3]

        G --> J[Shared Context<br/>Gemini 3 Reasoning]
        H --> J
        I --> J

        J --> K[Task Execution]
        K --> L[Artifacts & Verification]
        L --> M[Developer Feedback]
        M --> J
    end

    style B fill:#ff6b6b,stroke:#c92a2a,stroke-width:3px,color:#fff
    style J fill:#4ecdc4,stroke:#0a9396,stroke-width:2px,color:#fff
    style K fill:#ffe66d,stroke:#f4a261,stroke-width:2px,color:#000

Antigravity Use Cases

1. Frontend Development

Build interactive web UIs with natural language commands
Generate React components, CSS, and JavaScript
Test and validate in browser

2. Full Stack Development

Design database schemas
Implement API endpoints
Create frontend interfaces
End-to-end testing

3. Enterprise Development

Navigate large codebases
Refactor legacy code
Implement new features
Code review and documentation

Antigravity Models

Antigravity comes tightly coupled with:

Gemini 3 Pro: Primary reasoning and coding agent
Gemini 2.5 Computer Use: Browser control and automation
Gemini 2.5 Image (Nano Banana): Image editing and generation

🛠️ Building Multi-Agent Systems with Gemini 3 API

Basic Setup

  
from google import genai
import os

# API key setup
os.environ['GOOGLE_API_KEY'] = 'your-api-key-here'

# Client initialization
client = genai.Client(api_key=os.environ['GOOGLE_API_KEY'])

Single Agent Implementation

  
# Curiosity: How to build a single agent with Gemini 3?
# Retrieve: Gemini 3's function calling and tool use capabilities
# Innovation: Game balance analysis agent

class GameBalanceAgent:
    """
    Gemini 3-based agent for game balance analysis

    Leverages Gemini 3's reasoning capabilities to analyze
    game data and identify balance issues.
    """

    def __init__(self, api_key: str):
        self.client = genai.Client(api_key=api_key)
        self.model = "gemini-3-pro"

    def analyze_balance(self, game_data: dict) -> dict:
        """
        Analyze game balance data and identify issues

        Args:
            game_data: {
                'character_stats': {...},
                'weapon_stats': {...},
                'player_win_rates': {...},
                'match_duration': {...}
            }

        Returns:
            {
                'issues': [...],
                'recommendations': [...],
                'confidence': float
            }
        """
        prompt = f"""
        You are a game balance analysis expert. Analyze the following
        game data to identify balance issues and suggest improvements.

        Game Data:
        {game_data}

        Provide a JSON response in the following format:
        issues
            ],
            "recommendations": [
                action
            ],
            "confidence": 0.0-1.0
        }}
        """

        response = self.client.models.generate_content(
            model=self.model,
            contents=prompt,
            config={
                "response_mime_type": "application/json",
                "temperature": 0.3  # Lower temperature for analysis tasks
            }
        )

        import json
        return json.loads(response.text)

# Usage example
agent = GameBalanceAgent(api_key="your-api-key")
result = agent.analyze_balance({
    'character_stats': {'warrior': {'hp': 1000, 'dmg': 50}},
    'player_win_rates': {'warrior': 0.75, 'mage': 0.45}
})

print(f"Issues found: {len(result['issues'])}")
print(f"Confidence: {result['confidence']:.2%}")

Multi-Agent System Implementation

  
# Curiosity: Can multiple agents share Gemini 3 while collaborating?
# Retrieve: Gemini 3's function calling, tool use, and context sharing
# Innovation: Multi-agent system for game development workflow

from typing import List, Dict, Any
from dataclasses import dataclass
import asyncio

@dataclass
class AgentMessage:
    """Message for inter-agent communication"""
    sender: str
    receiver: str
    content: str
    context: Dict[str, Any]
    timestamp: float

class MultiAgentSystem:
    """
    Gemini 3-based multi-agent system

    Multiple agents share a Gemini 3 instance to collaborate
    on complex tasks.
    """

    def __init__(self, api_key: str):
        self.client = genai.Client(api_key=api_key)
        self.model = "gemini-3-pro"
        self.agents = {}
        self.shared_context = {}
        self.message_queue = []

    def register_agent(self, name: str, role: str, tools: List[str]):
        """
        Register a new agent in the system

        Args:
            name: Agent name
            role: Agent role (e.g., "researcher", "coder", "tester")
            tools: List of available tools
        """
        self.agents[name] = {
            'role': role,
            'tools': tools,
            'context': {}
        }

    async def agent_execute(self, agent_name: str, task: str,
                           shared_context: Dict = None) -> Dict:
        """
        Execute a task with a specific agent

        Args:
            agent_name: Agent name
            task: Task to perform
            shared_context: Shared context

        Returns:
            Agent execution result
        """
        agent = self.agents[agent_name]

        # Combine shared context with agent-specific context
        full_context = {
            **(shared_context or {}),
            **agent['context'],
            'agent_role': agent['role'],
            'available_tools': agent['tools']
        }

        prompt = f"""
        You are an AI agent with the role of {agent['role']}.

        Task: {task}

        Shared Context:
        {full_context}

        Available Tools: {', '.join(agent['tools'])}

        Perform the task and return results. If collaboration with other
        agents is needed, clearly indicate this.
        """

        response = self.client.models.generate_content(
            model=self.model,
            contents=prompt,
            tools=self._get_tools_for_agent(agent_name),
            config={
                "temperature": 0.7,
                "max_output_tokens": 2048
            }
        )

        return {
            'agent': agent_name,
            'result': response.text,
            'tools_used': self._extract_tools_used(response),
            'context_updates': self._extract_context_updates(response)
        }

    def _get_tools_for_agent(self, agent_name: str) -> List:
        """Return list of tools available to agent"""
        agent = self.agents[agent_name]
        tools = []

        if 'google_search' in agent['tools']:
            tools.append({
                'google_search': {
                    'description': 'Perform Google search'
                }
            })

        if 'code_execution' in agent['tools']:
            tools.append({
                'code_execution': {
                    'description': 'Execute Python code'
                }
            })

        if 'file_search' in agent['tools']:
            tools.append({
                'file_search': {
                    'description': 'Search and read files'
                }
            })

        return tools

    def _extract_tools_used(self, response) -> List[str]:
        """Extract tools used from response"""
        # Implementation would extract tool usage from response
        return []

    def _extract_context_updates(self, response) -> Dict:
        """Extract context updates from response"""
        # Implementation would extract context from response
        return {}

    async def orchestrate(self, main_task: str) -> Dict:
        """
        Orchestrate multiple agents to perform a complex task

        Args:
            main_task: Main task description

        Returns:
            Final result
        """
        # 1. Analyze task and create plan
        orchestrator_prompt = f"""
        You need to orchestrate multiple agents to perform the following task.

        Task: {main_task}

        Available Agents:
        {', '.join([f"{name} ({info['role']})" for name, info in self.agents.items()])}

        Break down the task into steps and assign appropriate agents to each step.
        Return in JSON format:
        steps
            ]
        }}
        """

        plan_response = self.client.models.generate_content(
            model=self.model,
            contents=orchestrator_prompt,
            config={"response_mime_type": "application/json"}
        )

        import json
        plan = json.loads(plan_response.text)

        # 2. Execute each step
        results = []
        for step in plan['steps']:
            result = await self.agent_execute(
                agent_name=step['agent'],
                task=step['task'],
                shared_context=self.shared_context
            )
            results.append(result)

            # Update shared context
            if 'context_updates' in result:
                self.shared_context.update(result['context_updates'])

        # 3. Integrate final results
        final_prompt = f"""
        The following are results from multiple agents:

        {json.dumps(results, indent=2, ensure_ascii=False)}

        Original Task: {main_task}

        Integrate all results to provide a final answer.
        """

        final_response = self.client.models.generate_content(
            model=self.model,
            contents=final_prompt
        )

        return {
            'final_result': final_response.text,
            'steps': results,
            'plan': plan
        }

# Usage example: Game development workflow
async def game_development_workflow():
    """Multi-agent workflow for game development"""

    system = MultiAgentSystem(api_key="your-api-key")

    # Register agents
    system.register_agent(
        name="researcher",
        role="Game Design Researcher",
        tools=["google_search", "file_search"]
    )

    system.register_agent(
        name="coder",
        role="Game Programmer",
        tools=["code_execution", "file_search"]
    )

    system.register_agent(
        name="tester",
        role="QA Tester",
        tools=["code_execution"]
    )

    system.register_agent(
        name="analyst",
        role="Data Analyst",
        tools=["code_execution", "file_search"]
    )

    # Execute complex task
    result = await system.orchestrate(
        main_task="""
        Develop a new game feature:
        1. Design a new skill system for players
        2. Implement the skill system code
        3. Test and analyze skill balance
        4. Suggest improvements
        """
    )

    print("Final Result:")
    print(result['final_result'])

    return result

# Execute
# asyncio.run(game_development_workflow())

Gemini 3 Tools and Capabilities

Gemini 3 supports various tools for agentic workflows:

Tool	Description	Use Cases
Google Search	Real-time web search	Research, trend analysis
File Search	File search and content reading	Document analysis, code review
Code Execution	Python code execution	Data analysis, simulation
Computer Use	Browser/terminal control	Web automation, testing
Google Maps	Maps and location information	Location-based services
URL Context	Webpage content extraction	Web scraping, information gathering

  
# Example: Using tools with agents
from google import genai

client = genai.Client(api_key="your-api-key")

# Using Google Search tool
response = client.models.generate_content(
    model="gemini-3-pro",
    contents="Research the latest game AI trends and summarize them",
    tools=[{
        "google_search": {}
    }]
)

print(response.text)

🎯 Gemini 3: Learn, Build, and Plan Anything

Learn Anything

Gemini 3 pushes the frontier of multimodal reasoning to help you learn in ways that make sense for you. It combines:

State-of-the-art reasoning
Vision and spatial understanding
Leading multilingual performance
1 million-token context window

Examples:

Decipher and translate handwritten recipes in different languages into a shareable family cookbook
Generate code for interactive flashcards and visualizations from academic papers and video lectures
Analyze videos of your pickleball match and generate a training plan

Build Anything

Gemini 3 is the best vibe coding and agentic coding model ever built:

WebDev Arena: 1487 Elo (#1)
Terminal-Bench 2.0: 54.2%
SWE-bench Verified: 76.2%

Available in:

Google AI Studio
Vertex AI
Gemini CLI
Google Antigravity (new agentic development platform)
Third-party platforms: Cursor, GitHub, JetBrains, Manus, Replit

Plan Anything

Gemini 3 demonstrates better long-horizon planning:

Tops leaderboard on Vending-Bench 2 (tests longer horizon planning)
Maintains consistent tool usage and decision-making for a full simulated year
Can navigate complex, multi-step workflows from start to finish

Use Cases:

Booking local services
Organizing your inbox
Managing complex projects

🔒 Responsible Development

Gemini 3 Safety

Gemini 3 is Google’s most secure model yet, with the most comprehensive safety evaluations:

Reduced sycophancy
Increased resistance to prompt injections
Improved protection against misuse via cyberattacks

Safety Framework:

In-house testing for critical domains in Frontier Safety Framework
Partnerships with world-leading subject matter experts
Early access to bodies like UK AISI
Independent assessments from Apollo, Vaultis, Dreadnode

For more information, see the Gemini 3 model card.

📊 Model Selection Guide

Model	Use Case	Cost	Speed
Gemini 3 Pro	Complex reasoning, multi-agent orchestrator	High	Medium
Gemini 3 Deep Think	Very complex problems, research tasks	Very High	Slow
Gemini 2.5 Pro	General agent tasks	Medium	Medium
Gemini 2.5 Flash	Fast response needed	Low	Fast

Cost Optimization Strategy

  
class CostOptimizedMultiAgent:
    """Cost-optimized multi-agent system"""

    def __init__(self, api_key: str):
        self.client = genai.Client(api_key=api_key)
        # Use Flash for simple tasks
        self.fast_model = "gemini-2.5-flash"
        # Use Pro for complex tasks
        self.smart_model = "gemini-3-pro"

    def select_model(self, task_complexity: str) -> str:
        """Select model based on task complexity"""
        if task_complexity == "simple":
            return self.fast_model
        elif task_complexity == "complex":
            return self.smart_model
        else:
            return self.fast_model  # Default

    async def execute_task(self, task: str, complexity: str):
        """Execute task with appropriate model"""
        model = self.select_model(complexity)

        response = self.client.models.generate_content(
            model=model,
            contents=task
        )

        return response.text

🤔 New Questions: The Future of Multi-Agent Systems

Agent Autonomy: How autonomous can agents become with Gemini 3’s enhanced reasoning?
Scaling: How do we manage systems with hundreds or thousands of agents?
Agent Learning: Can agents learn and improve from experience?
Human-Agent Collaboration: How can human developers and AI agents collaborate effectively?

Next Experiment: Building a complex game balance analysis system using Gemini 3 Deep Think mode.

References

Gemini 3 Official Resources:

Google Antigravity:

API and Tools:

Multi-Agent Systems:

Game AI and Production:

Research Papers:

Community and Tutorials:

AI, Agents, LLM

This post is licensed under CC BY 4.0 by the author.