Gemini 2 is really good!

When building AI agents, computation is everything. The more thinking your agent can do, the better decisions it can make. But computation costs money—until now. Google's Gemini 2 is a game-changer for AI agent development, offering unprecedented capabilities at a fraction of the cost of other leading models.

The Economics of Agent Intelligence

AI agents operate through trial and error, testing approaches and learning from outcomes. This iterative process demands significant computational resources, which has traditionally made sophisticated agent development prohibitively expensive.

Enter Gemini 2.0.

The cost differential between leading models is staggering:

Model	Input Price (USD per million tokens)	Output Price (USD per million tokens)
GPT-4.5	$75	$150
Claude 3.7	$3	$15
Gemini 2.0	$0.15	$0.60

At just 20 cents per million tokens on average (combining input/output costs), Gemini 2.0 is 500 times cheaper than GPT-4.5 and 30 times cheaper than Claude 3.7.

Why This Matters for Agents

AI agents thrive on computation. More attempts, more reasoning steps, and more planning iterations lead to better outcomes. With Gemini 2.0's pricing, developers can:

1. Run complex multi-step reasoning processes
2. Generate and evaluate multiple solution paths
3. Simulate outcomes before taking action
4. Perform continuous self-improvement loops

All at a fraction of what it would cost with other models.

Vision Capabilities: Seeing the World

Gemini 2 provides powerful multimodal capabilities that enable agents to process and understand visual information. This allows your agents to interact with the world in more meaningful ways.

Here's how to implement vision capabilities in your agent:

import { GoogleGenerativeAI } from '@google/generative-ai';

const genAI = new GoogleGenerativeAI(process.env.API_KEY);

async function processImage() {
  const model = genAI.getGenerativeModel({ model: 'gemini-pro-vision' });
  
  // The image file you want to analyze
  const fileBuffer = readFileSync('path/to/image.jpg');
  
  // Convert the file to a GoogleGenerativeAI.Part
  const imagePart = {
    inlineData: {
      data: fileBuffer.toString('base64'),
      mimeType: 'image/jpeg',
    },
  };
  
  const result = await model.generateContent([
    'Describe what you see in this image and identify any relevant details.',
    imagePart,
  ]);
  
  console.log(result.response.text());
}

With vision capabilities, your agents can:

• Analyze charts, graphs, and documents
• Process visual information from user interfaces
• Understand spatial relationships in diagrams
• Work with image-based content for richer context

Configurable Safety Settings

For production-ready agents, Gemini 2 offers fine-grained control over safety settings. This allows you to tailor your agent's behavior to your specific use case and risk tolerance.

import { GoogleGenerativeAI, HarmCategory, HarmBlockThreshold } from '@google/generative-ai';

const genAI = new GoogleGenerativeAI(process.env.API_KEY);

async function configureAgentSafety() {
  const model = genAI.getGenerativeModel({
    model: 'gemini-pro',
    safetySettings: [
      {
        category: HarmCategory.HARM_CATEGORY_HARASSMENT,
        threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
      },
      {
        category: HarmCategory.HARM_CATEGORY_HATE_SPEECH,
        threshold: HarmBlockThreshold.BLOCK_ONLY_HIGH,
      },
      {
        category: HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,
        threshold: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
      },
      {
        category: HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,
        threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE,
      },
    ],
  });
  
  // Your agent implementation with custom safety settings
}

These safety controls let you:

• Define appropriate thresholds for different harm categories
• Balance safety with functionality
• Create agents suitable for different audiences and contexts
• Comply with organizational policies and regulatory requirements

Beyond Cost: Remote Code Execution

Perhaps the most exciting feature for agent development is Gemini 2's remote code execution capabilities. This isn't just about running code—it's about expanding an agent's abilities beyond its training data.

Here's a simple example of how Gemini 2 can execute code:

import { GoogleGenerativeAI } from '@google/generative-ai';

const genAI = new GoogleGenerativeAI(process.env.API_KEY);

async function runCodeExecution() {
  const model = genAI.getGenerativeModel({ model: 'gemini-pro' });
  
  // The agent can execute code to solve problems
  const result = await model.generateContent(`
    Write and execute a Python function that finds the prime factors of 294.
    Then tell me which of those factors is largest.
  `);
  
  console.log(result.response.text());
}

runCodeExecution();

When an agent can write and execute code on the fly, it can:

1. Access current information by scraping websites or querying APIs
2. Process data dynamically instead of being limited to reasoning alone
3. Verify its own work by running tests on proposed solutions
4. Interact with external systems to accomplish real-world tasks

More Computation = Smarter Agents

The traditional bottleneck in agent development has been managing the computation budget. With costs so low, you can now implement:

• Recursive reasoning chains where agents refine their thinking over multiple passes
• Multi-agent systems with specialists collaborating on complex tasks
• Simulation-based planning to test actions before execution
• Self-critique loops where agents evaluate and improve their own output

Getting Started with Gemini 2 for Agents

Building your first agent with Gemini 2 is surprisingly straightforward. Here's how you can structure your agent to take advantage of JSON mode for structured thinking:

import { GoogleGenerativeAI } from '@google/generative-ai';

const genAI = new GoogleGenerativeAI(process.env.API_KEY);

async function createAgentWithJSONMode() {
  const model = genAI.getGenerativeModel({
    model: 'gemini-pro',
    generationConfig: {
      responseSchema: {
        type: 'object',
        properties: {
          thoughts: {
            type: 'string',
            description: 'Agent reasoning process'
          },
          action: {
            type: 'string',
            description: 'Next action to take'
          },
          actionInput: {
            type: 'object',
            description: 'Parameters for the action'
          }
        },
        required: ['thoughts', 'action', 'actionInput']
      }
    }
  });

  // Your agent prompt and implementation here
}

Setting Up Your Gemini 2 Environment

Before diving into agent development, you'll need to set up your environment. Follow these steps to get started:

1. Create a Google AI Studio Account

First, you'll need to sign up for Google AI Studio:

1. Visit Google AI Studio
2. Sign in with your Google account
3. Accept the terms of service
4. Navigate to the API Keys section

2. Generate an API Key

1. In Google AI Studio, click on "Get API key" or navigate to the API keys section
2. Create a new API key
3. Copy the key and store it securely - you'll need it for your application

3. Install the Gemini SDK

Add the Google Generative AI SDK to your project:

# Using npm
npm install @google/generative-ai

# Using yarn
yarn add @google/generative-ai

# Using pnpm
pnpm add @google/generative-ai

4. Set Up Environment Variables

Create a .env file in your project root to store your API key securely:

Regular Prompting: Simple Yet Powerful

For many agent tasks, a straightforward approach works best. Gemini 2 excels at regular prompting with clear instructions:

import { GoogleGenerativeAI } from '@google/generative-ai';

const genAI = new GoogleGenerativeAI(process.env.API_KEY);

async function simpleAgentPrompting() {
  const model = genAI.getGenerativeModel({ model: 'gemini-pro' });
  
  const prompt = `
    You are a research assistant agent. I need you to:
    1. Find information about quantum computing advances in 2023
    2. Summarize the key breakthroughs
    3. Suggest three areas where these advances might be applied
    
    Format your response clearly with sections for each task.
  `;
  
  const result = await model.generateContent(prompt);
  console.log(result.response.text());
}

The combination of clear instructions with Gemini 2's reasoning capabilities makes even simple prompting approaches highly effective for many agent tasks.

Conclusion

Gemini 2.0 represents a paradigm shift in what's possible with AI agents. The combination of dramatically lower costs and advanced capabilities like code execution, vision processing, structured outputs, and configurable safety means we're entering a new era of agent development.

For developers, researchers, and businesses looking to build truly capable AI assistants, Gemini 2.0 isn't just a good choice—it's the economically optimal one. When your agents can think 500 times more for the same price, the possibilities expand exponentially.

What would you build if computation was essentially free? With Gemini 2, we're about to find out.

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Have you experimented with Gemini 2.0 for agent development? Share your experiences in the comments below!