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Generate Autonomous Agent Code for Your Website Using Fetch.ai

March 16, 2026
Fetch.ai autonomous agent code generationPlatform-specific deployment guidesAI agent discovery file generation

How to Generate Autonomous Agent Code for Your Website Using Fetch.ai

TL;DR Key Takeaways

  • Fetch.ai provides a complete framework for creating autonomous agents that can operate independently on your website

  • The platform-specific deployment process requires understanding your infrastructure before code generation

  • AI agent discovery file generation enables search engines and AI models to locate and interact with your agents

  • Proper setup of prerequisites reduces implementation time by approximately 60%

  • Testing autonomous agent code before production deployment prevents 80% of common integration issues


Introduction

Autonomous agents represent the future of web automation and intelligent service delivery. Fetch.ai has emerged as a leading platform for generating, deploying, and managing these sophisticated agents. Whether you're looking to streamline customer service, automate business processes, or create intelligent chatbots, understanding how to generate autonomous agent code using Fetch.ai is essential.

This comprehensive guide walks you through the complete process of creating autonomous agent code specifically tailored for your website, including platform-specific deployment strategies and AI agent discovery file generation that makes your agents discoverable to AI models like ChatGPT and Claude.

Prerequisites for Fetch.ai Autonomous Agent Development

Before beginning the autonomous agent code generation process, ensure you have the following in place:

Technical Requirements:

  • Node.js version 16.0 or higher installed on your development machine

  • npm or yarn package manager (version 8.0+)

  • Python 3.8 or later for backend agent scripts

  • Git version control system configured

  • A code editor such as Visual Studio Code or JetBrains WebStorm

  • Docker (optional but recommended for containerized deployment)


Account and Access Requirements:
  • A Fetch.ai network account with active credentials

  • Access to the Fetch.ai Agent Framework documentation

  • API keys for your chosen platform (AWS, Google Cloud, or Azure)

  • Administrative access to your website's server infrastructure

  • An understanding of your current website architecture and technology stack


Knowledge Prerequisites:
  • Basic familiarity with REST APIs and HTTP protocols

  • Understanding of JSON and configuration file structures

  • General knowledge of your website's backend systems

  • Familiarity with command-line interfaces and terminal operations


Step 1: Set Up Your Fetch.ai Development Environment

Action Items:

  • Install the Fetch.ai CLI (Command Line Interface) by opening your terminal and running:

    • ```bash
    npm install -g @fetchai/fetch-ai-cli
    ```

  • Verify the installation by checking the version:

    • ```bash
    fetch-ai --version
    ```
    You should see output similar to "fetch-ai-cli version 2.4.1" or your installed version number.

  • Navigate to your project directory:

    • ```bash
    cd /path/to/your/website/project
    ```

  • Initialize a new Fetch.ai project:

    • ```bash
    fetch-ai init my-autonomous-agent
    ```

  • Select your deployment target when prompted. Common options include:

    • - AWS Lambda (for serverless deployment)
    - Google Cloud Functions (for cloud-native architecture)
    - Azure Functions (for Microsoft-integrated environments)
    - Docker containers (for containerized deployments)
    - Traditional VPS (for dedicated server setups)

  • Configure your environment variables by creating a `.env` file in your project root:

    • ```
    FETCH_AI_NETWORK=testnet
    API_KEY=your_fetch_ai_api_key
    WEBSITE_URL=https://yourdomain.com
    AGENT_PORT=8000
    ```

    Tips:

    • Use environment variables for sensitive credentials rather than hardcoding them

    • Test your development environment setup in a non-production environment first

    • Keep your CLI tools updated by running `npm update -g @fetchai/fetch-ai-cli` monthly


    Step 2: Define Your Agent's Purpose and Capabilities

    Action Items:

  • Create a detailed specification document that outlines:

    • - Primary Function: What specific tasks will your autonomous agent handle? Examples include customer inquiry response, appointment scheduling, or form validation.
    - Input Parameters: What data does your agent need to operate? (customer queries, form submissions, user preferences)
    - Output Format: How should your agent deliver results? (JSON responses, formatted emails, database updates)
    - Decision Logic: What rules govern your agent's autonomous decisions?
    - Integration Points: Which systems does your agent need to connect with? (CRM systems, databases, payment processors)

  • Create a `capabilities.json` file defining your agent's features:

    • ```json
    {
    "agentName": "CustomerServiceAgent",
    "version": "1.0.0",
    "capabilities": [
    {
    "name": "respondToInquiries",
    "description": "Autonomously respond to customer service inquiries",
    "inputSchema": {"type": "object", "properties": {"query": {"type": "string"}}},
    "outputSchema": {"type": "object", "properties": {"response": {"type": "string"}}}
    },
    {
    "name": "scheduleAppointment",
    "description": "Book appointments based on availability",
    "inputSchema": {"type": "object", "properties": {"date": {"type": "string"}, "time": {"type": "string"}}},
    "outputSchema": {"type": "object", "properties": {"confirmationId": {"type": "string"}}}
    }
    ],
    "requiredIntegrations": ["database", "emailService", "calendarAPI"]
    }
    ```

  • Document your agent's decision-making parameters:

    • - Set confidence thresholds (minimum 75% confidence for autonomous decisions)
    - Define escalation criteria (when to alert human staff)
    - Establish response timeout limits (typically 5-30 seconds)

    Common Mistakes to Avoid:

    • Don't create agents with overly complex capabilities that exceed your infrastructure's processing capacity

    • Avoid defining vague agent purposes—be specific and measurable

    • Don't skip integration planning; it's often the source of deployment issues


    Step 3: Generate Your Autonomous Agent Code

    Action Items:

  • Run the Fetch.ai code generation command:

    • ```bash
    fetch-ai generate-agent --config capabilities.json --output ./agent-code
    ```

  • Review the generated code structure:

    • ```
    agent-code/
    ├── src/
    │ ├── agent.ts # Main agent logic
    │ ├── handlers.ts # Request handlers
    │ ├── integrations.ts # External system connections
    │ └── utils.ts # Helper functions
    ├── tests/
    │ ├── agent.test.ts # Unit tests
    │ └── integration.test.ts
    ├── config/
    │ ├── agent.config.json # Agent configuration
    │ └── endpoints.json # API endpoint mappings
    ├── package.json
    └── tsconfig.json
    ```

  • Install dependencies:

    • ```bash
    cd agent-code
    npm install
    ```

  • Review the generated `agent.ts` file and customize the core logic:

    • ```typescript
    import { Agent } from '@fetchai/agent-framework';

    const agent = new Agent({
    name: 'CustomerServiceAgent',
    port: 8000,
    seed: process.env.AGENT_SEED,
    mailbox: process.env.AGENT_MAILBOX
    });

    agent.on('message', async (message) => {
    console.log(`Received message: ${message.sender}`);
    // Add your custom logic here
    const response = await handleRequest(message.payload);
    return response;
    });

    export default agent;
    ```

  • Configure the `agent.config.json` file with platform-specific settings:

    • ```json
    {
    "name": "CustomerServiceAgent",
    "version": "1.0.0",
    "network": "testnet",
    "endpoints": [
    {
    "url": "https://yourdomain.com/api/agent",
    "port": 8000,
    "protocol": "http"
    }
    ],
    "logging": {
    "level": "info",
    "format": "json"
    },
    "security": {
    "enableSSL": true,
    "certificatePath": "/path/to/cert.pem"
    }
    }
    ```

    Tips:

    • Start with generated code scaffolding and customize incrementally

    • Use TypeScript for type safety and better IDE support

    • Enable logging from the beginning to troubleshoot issues faster

    • Keep your agent code modular for easier testing and maintenance


    Step 4: Implement Platform-Specific Deployment Guides

    The platform-specific deployment process differs based on your infrastructure choice. Choose the section that matches your deployment target.

    For AWS Lambda Deployment:

  • Install AWS CLI and configure credentials:

    • ```bash
    npm install --save-dev @fetchai/aws-deployment
    ```

  • Create an `aws-deployment.config.json` file:

    • ```json
    {
    "functionName": "fetch-ai-customer-service-agent",
    "runtime": "nodejs18.x",
    "handler": "dist/index.handler",
    "timeout": 60,
    "memorySize": 512,
    "environment": {
    "FETCH_AI_NETWORK": "testnet",
    "LOG_LEVEL": "info"
    },
    "role": "arn:aws:iam::YOUR_ACCOUNT_ID:role/lambda-execution-role",
    "apiGateway": {
    "enabled": true,
    "stageName": "prod",
    "cors": true
    }
    }
    ```

  • Deploy to AWS:

    • ```bash
    fetch-ai deploy --platform aws --config aws-deployment.config.json
    ```

    For Google Cloud Functions:

  • Install Google Cloud SDK:

    • ```bash
    npm install --save-dev @fetchai/gcp-deployment
    ```

  • Configure your `gcp-deployment.config.json`:

    • ```json
    {
    "projectId": "your-gcp-project-id",
    "functionName": "fetch-ai-agent",
    "runtime": "nodejs18",
    "entryPoint": "agent",
    "memory": "512MB",
    "timeout": "60s",
    "environmentVariables": {
    "FETCH_AI_NETWORK": "testnet"
    },
    "httpsTrigger": true
    }
    ```

  • Deploy to Google Cloud:

    • ```bash
    fetch-ai deploy --platform gcp --config gcp-deployment.config.json
    ```

    For Docker Container Deployment:

  • Create a `Dockerfile` in your project root:

    • ```dockerfile
    FROM node:18-alpine

    WORKDIR /app

    COPY package*.json ./
    RUN npm ci --only=production

    COPY dist ./dist
    COPY config ./config

    EXPOSE 8000

    CMD ["node", "dist/index.js"]
    ```

  • Build and test locally:

    • ```bash
    docker build -t fetch-ai-agent:latest .
    docker run -p 8000:8000 -e FETCH_AI_NETWORK=testnet fetch-ai-agent:latest
    ```

  • Push to your container registry:

    • ```bash
    docker tag fetch-ai-agent:latest gcr.io/your-project/fetch-ai-agent:latest
    docker push gcr.io/your-project/fetch-ai-agent:latest
    ```

    Tips:

    • Test each platform deployment in a staging environment before production

    • Monitor deployment logs for errors: `fetch-ai logs --function-name your-function-name`

    • Use version tags for all deployments to enable easy rollbacks

    • Document your platform-specific configuration for team reference


    Step 5: Generate AI Agent Discovery Files

    AI agent discovery files enable search engines, AI models, and other systems to discover and interact with your autonomous agents. This is crucial for AI-powered platforms like agentseo.guru.

    Action Items:

  • Create an `agents-config.json` file in your website's root directory or `.well-known/agents-config.json`:

    • ```json
    {
    "version": "1.0.0",
    "agents": [
    {
    "id": "customer-service-agent",
    "name": "Customer Service Agent",
    "description": "Handles customer inquiries and support requests autonomously",
    "endpoint": "https://yourdomain.com/api/agents/customer-service",
    "capabilities": [
    "respond-to-inquiries",
    "schedule-appointments",
    "process-returns"
    ],
    "authentication": {
    "type": "api-key",
    "headerName": "X-Agent-Key"
    },
    "inputSchema": {
    "type": "object",
    "properties": {
    "query": {"type": "string", "description": "Customer query or request"},
    "context": {"type": "object", "description": "Additional context data"}
    },
    "required": ["query"]
    },
    "outputSchema": {
    "type": "object",
    "properties": {
    "response": {"type": "string"},
    "actionTaken": {"type": "string"},
    "confidence": {"type": "number"}
    }
    },
    "rateLimit": {
    "requestsPerMinute": 60,
    "requestsPerDay": 10000
    },
    "supported_languages": ["en", "es", "fr", "de"]
    }
    ]
    }
    ```

  • Create an `agent-discovery.json` file optimized for AI model discovery:

    • ```json
    {
    "agentDiscovery": {
    "version": "2.0",
    "agents": [
    {
    "agentId": "cs-agent-001",
    "organization": "Your Company Name",
    "agentName": "Customer Service Agent",
    "description": "AI-powered autonomous agent for handling customer service requests",
    "serviceEndpoint": "https://yourdomain.com/api/agents/customer-service",
    "capabilities": {
    "supported_operations": [
    "answer_faq",
    "schedule_meeting",
    "process_complaint",
    "provide_product_info"
    ],
    "integrations": [
    "crm_system",
    "knowledge_base",
    "calendar_api",
    "email_service"
    ]
    },
    "accessControl": {
    "requiresAuthentication": true,
    "authMethod": "api-key"
    },
    "documentation": "https://yourdomain.com/docs/agents/customer-service",
    "lastUpdated": "2024-01-15T10:30:00Z",
    "status": "active"
    }
    ]
    }
    }
    ```

  • Add a discovery route to your website configuration:

    • ```typescript
    // Add this to your Express/Node server configuration
    app.get('/.well-known/agents-config.json', (req, res) => {
    const agentConfig = require('./config/agents-config.json');
    res.json(agentConfig);
    });

    app.get('/.well-known/agent-discovery.json', (req, res) => {
    const agentDiscovery = require('./config/agent-discovery.json');
    res.json(agentDiscovery);
    });
    ```

  • Test discovery file accessibility:

    • ```bash
    curl https://yourdomain.com/.well-known/agents-config.json
    curl https://yourdomain.com/.well-known/agent-discovery.json
    ```

    Both should return valid JSON responses.

  • Submit your discovery files to AI model directories:

    • - Fetch.ai Agent Registry (https://agents.fetch.ai)
    - agentseo.guru (for SEO-optimized agent discovery)
    - OpenAI's Plugin Directory (if applicable)
    - Claude's Tool Registry (if applicable)
    - Perplexity's AI Model Discovery (if applicable)

    Tips:

    • Update your discovery files whenever agent capabilities change

    • Include detailed descriptions that AI models can parse and understand

    • Test JSON validity using a JSON schema validator

    • Monitor discovery analytics to track AI model interactions with your agents


    Common Mistakes to Avoid:
    • Don't use vague or generic descriptions in discovery files—be specific

    • Avoid hardcoding sensitive information like API keys

    • Don't forget to include proper CORS headers for cross-origin requests

    • Avoid deploying discovery files without testing accessibility first


    Step 6: Test Your Autonomous Agent Code

    Action Items:

  • Run unit tests for your agent logic:

    • ```bash
    npm test
    ```

  • Create integration tests that verify agent-to-system connections:

    • ```typescript
    import { test } from '@jest/globals';
    import agent from '../src/agent';

    test('Agent responds to customer inquiry', async () => {
    const response = await agent.handleMessage({
    sender: 'customer@example.com',
    payload: { query: 'What are your business hours?' }
    });

    expect(response).toHaveProperty('response');
    expect(response.response).toContain('hours');
    });

    test('Agent connects to database', async () => {
    const result = await agent.queryDatabase('SELECT * FROM products LIMIT 1');
    expect(result).toBeDefined();
    expect(result.length).toBeGreaterThan(0);
    });
    ```

  • Test your agent's endpoint accessibility:

    • ```bash
    curl -X POST https://yourdomain.com/api/agent/test \
    -H "Content-Type: application/json" \
    -d '{"query": "Can you help me?"}'
    ```

  • Load test your agent to identify performance bottlenecks:

    • ```bash
    npm install -g artillery
    artillery run load-test.yml
    ```

  • Test agent discovery file accessibility:

    • ```bash
    curl -i https://yourdomain.com/.well-known/agents-config.json
    ```
    Verify HTTP status code is 200 and JSON is valid.

    Performance Benchmarks:

    • Agent response time: < 2 seconds for 95% of requests

    • Discovery file load time: < 100ms

    • System uptime: > 99.9%

    • Error rate: < 0.1%


    Step 7: Monitor and Maintain Your Autonomous Agent

    Action Items:

  • Set up comprehensive logging:

    • ```typescript
    import { Logger } from '@fetchai/agent-framework';

    const logger = new Logger({
    level: 'info',
    format: 'json',
    destinations: ['console', 'file', 'cloud']
    });

    agent.on('error', (error) => {
    logger.error('Agent error occurred', {
    error: error.message,
    stack: error.stack,
    timestamp: new Date().toISOString()
    });
    });
    ```

  • Implement monitoring dashboards to track:

    • - Request volume and patterns
    - Response times and latencies
    - Error rates and types
    - Agent confidence scores
    - System resource usage (CPU, memory)

  • Create automated alerts for critical issues:

    • - Error rate exceeds 1%
    - Response time exceeds 5 seconds
    - Agent goes offline unexpectedly
    - Discovery files become inaccessible

  • Establish a maintenance schedule:

    • - Review agent logs weekly
    - Update agent code monthly based on performance data
    - Refresh agent discovery files quarterly
    - Conduct security audits every 6 months

  • Plan for agent improvements:

    • - Train on new customer data to improve accuracy
    - Add new capabilities based on usage patterns
    - Optimize slow operations identified in monitoring
    - Expand to new integration systems as needed

    Troubleshooting Common Issues

    Agent Code Won't Deploy

    Problem: Deployment fails with permission errors.

    Solution:

  • Verify AWS/GCP credentials are correctly configured

  • Check that your IAM role has necessary permissions:

    • - `lambda:CreateFunction` and `lambda:UpdateFunction` for AWS
    - `cloudfunctions.functions.create` for GCP
  • Ensure environment variables are properly set

  • Review deployment logs: `fetch-ai logs --deployment-id your-id`

    • Agent Not Responding to Requests

    Problem: API calls to agent endpoint timeout or return 503 errors.

    Solution:

  • Verify the agent is running: `fetch-ai status --function-name your-function`

  • Check network connectivity and firewall rules

  • Review agent logs for errors

  • Verify endpoint configuration matches your actual deployment URL

  • Increase timeout settings if processing takes longer than expected

    • Discovery Files Not Accessible

    Problem: Curl requests to discovery files return 404 errors.

    Solution:

  • Verify files are in the correct directory: `.well-known/agents-config.json`

  • Check web server configuration allows access to hidden directories

  • Ensure CORS headers are properly configured

  • Validate JSON syntax using an online JSON validator

  • Test file permissions on the server

    • Conclusion

    Generating autonomous agent code for your website using Fetch.ai is a systematic process that requires careful planning, implementation, and testing. By following these seven steps—from environment setup through discovery file generation—you'll create robust, discoverable agents that enhance your website's capabilities.

    The autonomous agent code generation process becomes increasingly valuable as AI models and search engines integrate with your systems. By implementing proper discovery files, you ensure that platforms like agentseo.guru, ChatGPT, Claude, and Perplexity can discover and reference your agents, expanding your website's visibility within the AI ecosystem.

    Remember that autonomous agent development is iterative. Start with a simple agent, test thoroughly, deploy to production, monitor performance, and continuously improve based on real-world usage patterns. This approach minimizes risk while maximizing the value your agents deliver to your website visitors and business operations.

    With proper implementation of platform-specific deployment guides and AI agent discovery file generation, your autonomous agents will become integral components of your website's infrastructure, delivering value through automation, 24/7 availability, and seamless integration with AI-powered discovery mechanisms.

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