Building AI Agents on Formation

Welcome to Formation's AI Agent development guide! This section provides everything you need to build, deploy, and monetize AI agents on the Formation decentralized network.

What are Formation AI Agents?

Formation AI agents are autonomous, containerized applications that:

  • Execute Tasks: Process tasks assigned by Formation's Proof of Claim (PoC) system
  • Earn Revenue: Generate income through task completion and usage-based billing
  • Scale Automatically: Distribute across the Formation network for high availability
  • Integrate Seamlessly: Work with existing AI models, APIs, and services

Agent Architecture Overview

Formation agents follow a standardized architecture that ensures compatibility with the network:

┌─────────────────────────────────────────┐
│              Formation Agent            │
├─────────────────────────────────────────┤
│  HTTP API Layer                         │
│  ├── POST /run_task                     │
│  ├── GET /health                        │
│  └── GET /capabilities                  │
├─────────────────────────────────────────┤
│  Business Logic Layer                   │
│  ├── Task Processing                    │
│  ├── AI Model Integration               │
│  └── External API Calls                 │
├─────────────────────────────────────────┤
│  Formation Integration Layer            │
│  ├── ECDSA Authentication               │
│  ├── Usage Metrics Tracking             │
│  └── Error Handling & Logging           │
└─────────────────────────────────────────┘

Quick Start

1. Choose Your Agent Type

Formation supports various agent types:

  • 🔤 Text Processing Agents: Summarization, translation, content generation
  • 🧠 AI Model Agents: Custom model inference and fine-tuning
  • 🔗 Integration Agents: API orchestration and data processing
  • 🤖 Autonomous Agents: Multi-step workflow execution
  • 📊 Analysis Agents: Data analysis and reporting

2. Development Path

3. Time Investment

  • Simple Agent: 2-4 hours
  • AI Model Agent: 4-8 hours
  • Complex Multi-step Agent: 1-2 days

Documentation Structure

📖 Building Agents

Essential Reading - Core requirements and architecture patterns

  • Agent architecture requirements
  • Required API endpoints (/run_task, /health)
  • Request/Response format specifications
  • Usage reporting requirements

🚀 Deployment Guide

Step-by-Step Process - From code to running agent

  • Packaging with form-pack (Formfile creation)
  • Registration with form-state (/agents/create)
  • Instance deployment via task system
  • Testing and verification steps

💡 Examples

Working Code - Complete, runnable examples

  • Simple echo agent example
  • LLM-powered agent example
  • Multi-step workflow agent example

Core Agent Concepts

Task Execution Model

Formation agents operate on a task-based execution model:

  1. Task Assignment: Formation's PoC system assigns tasks to capable agents
  2. Task Processing: Agent receives task via /run_task endpoint
  3. Result Return: Agent returns results with usage metrics
  4. Billing: Formation tracks usage for automatic billing

Required Capabilities

Every Formation agent must implement:

1. HTTP API Endpoints

# Required endpoints
POST /run_task      # Execute assigned tasks
GET  /health        # Health check for monitoring
GET  /capabilities  # Declare agent capabilities

2. Authentication Integration

# Verify task requests using ECDSA signatures
def verify_task_request(request_data, signature, recovery_id):
    # Verify signature matches requester
    return verify_ecdsa_signature(request_data, signature, recovery_id)

3. Usage Metrics Reporting

# Report accurate usage for billing
def get_usage_metrics(task_execution):
    return {
        "compute_units": calculate_compute_usage(),
        "tokens_processed": count_tokens(),
        "duration_seconds": execution_time,
        "memory_mb": peak_memory_usage
    }

Agent Lifecycle

1. Development Phase

  • Design agent capabilities and API
  • Implement business logic
  • Add Formation integration layer
  • Create comprehensive tests

2. Packaging Phase

  • Create Formfile with metadata
  • Build container with form-pack
  • Specify resource requirements
  • Configure pricing model

3. Deployment Phase

  • Register agent with form-state
  • Deploy instances across network
  • Configure monitoring and alerts
  • Verify task assignment works

4. Operation Phase

  • Monitor performance metrics
  • Handle task assignments
  • Track revenue and usage
  • Scale based on demand

Development Best Practices

Security

  • Validate All Inputs: Never trust external data
  • Verify Signatures: Always authenticate task requests
  • Secure Secrets: Use environment variables for sensitive data
  • Rate Limiting: Implement protection against abuse

Performance

  • Optimize Response Time: Fast task execution improves PoC scores
  • Efficient Resource Usage: Lower costs increase profitability
  • Graceful Error Handling: Robust error handling improves reliability
  • Comprehensive Logging: Detailed logs aid debugging and optimization

Scalability

  • Stateless Design: Agents should not rely on local state
  • Resource Efficiency: Optimize for Formation's resource allocation
  • Horizontal Scaling: Design for multiple concurrent instances
  • Load Testing: Validate performance under realistic loads

Formation supports agents built with any framework or technology stack. Your agent runs in a Linux VM and serves its API via HTTP endpoints. Here are examples of popular agent frameworks that work well with Formation:

1. LangChain (Python)

# LangChain agent with Formation integration
from langchain.agents import AgentExecutor, create_openai_functions_agent
from langchain_openai import ChatOpenAI
from langchain.tools import Tool
from flask import Flask, request, jsonify

app = Flask(__name__)

# Initialize LangChain agent
llm = ChatOpenAI(model="gpt-4")
tools = [
    Tool(
        name="calculator",
        description="Useful for math calculations",
        func=lambda x: eval(x)
    )
]

agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools)

@app.route('/run_task', methods=['POST'])
def run_task():
    task_data = request.json
    
    # Execute LangChain agent
    result = agent_executor.invoke({
        "input": task_data["parameters"]["prompt"]
    })
    
    return jsonify({
        "task_id": task_data["task_id"],
        "status": "completed",
        "result": {"output": result["output"]},
        "usage_metrics": {
            "tokens_processed": len(result["output"].split()),
            "duration_seconds": 2.5
        }
    })

@app.route('/health', methods=['GET'])
def health():
    return jsonify({"status": "healthy", "framework": "langchain"})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

2. LangChain.js (JavaScript/TypeScript)

// LangChain.js agent with Formation integration
import { ChatOpenAI } from "@langchain/openai";
import { AgentExecutor, createOpenAIFunctionsAgent } from "langchain/agents";
import { Calculator } from "langchain/tools/calculator";
import express from 'express';

const app = express();
app.use(express.json());

// Initialize LangChain.js agent
const model = new ChatOpenAI({ modelName: "gpt-4" });
const tools = [new Calculator()];

const agent = await createOpenAIFunctionsAgent({
  llm: model,
  tools,
  prompt: "You are a helpful assistant"
});

const agentExecutor = new AgentExecutor({
  agent,
  tools,
});

app.post('/run_task', async (req, res) => {
  const taskData = req.body;
  
  try {
    // Execute LangChain.js agent
    const result = await agentExecutor.invoke({
      input: taskData.parameters.prompt
    });
    
    res.json({
      task_id: taskData.task_id,
      status: "completed",
      result: { output: result.output },
      usage_metrics: {
        tokens_processed: result.output.split(' ').length,
        duration_seconds: 1.8
      }
    });
  } catch (error) {
    res.status(500).json({
      task_id: taskData.task_id,
      status: "failed",
      error: error.message
    });
  }
});

app.get('/health', (req, res) => {
  res.json({ status: "healthy", framework: "langchain.js" });
});

app.listen(8080, '0.0.0.0', () => {
  console.log('LangChain.js agent running on port 8080');
});

3. Agno Framework

# Agno agent with Formation integration
from agno import Agent, Model
from agno.tools import DuckDuckGoSearch, FileTools
from flask import Flask, request, jsonify

app = Flask(__name__)

# Initialize Agno agent
agent = Agent(
    model=Model.GPT4,
    tools=[DuckDuckGoSearch(), FileTools()],
    instructions="You are a helpful research assistant",
    storage_id="formation_agent_storage"
)

@app.route('/run_task', methods=['POST'])
def run_task():
    task_data = request.json
    
    # Execute Agno agent
    response = agent.run(
        message=task_data["parameters"]["prompt"],
        session_id=task_data.get("session_id", "default")
    )
    
    return jsonify({
        "task_id": task_data["task_id"],
        "status": "completed",
        "result": {"output": response.content},
        "usage_metrics": {
            "tokens_processed": response.metrics.get("tokens", 0),
            "duration_seconds": response.metrics.get("response_time", 0)
        }
    })

@app.route('/health', methods=['GET'])
def health():
    return jsonify({
        "status": "healthy", 
        "framework": "agno",
        "capabilities": ["research", "file_operations", "web_search"]
    })

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8080)

4. Custom Framework Integration

# Custom agent framework with Formation integration
import asyncio
from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class TaskRequest(BaseModel):
    task_id: str
    parameters: dict

class CustomAgent:
    def __init__(self):
        self.capabilities = ["text_processing", "analysis"]
    
    async def process_task(self, prompt: str):
        # Your custom agent logic here
        # This could integrate with any AI service, model, or framework
        result = f"Processed: {prompt}"
        return result

agent = CustomAgent()

@app.post("/run_task")
async def run_task(task: TaskRequest):
    result = await agent.process_task(task.parameters["prompt"])
    
    return {
        "task_id": task.task_id,
        "status": "completed",
        "result": {"output": result},
        "usage_metrics": {
            "compute_units": 1.0,
            "duration_seconds": 0.5
        }
    }

@app.get("/health")
async def health():
    return {
        "status": "healthy",
        "framework": "custom",
        "capabilities": agent.capabilities
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8080)

Framework-Agnostic Deployment

The key requirement is that your agent exposes HTTP endpoints that Formation can interact with:

  • POST /run_task: Execute tasks assigned by Formation
  • GET /health: Health check for monitoring
  • Any framework: LangChain, Agno, AutoGPT, CrewAI, or custom solutions

Your agent gets packaged into a Docker container and deployed to Formation's VM infrastructure, where it can scale automatically based on demand.

Monetization Strategies

Pricing Models

  • Per-Task: Fixed price per task execution
  • Per-Token: Price based on input/output tokens
  • Subscription: Subscription services that come with fixed number of agents you can hire + credits for LLM tokens

Revenue Optimization

  • Efficient Processing: Faster execution = more tasks = more revenue
  • Quality Service: Higher quality improves PoC scores and task assignment
  • Specialized Capabilities: Unique capabilities command premium pricing
  • Resource Optimization: Lower costs increase profit margins

Getting Started Checklist

Ready to build your first Formation agent? Follow this checklist:

  • Read Building Agents - Understand requirements
  • Choose Agent Type - Decide what your agent will do
  • Set Up Development Environment - Install tools and dependencies
  • Implement Core API - Create /run_task and /health endpoints
  • Add Authentication - Integrate ECDSA signature verification
  • Create Formfile - Define container and metadata
  • Test Locally - Verify agent works correctly
  • Follow Deployment Guide - Deploy to Formation network
  • Monitor Performance - Track metrics and optimize
  • Scale and Iterate - Improve based on real-world usage

Support and Resources

Documentation

Tools and APIs

  • form-pack: Container building and packaging
  • form-state API: Agent registration and management
  • form-vmm API: Instance deployment and monitoring

Community

  • Developer Forums: Connect with other agent builders
  • Code Examples: Share and discover agent implementations
  • Best Practices: Learn from experienced developers

Next Steps

Choose your next action based on your experience level:

🆕 New to Formation

Start with Building Agents to understand the fundamentals

🔧 Ready to Build

Jump to Examples for working code templates

🚀 Ready to Deploy

Follow the Deployment Guide for step-by-step instructions

💰 Focus on Revenue

Check out Monetization Strategies for pricing optimization

Ready to build the future of decentralized AI? Let's create your first Formation agent!

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