Building Custom AI Agents (Your AI, Your Rules)

Faktora.ai enables developers to create, train, and deploy custom AI agents that automate DeFi operations, execute trades, manage liquidity, and interact with smart contracts—all without requiring constant human intervention.

Unlike traditional DeFi bots, Faktora AI agents are modular, adaptive, and can be customized with different execution strategies. Developers can:

Define unique agent personalities & decision-making logic
Train agents to optimize for yield farming, arbitrage, market-making, or treasury management
Connect agents to multiple DeFi protocols using a plug-and-play architecture

Step-by-Step: Building a Custom AI Agent

1️⃣ Define Agent Behavior & Execution Strategy

Developers can specify how an AI agent should behave using natural language commands or predefined templates.

{
  "name": "Yield Optimizer",
  "description": "AI agent that allocates funds to the highest-yielding DeFi pools",
  "strategy": {
    "liquiditySources": ["Aave", "Compound", "Curve"],
    "riskTolerance": "medium",
    "executionFrequency": "hourly"
  }
}

💡 This JSON blueprint defines an AI agent that scans DeFi protocols for the best yield farming opportunities and reallocates funds dynamically.

2️⃣ Connect to DeFi Protocols & Smart Contracts

Faktora AI agents interact with multiple DeFi ecosystems using modular protocol adapters.

Example: Connecting an AI agent to Uniswap & Aave.

import { UniswapV3 } from "faktora-ai";
import { AaveLending } from "faktora-ai";

const agent = new FaktoraAI.Agent({
  name: "Liquidity Manager",
  protocols: [UniswapV3, AaveLending],
  strategy: "optimize yield and rebalance liquidity",
});

💡 This allows the agent to execute swaps on Uniswap while managing lending positions on Aave.

3️⃣ Implement AI-Driven Execution Logic

Faktora AI agents continuously refine their execution based on market conditions.

Example: AI dynamically adjusting staking allocations.

if (marketVolatility > threshold) {
  agent.reallocateFunds("Aave", "Compound", "stable-yield");
} else {
  agent.holdPosition();
}

💡 This ensures the AI agent reacts to real-time market movements, reducing risk exposure.

4️⃣ Deploy & Monitor AI Agents in Real-Time

Once configured, AI agents can be deployed on-chain, where they execute transactions autonomously.

✅ Monitor agent performance via the Faktora dashboard ✅ Adjust execution parameters dynamically without redeploying contracts ✅ Enable/disable AI agent actions at any time

Example: Deploying an AI agent with real-time monitoring.

agent.deploy({
  network: "Ethereum",
  executionMode: "autonomous",
});

💡 Agents operate fully on-chain but can be manually adjusted by developers at any time.

Use Cases: What You Can Build with Faktora AI Agents

Use Case

Example AI Agent Functionality

Yield Optimization

AI agent reallocates liquidity based on highest available APY across DeFi protocols.

Arbitrage Execution

Agent detects price discrepancies & executes cross-exchange arbitrage trades.

MEV Protection

AI prevents frontrunning by using private relays & transaction bundling.

On-Chain Treasury Management

Agent manages DAO treasury funds, allocating capital based on governance-defined risk parameters.

The flexibility of Faktora AI means developers can create AI agents for virtually any DeFi use case.

💡 With Faktora.ai, developers have full control over how AI interacts with DeFi—no limitations, just possibilities.

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Last updated 2 months ago

{ "name": "Yield Optimizer", "description": "AI agent that allocates funds to the highest-yielding DeFi pools", "strategy": { "liquiditySources": ["Aave", "Compound", "Curve"], "riskTolerance": "medium", "executionFrequency": "hourly" } }

import { UniswapV3 } from "faktora-ai"; import { AaveLending } from "faktora-ai"; const agent = new FaktoraAI.Agent({ name: "Liquidity Manager", protocols: [UniswapV3, AaveLending], strategy: "optimize yield and rebalance liquidity", });