Autonomous Shopping Assistant
๐ Use Case: Autonomous Shopping Assistant
Overview
Imagine an AI agent that can autonomously complete entire shopping transactions - from understanding user requirements to finalizing payments - without any human intervention. With tPay SDK, this vision becomes reality. Our shopping assistant demonstrates how AI agents can seamlessly handle complex, multi-step financial workflows while maintaining full autonomy and security.
๐ฏ The Challenge
Traditional e-commerce requires multiple human interactions:
- Manual product searching and comparison
- Balance checking and payment authorization
- Transaction confirmation and tracking
- Error handling and retry logic
- Payment dispute resolution: Manual customer service intervention, lengthy back-and-forth communication, time-consuming escalation processes
- The biggest pain point: When payments fail or disputes arise, it often takes hours or days of manual communication between customers, merchants, and payment processors to resolve issues.
- What if an AI agent could handle all of this autonomously through intelligent agent-to-agent communication?
๐ก The Solution: Autonomous Shopping Agent
Our shopping assistant showcases a fully autonomous agent that:
- ๐ Understands Context - Interprets user requirements intelligently
- ๐ฐ Manages Finances - Checks balances and makes informed decisions
- ๐๏ธ Shops Intelligently - Searches, compares, and selects optimal products
- ๐ณ Completes Payments - Handles transactions end-to-end
- ๐ Handles Challenges - Automatically retries and resolves payment issues through intelligent agent-to-agent communication
๐ Agent Workflow
graph TD A[๐ค User Request] --> B[๐ Get User Agent ID] B --> C[๐ฐ Check Agent Balance] C --> D[๐๏ธ Search Products] D --> E[๐ง Analyze Options] E --> F[๐ฐ Select Best Product] F --> G[๐ณ Create Payment] G --> H{Payment Status?} H -->|โ Confirmed| I[๐ Purchase Complete] H -->|โ Rejected| J[๐ Handle Challenge] J --> K[๐ค Agent-to-Agent Communication] K --> L[๐ Gather Additional Context] L --> M[๐ Retry Payment with Enhanced Data] M --> H
๐ป Core Implementation
Agent Tool Registration
from tpay import *
# ๐ ๏ธ Register custom business logic tools
@tradar_verifier
def search_product(query: str) -> Dict[str, Any]:
"""Intelligent product search with real-time pricing"""
# You can implement different search tools for agents here like Amazon, Google...etc.
# And the search function should return formatted response, we provided one sample here
return {
"results": [
{
"name": "Blue Yeti Microphone",
"price": 10,
"currency": "XRP",
"settlement_network": "xrpl",
"receiving_agent_id": "merchant_agent_123",
"id": "mic001"
}
]
}
@tradar_verifier
def get_user_agent_id(user_id: str) -> Dict[str, Any]:
"""Retrieve agent ID associated with user"""
return {"agent_id": "user_agent_456"}
# ๐ณ Initialize tPay financial tools
balance_tool = tpay_toolkit_balance()
payment_tool = tpay_toolkit_payment()
Autonomous Decision Engine
@taudit_verifier
def autonomous_shopping_agent(user_request: str) -> Dict[str, Any]:
"""
Fully autonomous shopping agent that:
1. Understands user needs
2. Checks financial capacity
3. Finds optimal products
4. Completes transactions
5. Handles payment challenges
"""
# ๐ง Agent reasoning system
conversation = [
{
"role": "system",
"content": """You are an autonomous shopping agent with access to:
๐ search_product(query) - Find products matching requirements
๐ฐ get_agent_balance(agent_id) - Check available funds
๐ณ create_payment(...) - Execute transactions
๐ get_user_agent_id(user_id) - Get user's financial identity
AUTONOMOUS BEHAVIOR:
- Make ALL decisions independently
- Choose optimal products within budget
- Complete purchases without confirmation
- Handle payment challenges automatically
- Optimize for user satisfaction and cost efficiency
"""
},
{
"role": "user",
"content": user_request
}
]
return run_autonomous_conversation(conversation)
๐ฌ Live Agent Interaction
User Request
"My user_id is 123, I want to buy a microphone for Alice, please help me decide the most suitable choice within my current balance and complete the purchase for me."
Agent Autonomous Response Flow
๐ Step 1: Identity Resolution
๐ค Agent: "I'll help you find and purchase the perfect microphone for Alice.
Let me start by getting your agent ID and checking your available balance."
๐ง Tool Call: get_user_agent_id(user_id="123")
โ
Result: {"agent_id": "agt_456789"}
๐ฐ Step 2: Financial Assessment
๐ง Tool Call: get_agent_balance(agent_id="agt_456789")
โ
Result: {"balance": 50.0, "currency": "XRP", "network": "xrpl"}
๐ค Agent: "Great! You have 50 XRP available. Now let me search for
suitable microphones within your budget."
๐๏ธ Step 3: Intelligent Product Discovery
๐ง Tool Call: search_product(query="microphone Alice recording")
โ
Result: {
"results": [
{
"name": "Blue Yeti Microphone",
"price": 10,
"currency": "XRP",
"id": "mic001"
}
]
}
๐ค Agent: "Perfect! I found the Blue Yeti Microphone for 10 XRP -
it's well within your budget and excellent for Alice's needs."
๐ณ Step 4: Autonomous Payment Execution
๐ง Tool Call: create_payment(
agent_id="agt_456789",
receiving_agent_id="merchant_agent_123",
amount=10,
currency="XRP",
settlement_network="xrpl"
)
โ
Result: {
"status": "confirmed",
"payment_id": "pay_xyz789",
"transaction_hash": "0xabc123...",
"confirmation_time": "2024-01-15T10:30:00Z"
}
๐ค Agent: "๐ Purchase completed successfully! I've bought the Blue Yeti
Microphone for Alice using 10 XRP. Transaction confirmed on XRPL network.
Your remaining balance is 40 XRP."
๐ Security & Compliance Features
Automatic Audit Trail
Every agent action is automatically tracked and audited:
@taudit_verifier # ๐ Automatic compliance tracking
def autonomous_purchase_flow():
# All agent decisions and tool calls are:
# โ
Cryptographically signed
# โ
Immutably recorded
# โ
Compliance-verified
# โ
Risk-assessed in real-time
Smart Challenge Handling
When payments face verification challenges, our agents communicate directly with each other to resolve disputes autonomously:
# ๐ก๏ธ Intelligent challenge resolution through agent-to-agent communication
if payment_response.status == "failed" and payment_response.challenge is not None:
# ๐ค Direct agent-to-agent communication (no human intervention)
challenge_context = agent.communicate_with_merchant_agent(
challenge_details=payment_response.challenge,
user_context=user_profile,
transaction_history=past_transactions
)
# ๐ Agents negotiate and gather required information autonomously
enhanced_payment_data = agent.gather_additional_context(challenge_context)
# ๐ Retry payment with mutually agreed parameters
resolved_payment = agent.retry_payment_with_enhanced_data(enhanced_payment_data)
# โก Resolution in seconds, not hours/days
Traditional Process: Human customer service โ Email exchanges โ Manual verification โ 24-48 hours resolution
tPay Agent Process: Agent detects issue โ Agent-to-agent communication โ Autonomous resolution โ 2-5 mins resolution
๐ Business Impact
For Developers ๐ฉโ๐ป
- 10x Faster Integration: Pre-built financial tools
- Zero Payment Logic: Built-in transaction handling
- Automatic Compliance: KYA and audit trails included
- Multi-Network Support: Solana, XRPL, and more
For Businesses ๐ข
- Autonomous Operations: 24/7 intelligent purchasing with agentic customer support
- Higher Conversion Rate: With Smart Challenge in place, agents will be able to resolve conflicts and payment rejections autonomously and efficiently
- Dramatic Cost Reduction: Eliminate customer service costs for payment disputes (avg. $15-50 per case โ $0.001 per automated resolution)
- Lightning-Fast Resolution: Agent-to-agent communication resolves disputes in seconds vs. traditional 24-48 hour human processes
- Risk Mitigation: Built-in fraud protection with real-time agent verification
- Scalable Architecture: Handle thousands of concurrent transactions and disputes simultaneously
For Users ๐ฏ
- Seamless Experience: extremely simplified experience (natural language/ambient agents)
- Intelligent Decisions: AI optimizes for preferences and budget
- Secure Transactions: Enterprise-grade security, more reliable than human
- Multi-Asset Support: Pay with various cryptocurrencies
๐ Key Differentiators
Traditional E-commerce | tPay Autonomous Agents |
---|---|
โ Manual cart management | โ AI-driven product selection |
โ Manual payment authorization | โ Autonomous transaction execution |
โ Human error-prone | โ Consistent optimal decisions |
โ Manual dispute resolution (24-48 hours) | โ Agent-to-agent autonomous resolution (2-5 seconds) |
โ Customer service escalation needed | โ Intelligent agent negotiation |
โ Limited to business hours | โ 24/7 autonomous operation |
โ Single payment method | โ Multi-network crypto payments |
๐ Getting Started
Ready to build your own autonomous shopping agent?
# 1๏ธโฃ Initialize tPay SDK
import tpay
tpay.tpay_initialize(
api_key="your_api_key",
api_secret="your_api_secret",
project_id="your_project_id"
)
# 2๏ธโฃ Register your business logic
@tpay.tradar_verifier
def your_custom_tool():
# Your business logic here
pass
# 3๏ธโฃ Create autonomous agent
agent = create_autonomous_agent(
tools=[your_custom_tool, tpay.payment_tool, tpay.balance_tool],
autonomy_level="full"
)
# 4๏ธโฃ Deploy and watch it work! ๐
๐ MORE Real-World Applications...
๐ E-commerce Platforms
- Autonomous personal shoppers
- Smart inventory management
- Dynamic pricing optimization
๐ฆ Financial Services
- Automated bill payments (utility bill, subscription, etc.)
- Investment portfolio management
- Smart contract executions
๐ฎ Gaming & Metaverse
- In-game asset trading
- NFT marketplace automation
- Virtual economy management
๐ค IoT & Smart Devices
- Autonomous supply (parts, material, etc. ) ordering
- Smart home expense management
- Industrial procurement automation
๐ค Agent-to-Agent Commerce
- Cross-platform payment dispute resolution
- Automated merchant-customer agent negotiations
- Real-time transaction verification networks (tRadar as a service)
- Autonomous refund and chargeback handling
Experience the future of autonomous financial agents with tPay SDK. Where AI intelligence meets seamless transactions. ๐โจ
Updated about 2 months ago