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🤖 World's First AI Agent Payment Risk Solution

Secure Autonomous Commerce

Payment Risk Assessment for AI Agents

Industry-leading ML predictions trained on 70,000+ agent scenarios—not rule-based guesswork. Know your risk before your processor does. Real-time risk scoring, PSP approval odds for 7 major processors, purpose-built for autonomous agent transactions.

70k+ Agents
Training Dataset
7 PSPs
Real Approval Odds
Access ML API →See How It Works

The Agentic Commerce Payment Problem

AI agents are beginning to transact autonomously, but no payment processor knows how to assess their risk.

⛔

PSPs Reject Agents

Stripe, PayPal, Square have no framework to assess agent transactions. Result: blanket rejections or surprise terminations.

❓

No Risk Standards

Zero industry consensus on what makes an agent "safe" to transact. Developers build blind with no compliance roadmap.

🔥

$Billions at Stake

Autonomous commerce will be a multi-trillion dollar market. Without payment rails, it can't scale.

How AgentGuard Works

Three ML-powered APIs that give agents the payment intelligence they need to transact safely.

API #1: POST /v1/score

Real-Time Risk Score

Submit agent profile → get 0-100 AgentScore + risk band (Excellent/Good/Fair/Poor) in under 500ms.

{ "score": 94.2, "risk_band": "Excellent", "referral_probability": 0.89 }
Training Data
70,000
Synthetic agent profiles
PSP Coverage
7 Processors
Stripe, PayPal, Checkout.com, Adyen, Square, Braintree, Authorize.net
API #2: POST /v1/psp_odds

PSP Approval Prediction

Get real approval odds for 7 major payment processors based on synthetic agent-PSP approval patterns.

81%
Checkout.com
64%
Braintree
63%
PayPal
25%
Stripe
Post-VAMP Oct 2025 Compliance
✓ Visa TAP Integration
+16pp approval lift
✓ Human-in-Loop Detection
+12pp approval lift
✓ Enhanced KYC Scoring
Granular compliance verification
API #3: POST /v1/oracle

Combined Oracle Endpoint

One call → Complete payment readiness report: AgentScore, all PSP odds, risk band, and recommended actions.

Perfect for:
🤖 Agent Dashboards
Real-time payment health monitoring
🔄 Pre-Transaction Checks
Verify payment viability before initiating
📊 Portfolio Analytics
Aggregate risk across agent fleets

Production-Ready Infrastructure

⚡ Sub-500ms Latency

Google Cloud Run with warm instances. Model caching eliminates cold starts.

p95: <700ms | p50: <200ms

🔒 Enterprise Security

API key authentication via Secret Manager. All requests logged to BigQuery for audit.

SOC 2 Type II ready

📈 Auto-Scaling

1 to 10 instances automatically. Handles traffic spikes without manual intervention.

99.9% uptime SLA

🧠 Self-Improving

Monthly retraining with real merchant outcomes. Model drift monitoring via BigQuery.

Automated retraining pipeline

📊 BigQuery Integration

7 BQML models (one per PSP) + prediction audit logs. Full data lineage for compliance.

30-day retention default

🔗 Simple Integration

RESTful JSON API. Python client library included. OpenAPI spec available.

10 minutes to first call

Built for the Agentic Economy

🛒

Shopping Agents

Verify payment readiness before autonomous purchases. Know which PSP will approve the transaction.

💼

AI Marketplaces

Risk-score agent sellers before allowing transactions. Prevent chargebacks with pre-screening.

🤝

Agent-to-Agent Commerce

Mutual risk assessment before transactions. Both parties verify payment compliance.

💳

Subscription Agents

Optimize payment routing for recurring charges. Maximize approval rates across PSPs.

📦

Logistics Agents

Real-time payment verification for autonomous shipping and fulfillment operations.

🎮

Gaming & Metaverse

Enable NPCs and in-game agents to transact safely. Real-time risk scoring for virtual economies.

Simple Integration

10 minutes from API key to first prediction

# Python Client Library (Async)
from agentguard.ml_client import AgentGuardMLClient

client = AgentGuardMLClient()

agent_profile = {
    "agent_vertical": "shopping",
    "control_model": "hybrid",
    "avg_txn_value_usd": 120.0,
    "monthly_txn_count": 450,
    "uses_visa_tap": True,
    "human_in_loop": True,
    "kyc_level": "enhanced"
}

# Get AgentScore + PSP Odds in one call
result = await client.predict_oracle(agent_profile)

print(f"AgentScore: {result['score']}")
print(f"Risk Band: {result['risk_band']}")
print(f"Best PSP: {result['recommended_psp']}")
print(f"Approval Odds: {result['psp_odds']}")

# Output:
# AgentScore: 94.2
# Risk Band: Excellent
# Best PSP: Checkout.com
# Approval Odds: {'checkout': 0.81, 'braintree': 0.64, ...}

Early Access Pricing

First 100 developers get preferential pricing + equity options

DEVELOPER
Free
1,000 requests/month
  • ✓ All 3 APIs
  • ✓ Python client library
  • ✓ 30-day audit logs
  • ✓ Email support
Join API Waitlist
MOST POPULAR
PRODUCTION
$299/mo
100,000 requests/month
  • ✓ Everything in Developer
  • ✓ 99.9% uptime SLA
  • ✓ Priority support
  • ✓ Custom alerts
  • ✓ White-label option
Join API Waitlist

🚀 First 100 developers: 5-10% equity option available for strategic partners building in public

🧠 Methodology & Data Transparency

AgentGuard predictions are powered by machine learning models trained on 70,000+ synthetic agent profiles across 7 major payment processors and 7 industries—more training data than any AI agent payment tool on the market.

Why ML vs Rule-Based Systems: While most payment risk tools use static rules, AgentGuard adapts to real PSP behavior patterns learned from 70,000+ scenarios. Our models identify approval patterns that simple rules miss.

Data Type: Synthetic training data generated to reflect real PSP approval patterns while protecting merchant privacy.

For educational and informational purposes. Not financial or legal advice. Real-world PSP approval decisions depend on many factors beyond model predictions.

See Full Competitive Comparison →

Build the Future of Commerce

The first developers who build with AgentGuard will define how autonomous agents transact.

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Free tier • No credit card required • 10-minute integration