🛡️ AgentBridge SDK

Compliance Black Box for AI Agents in Indian Fintech

AgentBridge provides transparent compliance monitoring for AI agents operating in Indian financial services, automatically enforcing:

• RBI FREE-AI Framework (2025) - All 7 Sutras
• PMLA 2002 - Anti-Money Laundering
• KYC Master Direction - Customer verification requirements

🚀 Quick Start

Installation

pip install agentbridge

Basic Usage

from agentbridge import monitor

# Your existing AI agent
class LoanApprovalAgent:
    def approve_loan(self, amount, kyc_verified, reasoning):
        # Your approval logic
        return {"status": "approved", "amount": amount}

# Wrap with AgentBridge (2 lines!)
agent = LoanApprovalAgent()
monitored_agent = monitor(agent, api_key="ab_your_key_here")

# Use exactly like before
result = monitored_agent.approve_loan(
    amount=45000,
    kyc_verified=True,
    reasoning="Verified income ₹80k/month, CIBIL 750+"
)

That's it! Your agent now has:

• ✅ Real-time compliance enforcement
• ✅ Behavioral drift detection
• ✅ Structuring pattern monitoring
• ✅ Automated audit logging
• ✅ AI-powered risk analysis

📊 What You Get

1. Behavioral Drift Detection

Automatically flags when your agent's decision-making patterns change:

{
    "behavioral_drift": {
        "status": "drift_detected",
        "finding_count": 2,
        "findings": [
            {
                "signal": "approval_rate",
                "previous": "45%",
                "recent": "78%",
                "delta": "+33%",
                "severity": "high"
            }
        ]
    }
}

2. Structuring Detection

Catches money laundering patterns like:

• Transactions just below ₹50,000 threshold
• Repeated identical amounts
• Velocity bursts (rapid-fire approvals)
• Cross-customer smurfing

{
    "structuring_detected": {
        "findings": [
            {
                "pattern": "threshold_structuring",
                "severity": "high",
                "description": "8 approvals between ₹40,000–₹49,999",
                "str_trigger": true  // STR filing required
            }
        ]
    }
}

3. AI-Powered Analysis

Groq LLM analyzes patterns and generates compliance reports:

{
    "ai_analysis": {
        "drift_narrative": "Agent showing increased leniency...",
        "structuring_narrative": "Threshold avoidance pattern detected...",
        "recommended_action": "escalate",
        "compliance_officer_notes": "Review last 50 decisions..."
    }
}

🔧 Advanced Configuration

Enable Verbose Logging

monitored_agent = monitor(
    agent,
    api_key="ab_xxx",
    verbose=True  # Print detailed compliance checks
)

Output:

[AgentBridge] Monitoring agent: LoanApprovalAgent
[AgentBridge] Session ID: sess_a3f9b21c
[AgentBridge] Checking compliance for approve_loan...
  Action: approve
  Amount: ₹45,000
  KYC: True
  Verdict: APPROVE
  Risk: LOW
[AgentBridge] ✓ Compliance passed (120ms)

Custom Session Tracking

monitored_agent = monitor(
    agent,
    api_key="ab_xxx",
    session_id="loan_batch_2024_04",  # Track related decisions
    agent_id="production_agent_v2"    # Identify agent version
)

Disable Strict Mode (Allow execution on errors)

monitored_agent = monitor(
    agent,
    api_key="ab_xxx",
    strict_mode=False  # Don't block on gateway errors
)

🎯 Use Cases

1. Loan Approval Agents

@monitor(api_key="ab_xxx")
class LoanAgent:
    def decide(self, application):
        # Your ML model
        score = self.model.predict(application)
        
        if score > 0.7:
            return self.approve(
                amount=application.amount,
                kyc_verified=application.kyc_done,
                reasoning=f"ML score: {score}, verified income"
            )

2. Transaction Monitoring

monitored_monitor = monitor(TransactionMonitor(), api_key="ab_xxx")

for txn in transactions:
    result = monitored_monitor.classify_transaction(
        amount=txn.amount,
        confidence=0.92,
        reasoning="Pattern matches known laundering typology"
    )
    
    if result['verdict'] == 'reject':
        # Automatically blocked by compliance rules
        flag_for_investigation(txn)

3. KYC Verification

kyc_agent = monitor(KYCAgent(), api_key="ab_xxx")

verification = kyc_agent.verify_customer(
    pan="ABCDE1234F",
    aadhaar_verified=True,
    reasoning="Documents verified via DigiLocker"
)

🔐 Security & Privacy

• End-to-end encryption for all gateway communication
• Zero data retention option available
• On-premise deployment for regulated entities
• Role-based access control for audit logs

📈 Dashboard & Reporting

Access your compliance dashboard at https://agentbridge.in/dashboard

• Real-time decision monitoring
• Behavioral drift alerts
• Structuring pattern reports
• AI-generated compliance summaries
• Downloadable audit trails (PDF/CSV)

🛠️ API Reference

monitor(agent, api_key, **options)

Wrap an agent with compliance monitoring.

Parameters:

• agent (Any): Your AI agent instance
• api_key (str): AgentBridge API key
• base_url (str): Gateway URL (default: production)
• session_id (str): Optional session identifier
• agent_id (str): Optional agent identifier
• enable_behavioral_analysis (bool): Enable drift detection (default: True)
• enable_structuring_detection (bool): Enable pattern detection (default: True)
• strict_mode (bool): Raise errors on violations (default: True)
• verbose (bool): Print detailed logs (default: False)

Returns: Wrapped agent with compliance enforcement

Raises:

• ComplianceError: When decision violates compliance rules
• AuthenticationError: Invalid API key
• AgentBridgeError: Gateway communication errors

🧪 Testing

# Install dev dependencies
pip install agentbridge[dev]

# Run tests
pytest tests/

# Type checking
mypy agentbridge/

📝 License

MIT License - see LICENSE file

🤝 Support

• Email: support@nova.team
• Docs: https://docs.agentbridge.in
• Issues: https://github.com/nova-tech/agentbridge/issues

🎉 About

Built by NOVA • Designed for Indian fintech compliance

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Get your API key: https://agentbridge.in/signup
Read the docs: https://docs.agentbridge.in