POC-Agentic AI Solution

🧠 Use Case Overview: Agentic AI for Mutual Fund Platform

Objective: Enhance decision-making in a Mutual Fund platform with GenAI Agents for:

• Investor onboarding & profiling

• Portfolio recommendation

• KYC & compliance checks

• Dispute management

• Fraud detection

• Investment advisory

While ensuring guardrails (for safety) and explainability (XAI) for trust, regulation, and compliance.

✅ Step-by-Step Solution Blueprint

1. Define Roles of Autonomous AI Agents (Agentic AI)

Agent	Responsibilities
Orchestrator Agent	Coordinates multi-agent collaboration, tracks context and decision lineage
KYC Compliance Agent	Performs document parsing, face match, PAN/Aadhaar validation
Risk & Suitability Agent	Evaluates risk profile using survey + behavior
Portfolio Advisor Agent	Recommends funds based on goals, suitability, market trends
Fraud Detection Agent	Analyzes anomalies in transactions using ML
Dispute Resolution Agent	Auto-summarizes issues and proposes resolution with escalation triggers

2. Architecture: Agentic AI with Guardrails + Explainability

🧩 High-Level Flow:

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User ➜ Gateway API ➜ Orchestrator Agent ├─ KYC Agent ➜ Azure OCR / Face API ├─ Risk Agent ➜ Azure ML (Rules + Models) ├─ Advisory Agent ➜ LLM + Fund Data ├─ Fraud Agent ➜ Kafka ➜ Azure ML Model ➜ SHAP └─ Guardrails AI + SHAP ➜ Validate + Explain ⬑ Orchestrator logs decision path

3. Key Technologies

Component	Tech Stack
LLM Coordination	LangGraph + LangChain
Spring Microservices	Spring Boot + Spring Cloud Gateway
Guardrails	Guardrails AI (GuardrailsHub / Python)
Explainability	SHAP, LIME, Captum (PyTorch)
Model Serving	Azure ML, FastAPI / Flask
Deployment	Azure AKS, Azure Monitor, Log Analytics, App Gateway
Message Bus	Kafka for async events (KYC completed, fraud alerts)
Agent Infra	Vector DB (Weaviate / FAISS), Redis for session memory

4. Guardrails AI – Where and How Applied

Location	Purpose
🛡️ Input Guarding	Enforce input schema (e.g., no profanity, max token limits)
🧠 Output Guarding	Ensure generated advice complies with SEBI, no hallucinations
✅ Validation Checkpoints	All recommendations validated via regex, type, tone
🔐 Audit Logs	Orchestrator logs decisions + validation results for compliance

Framework Used: Guardrails AI (Python SDK)

5. Explainability (XAI) – Where and How Applied

Agent	Explainability Applied
Risk Agent	SHAP used to show top 5 features influencing suitability
Fraud Agent	SHAP force plots to justify why txn flagged as fraud
Advisory Agent	Textual explanation of fund recommendation logic (goal, risk, sector trend)
Dispute Agent	Extractive summaries using LLM + RAG citations

Tools Used:

• SHAP

• Azure Interpret ML

6. Spring Boot Microservices Integration

Microservice	Responsibilities
kyc-service	PAN OCR, Aadhaar match, face comparison (Azure Cognitive)
risk-profile-service	Stores user risk scores, interacts with ML
portfolio-service	Calls LLM (LangChain) with vector DB
fraud-detector-service	Kafka consumer ➜ model scoring ➜ SHAP explanation
explainability-service	Serves SHAP plots + textual reasons
guardrails-service	Python FastAPI wrapper for Guardrails validation
orchestrator-service	Coordinates all via LangGraph + HTTP APIs

7. DevOps and Azure Setup

Layer	Azure Services
Deployment	Azure AKS, Azure Container Registry
ML Models	Azure ML Endpoints
Logging & Monitoring	Azure Monitor, App Insights, Log Analytics
Security	Azure Key Vault, App Gateway + WAF, MS Entra ID (AAD)
Data Store	Azure SQL, CosmosDB, Blob Storage, Redis Cache

🧪 Optional POC / Live Demo Flow

• Run a mock model for fraud detection (FastAPI + SHAP)

• Deploy on Azure ML or locally

• Return SHAP force plot to frontend

• Call Guardrails AI wrapper before sending response to user

✅ Sample Output to User (Post-Guardrails + XAI)

“⚠️ This transaction appears unusual and is flagged for manual review. Top Factors: Amount exceeds 3x average (45%) Location mismatch from last 5 txns (33%) IP Address flagged (22%) Explanation Score Chart available here”

📌 Conclusion

With this architecture:

• ✅ LLMs are controlled and explainable

• ✅ LangGraph enables deterministic multi-agent orchestration

• ✅ Guardrails AI ensures safety and compliance

• ✅ SHAP/LIME explain critical decisions (risk, fraud, advice)

• ✅ Azure stack offers production-grade scalability and monitoring