Think Different

                                                                                 gemini ai Source diagram: Multi-agent GenAI chat platform for a bank — Coordinator Agent orchestrating Accounts / Transaction / Service agents over MCP servers, fronted by an edge/API layer, with observability, cost tracking, PII redaction, agent evaluation, and a session store. This document maps every box in the diagram to concrete, current (2026) Azure and AWS resources, plus the open-source/framework layer (LangGraph, MCP, evaluation, Neo4j) you'd wire underneath either cloud. Where a component is genuinely cloud-native (e.g., the bank's own IdP, Azure/AWS aren't interchangeable at the same layer), that's called out. 1. Layer-by-Layer Component Map # Diagram comp...

                                                                                     generated by meta ai If a bank refuses to send raw or sensitive data outside its perimeter, you can build an agentic AI application using OpenAI, Gemini, or Claude APIs by adopting architectural patterns that keep raw data local while using public models strictly for high-level reasoning and orchestrating tasks . The primary strategies to achieve this include: 1. Local Anonymization & Data Masking (PII Redaction) Before sending any prompt to the API, pass the internal data through a local processing layer that strips out or replaces sensitive information (PII, account numbers, names, financial balances) with synthetic placeholders. How it works: A l...