Think Different

                                            generated by microsoft copilot I would make the Azure version slightly more advanced than the AWS/GCP versions because Azure now has a particularly strong enterprise story around Microsoft Foundry + Agent 365 + Entra + Defender + Purview. I also want to update two terms in your original architecture for interview accuracy: Use “Microsoft Foundry” / “Microsoft Foundry Agent Service” rather than treating the older Azure AI Foundry naming as the whole platform. “Agent 365” is now a real enterprise control plane for discovering, governing, securing and managing agents across Microsoft and third-party ecosystems. It became generally available for the commercial segment on May 1, 2026. ( Microsoft Learn ) Azure API Management now has dedicated AI Gateway capabilities, including token quotas, semantic caching, model routing/governance and MCP...

                                                generated by gemini I would build the GCP version around the same financial-services use case as the AWS architecture, but use the current 2026 Google Cloud stack: Gemini Enterprise Agent Platform, ADK, Agent Runtime, Model Garden, Model Armor, GKE, BigQuery Vector Search, Cloud Storage, VPC Service Controls, IAM, Cloud KMS, Cloud Logging/Monitoring, and MCP/A2A where appropriate. Google's current Agent Platform has evolved from the older Vertex AI-centric terminology and now provides model access, agent development, evaluation, deployment, orchestration and governance. ADK can run agents on Agent Runtime, Cloud Run or GKE. ( Google Cloud ) GCP Enterprise GenAI + Agentic AI Architecture 1. The business problem Let's use the same banking scenario: "Investigate why counterparty ABC breached its exposure limit yesterday and ...

                                                       generated by chatgpt The best way to understand this architecture is to build one realistic financial-services application twice: RAG application — "Risk Policy Assistant" Agentic AI application — "Risk Investigation Agent" Guardrails can detect/filter certain unsafe or policy-violating content and can help with grounding-related controls, but hallucination prevention fundamentally comes from architecture: RAG grounding, tool constraints, evaluation, citations, and validation. 1. Real-world example Imagine a bank has thousands of documents: Risk Policies Credit Policies Liquidity Policies Market Risk Policies Counterparty Policies Basel Documents Regulatory Documents Internal Procedures Trade Processing Manuals A user asks: "What is the bank's policy for counterparty exposure when the credit rat...