Agentic AI in Practice: Building Governed AI Agents with Azure OpenAI & Strands (AML Use Case)

Description

Large Language Models are powerful—but real enterprise value comes from Agentic AI, not standalone prompts. In this video, I walk through a practical, production-grade Agentic AI architecture built using Azure OpenAI and the Strands agent framework, demonstrating how multiple specialized AI agents can be orchestrated safely, predictably, and auditable in a regulated environment. Rather than treating AI as a black box, this approach uses: A central orchestrator for control and governance Specialized agents with narrow responsibilities Deterministic scoring combined with AI reasoning Clear separation between data gathering, logic, and judgment The example use case shown is Anti-Money Laundering (AML) screening, but the architecture itself is domain-agnostic and can be applied to: Risk and compliance automation KYC and onboarding workflows Financial crime detection Enterprise decision support systems 🔍 What this video covers What Agentic AI actually means in practice How Strands enables controlled multi-agent orchestration Using Azure OpenAI safely within enterprise guardrails Why specialized agents outperform monolithic LLM solutions How to design AI systems that are explainable, auditable, and production-ready 🧠 Key takeaway Agentic AI is not about “more AI autonomy.” It is about better control, clearer responsibility, and safer outcomes. If you are a solution architect, AI engineer, compliance leader, or enterprise technologist, this video shows a practical blueprint you can adapt immediately. #AgenticAI #AIArchitecture #AzureOpenAI #StrandsAI #EnterpriseAI #AIGovernance #FinancialCrime #AML #ResponsibleAI #AIEngineering