Microsoft AI Foundry Deep Dive | Day 4 Evaluation Framework

Title Microsoft AI Foundry Evaluation Framework | Measure AI Agent Quality Description In this episode of the Microsoft AI Foundry Deep Dive series, we learn how to evaluate AI Agents before deploying them to production. Building an AI Agent is only the first step. Enterprise AI teams must measure answer quality, groundedness, relevance, coherence, safety, and overall reliability. Microsoft AI Foundry provides a powerful Evaluation Framework that helps organizations benchmark, validate, and improve AI solutions using synthetic datasets, evaluation runs, and built-in evaluators. In this episode, we evaluate our HR Leave Assistant built in previous episodes and measure its performance using Foundry's built-in evaluation capabilities. Topics Covered: ✅ Agent Evaluation vs Model Evaluation ✅ Synthetic Dataset Generation ✅ Individual Turns vs Full Conversations ✅ Groundedness Evaluation ✅ Relevance Evaluation ✅ Coherence Evaluation ✅ Fluency Evaluation ✅ Safety Evaluation ✅ Evaluation Metrics & Scorecards ✅ Evaluation Cost & Token Usage ✅ Enterprise AI Quality Benchmarks Demo: • Generate synthetic HR policy questions • Run evaluation against HR Leave Assistant • Analyze Groundedness, Relevance, Coherence, and Fluency • Review token consumption and evaluation costs • Understand enterprise AI quality standards By the end of this video, you'll understand how to measure AI quality, interpret evaluation results, and improve your AI Agents using Microsoft AI Foundry. Next Episode: Responsible AI & Guardrails in Microsoft AI Foundry #MicrosoftAIFoundry #AIEvaluation #AgentEvaluation #AzureAI #AzureOpenAI #GenerativeAI #EnterpriseAI #RAG #FoundryIQ #AIEngineering #AgenticAI #AIFoundry #PromptEngineering #ResponsibleAI #CloudComputing