Optimizing AI Agent Investments and Performance on Azure

Deploying AI agents at scale requires more than just building them; you must also manage their performance, costs, and governance. This text-based guide helps you navigate the essential strategies for monitoring and optimizing your AI agent investments. You will transition from simply running AI prototypes to managing production-grade agentic workflows. Through clear, written explanations and practical step-by-step scenarios, you will learn how to leverage Azure observability tools to track agent behavior, control cloud spend, and ensure enterprise compliance. What you'll learn: - Understand the foundational concepts of AI agents, LLM orchestration, and cloud resource allocation. - Configure enterprise-grade observability and tracking for goal-driven agents on Azure. - Monitor agent performance, latency, and token consumption to optimize operational costs. - Implement governance frameworks, security guardrails, and responsible AI practices. - Evaluate agent decision-making processes and system outputs using modern evaluation metrics. The course starts with basic terminology and foundational concepts of AI agents and cloud infrastructure before moving into practical monitoring setups, cost management strategies, and governance workflows. This course is designed for IT professionals, cloud administrators, and technology managers who are new to AI operations on Azure, with no prior experience with AI agent deployment required. Start reading today to build efficient, transparent, and high-performing AI agent deployments.