Fernando Vasconcellos introduces a comprehensive Microsoft learning path for building, deploying, and managing cost-efficient AI agents on Azure, offering practical strategies for both technical and business leaders.

Maximize the Cost Efficiency of AI Agents on Azure

Author: Fernando Vasconcellos

Microsoft has launched a new, comprehensive learning path designed to help organizations deliver measurable business value with AI agents on Azure—while controlling costs and fostering innovation. These seven curated modules empower developers, engineers, solution architects, and business analysts with practical guidance to ensure every AI project is both effective and financially sustainable.

Learning Path Modules Overview

1. Identify and Prioritize High-Impact, Cost-Effective AI Agent Use Cases

  • Learn a structured framework for researching, evaluating, and prioritizing AI agent opportunities.
  • Collaborate across teams to define KPIs and focus on achievable “quick wins.”
  • Explore Module 1

2. Understand the Key Cost Drivers of AI Agents

  • Dive deep into the infrastructure, integration, data, expertise, and operational expenses that shape Azure AI agent costs.
  • Discover approaches for resource right-sizing, efficient data preparation, and leveraging Microsoft tooling.
  • Explore Module 2

3. Forecast the Return on Investment (ROI) of AI Agents

  • Get step-by-step instructions and real examples for forecasting ROI, using techniques such as NPV calculation and sensitivity analysis.
  • Learn practical communication strategies for technical and business audiences.
  • Explore Module 3

4. Implement Best Practices: AI Center of Excellence and Beyond

  • Master organizational frameworks like AI Center of Excellence (CoE), FinOps, GenAI Ops, the Cloud Adoption Framework (CAF), and the Well-Architected Framework (WAF).
  • Adopt best practices to accelerate AI agent adoption, resource optimization, governance, and operational excellence.
  • Explore Module 4

5. Maximize Cost Efficiency: Choosing the Right Development Approach

  • Compare SaaS, PaaS, and IaaS for AI agent development, factoring in customization, cost, and scaling requirements.
  • Highlighted tools include Microsoft Copilot Studio, Visual Studio, and Azure AI Foundry.
  • Explore Module 5

6. Architect Scalable and Cost-Efficient AI Agent Solutions

  • Leverage Azure Landing Zones and reference architectures for secure, optimized deployments.
  • Explore strategies for model selection, governance, tagging, and flexible scaling for both single- and multi-agent systems.
  • Explore Module 6

7. Manage and Optimize AI Agent Investments on Azure

  • Monitor and optimize costs with Azure AI Foundry Observability, Azure Monitor Application Insights, and Microsoft Cost Management.
  • Learn to set budgets, track metrics, and enable ongoing improvement.
  • Explore Module 7

Start Learning Now

Ready to make your organization’s AI initiatives both innovative and cost-effective?

This learning path provides actionable frameworks, technical and strategic guidance, and practical resources to ensure AI investments on Azure are aligned with both business impact and financial discipline.

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