Maximize the Cost Efficiency of AI Agents on Azure
At a glance
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Maximize the ROI of AI agent investments by making cost-conscious, informed decisions at every stage. This learning path offers actionable guidance, from identifying high-impact use cases and understanding cost drivers, to forecasting ROI, adopting best practices, designing scalable and effective architectures, and optimizing ongoing investments.
Whether you're just beginning your AI journey or scaling enterprise adoption, you find practical strategies to build, deploy, and manage AI agents on Azure with confidence. Align innovation with financial discipline to ensure your AI initiatives deliver sustainable value and long-term success.
Prerequisites
Before starting this module, you should have a basic understanding of AI and large language models (LLMs) and be familiar with cloud platforms and software lifecycle concepts.
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Modules in this learning path
Learn how to uncover and evaluate AI agent use cases that deliver measurable business impact while being optimized for minimal costs.
You can build agentic AI agents using Microsoft Foundry and pretrained models. In this scenario, the infrastructure costs are all included with no requirement to consider costs such as compute and networking. This module explores more complex scenarios where you're considering the cost drivers for custom AI agents, particularly if they use AI models.
This module introduces you to practical frameworks for quantifying and communicating the ROI of AI agents, even if you don't have a background in finance. Business leaders learn how to evaluate both quantified financial impact and strategic/intangible value, and how to apply these insights to prioritize use cases, build compelling business cases, and guide investment decisions.
Learn how to implement best practices to empower AI agent efficiency and ensure long-term success by leveraging frameworks like AI Center of Excellence, FinOps, GenAI Ops, Cloud Adoption Framework, and Well-Architected Framework.
In this module, you'll evaluate when to develop custom AI agents tailored to your needs and when to deploy prebuilt solutions from trusted platforms. Learn how to assess trade-offs in time-to-value, complexity, customization, and operational cost. This module also guides you in selecting the right models for your AI agents, from lightweight task-specific agents to advanced multi-modal agents.
This module guides learners through designing AI agent architectures that scale with business demand, provide deep visibility into cost drivers, and support long-term governance. Participants explore reference architectures, orchestration patterns, and financial design principles that ensure operational efficiency, cost control, and strategic alignment.
Managing and optimizing AI agent investments on Azure is now more streamlined and powerful with the built-in observability capabilities in Microsoft Foundry. These tools provide enterprise-grade observability, governance, and performance optimization capabilities essential for deploying intelligent, goal-driven agents at scale. These tools provide enterprise-grade observability, governance, and performance optimization capabilities essential for deploying intelligent, goal-driven agents at scale.