Understand the key cost drivers of AI agents: Review

The 'Team Expertise Resource Allocation' unit came and went. It was a list of roles—data scientists, ML engineers, domain experts—and a note that their salaries are a cost. Yes, they are. What I was hoping for was some sort of framework or benchmark for estimating how many of each, or at what project phase their cost impact spikes. This module is good at listing the line items for a Total Cost of Ownership (TCO) model but offers almost no guidance on how to populate the cells.

The content is well-structured, breaking down the budget into infrastructure, development, data prep, and ongoing maintenance. For someone building their first project budget spreadsheet, it’s a useful taxonomy. The distinction between one-time setup costs and recurring operational costs is clear. And the content feels current; it correctly positions services like Azure OpenAI and Microsoft Foundry within this cost structure as of early 2026.

However, it's all text. There are no interactive elements, no sample TCO calculators, no 'what-if' scenarios. It’s essentially a well-organized whitepaper. The knowledge check at the end just confirms you can recall the category names. It’s useful as a pre-meeting read to ensure everyone on a new AI agent project is speaking the same language about budget items, but it won’t teach you how to actually forecast a single dollar.