The Economics of Agent Optimization: Four ways to lower the cost

This blog post is the second of a four-part series called The Economics of Agent Optimization which shares the strategies, capabilities, and proof points to help you optimize agent costs and run AI as a managed investment system on Microsoft Foundry.The first post set out the three decisions that system rests on: optimize each request at runtime, optimize each workflow over time, and govern spend continuously. This post takes the first, the one that touches every dollar you will ever spend on AI.

An agent is a loop around a model. It plans, calls a tool, reads the result, and reasons again, so a single completed outcome can take a dozen model requests. That is why the number the business cares about is the cost of a successful outcome, not the price of a token.

Every turn in that loop is still one model request, and each request carries...

Copyright of this story solely belongs to microsoft.com. To see the full text click HERE