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

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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 decisions about the model, the offer it runs on, what gets reused, and what the model is told. When those decisions are right, the saving repeats on every turn. That is why agent optimization starts here.

Learn how Microsoft Foundry can help you save time and money

The most expensive habit in production AI

Most AI applications are built the same way. In the prototype, you pick the strongest model available, put everything the model might need into the prompt, and confirm the idea works. That is the correct instinct for a prototype; the problem is what happens next. The prototype’s defaults quietly become the production architecture, and a pattern designed to answer “can this work?” becomes responsible for answering “can this scale …

Vollständiger Originalartikel ↗

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• Originalquelle / Hersteller-Advisory lesen: https://azure.microsoft.com/en-us/blog/the-economics-of-agent-optimization-four-ways-to-lower-the-cost/
• Verfügbaren Patch oder Workaround einspielen und dokumentieren.