The Economics of Agent Optimization: Context engineering for enterprise AI agents

This blog post is the third 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 systems rest on. The second post took the request at runtime. This post takes the next one: making each agent cheaper over time as it learns what works.

Every agent has a mechanism that determines what its model sees on each turn. In many production systems, that choice was set during prototyping and never revisited, even though it often drives the largest share of operating cost and contributes to disappointing answers.

This is also the part of an agent that can improve on its own. The model remains as capable as when you selected it, and instructions change only when someone rewrites them. But what an agent knows, can access, and remembers, grows as it runs—making it the key to improving performance while lowering cost over time. Managing that process is called context engineering.

Why the context window sets what an agent costs

A model has no memory of its own. On each turn, its context window supplies everything it can use: instructions, available tools, retrieved documents, and conversation history. When the turn ends, that context disappears and must be sent again on the next one.

That cost is manageable for a chatbot answering one question. For an agent working across many turns toward one outcome, it is often the largest expense. Because the context window is paid for every turn, unnecessary content is billed repeatedly.

The less visible cost is quality. More context does not guarantee better answers: a relevant fact buried in 40 pages is harder to use, and a long tool list makes the wrong choice more likely. Each mistake adds more turns—and more cost—to recover.