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 postset 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...

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