Your agent answers "what's the weather in Madrid?" and three seconds later someone asks "how's Madrid looking weather-wise?" The whole loop runs again: planning, tool calls, generation. Prompt caching discounts the input and charges you for all of that. Two application level caches remove the work instead: a semantic cache that returns the stored answer for a paraphrase, and a reasoning cache that replays the known plan and tool path. Measured on a deployed system: 127 ms hit, 3,004 ms miss.

What you'll learn:
• A decision framework for which cache layer saves which cost (tokens, latency, API calls)
• Working open-source code for both caches, deployable on either backend
• Numbers from deployed system: 127 ms hit vs 3,004 ms miss; cold vs warm runs going from 24,561 tokens to 3,576 and fewer tool calls
• The honest failure story: the first iteration saved nothing until the hint prompt was fixed

Outline:
• The bill nobody itemizes
• Semantic response cache
• Reasoning cache
• Freshness, or how not to serve wrong answers
• Decision framework + resources