AI BriefingPrompt Caching in Agents
Key point
Prompt Caching reduces the cost of agents, but it reacts very sensitively to configuration changes.
Details
Prompt Caching makes it possible to run AI agents more economically, but its structure has the drawback of being very fragile. Simply changing a Tool definition, swapping the model, or having the provider's routing decision change can turn a cheap incremental request into an expensive one that regenerates the entire context.
How the cache behaves directly affects not just cost but also the following factors:
- Latency
- Cost
- Tool Design
- Session Design
- Product Features (how product functionality is implemented)
This article takes an in-depth look at how caching works, along with the common causes that degrade cache performance.
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