The original title is "Agent Prompt Caches Are a Runtime Boundary | Focused Labs"

Short summary

Prompt caching cuts AI agent costs 41–80%, but only when stable context (system prompts, tools, examples) precedes dynamic content—it's a runtime boundary, not just a setting. OpenAI uses prefix matching (1,024+ tokens); Anthropic explicit cache breakpoints. Tool schemas, memory placement, and prompt order matter architecturally; LangChain's Deep Agents documents the pattern.

• •Prompt caching reduces agent token costs by 41–80% through provider-specific prefix strategies
• •Stable context must precede dynamic content; cache hits require exact text matching in the same order
• •Cache policy is a runtime architectural boundary: tool schemas, memory, and prompt order must be managed intentionally

Generated with AI, which can make mistakes.