Prompt caching turns context order into runtime architecture.
Moving a timestamp, session id, memory write, or even tool schema to the front of an agent’s prompt can single-handedly destroy the cache economics of every long-running task. This still causes trouble even though the model does answer the prompt, the trace even looks normal, and it still works more or less as before. But the increased bill and first token latency can add up quickly, only becoming apparent later for expensive agents where the difference is barely noticeable at first.
The fresh work from LangChain named the long-buried architecture decision hidden in plain sight for everyone using Deep Agents. The numbers are blunt: properly implemented prompt caching, as opposed to a simple cache of last N tokens, can reduce inference token cost by 41 to 80%. The more interesting number is from Deep Agents eval trajectories in LangChain’s harness: when provider-aware caching is turned on, average token-cost reduction lands between 49 and 80%.
A prompt cache is a test for stable context.
The cache boundary lives inside the prompt
