Prompt Caching for Long-Running Agents: A Claude Code Case Study
Source
Avi Chawla
Author
Avi Chawla
Date
Why it matters
A concrete guide to the prompt structure and cache discipline that can cut the cost and latency of long-running agents without changing their behavior.
Avi Chawla breaks down how prompt caching works for long-running AI agents, using Claude Code as a real-world example.
The piece explains static vs dynamic context, prefill and decode stages, KV cache mechanics, prefix hashing, and cache breakpoints, showing how disciplined context engineering can achieve a 92 percent cache hit rate and meaningfully cut inference costs.
Transcript
Avi Chawla explains prompt caching for long-running agents using Claude Code as a case study. The piece covers static versus dynamic context, prefill and decode, KV cache mechanics, prefix hashing, cache breakpoints, and the engineering discipline behind a 92 percent hit rate and lower costs.
