I don't know - all of these cache hit rate advocacy is kind of goodhearting. Cache hit rates are less important than the accuracy of LLM answers and people seem to forget about it. For example I've seen articles how sub-agents poison the cache. Sure you can fill up your context with irrelevant data to have high cache hit rate - but that degrades the LLM work - sub-agents are about providing the LLM with a clean context - just the information needed to do its job, no distraction.
All of the main providers now offer cache management that can work with sub-agents - but it was kind of bolted on - and all of them have different apis - so nobody uses them anyway.
This article mainly argues around harnesses but a breakthrough for me was understanding why harnesses. Without a harness and only a raw LLM, exploring and modifying codebases is not feasible. It’s just text in, text out, and has no way of handling the context of the codebase (if large enough). Plug an LLM into a harness, and all of the sudden it has an exosuit that lets it funnel its thinking into discrete actions. It can now use strategies to manage that large context in pieces.
I don't know - all of these cache hit rate advocacy is kind of goodhearting. Cache hit rates are less important than the accuracy of LLM answers and people seem to forget about it. For example I've seen articles how sub-agents poison the cache. Sure you can fill up your context with irrelevant data to have high cache hit rate - but that degrades the LLM work - sub-agents are about providing the LLM with a clean context - just the information needed to do its job, no distraction.
All of the main providers now offer cache management that can work with sub-agents - but it was kind of bolted on - and all of them have different apis - so nobody uses them anyway.
This article mainly argues around harnesses but a breakthrough for me was understanding why harnesses. Without a harness and only a raw LLM, exploring and modifying codebases is not feasible. It’s just text in, text out, and has no way of handling the context of the codebase (if large enough). Plug an LLM into a harness, and all of the sudden it has an exosuit that lets it funnel its thinking into discrete actions. It can now use strategies to manage that large context in pieces.