This is the way. Making your own agent to have a sticky memory context that is prepended to every execution is necessary to ensure each task is bounded by those precepts.

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Muromec 3 hours ago | parent | next [-]

The trick I'm doing -- the model is given a tool that runs a prompt in the current thread to consolidate it's working memory and identity (it has a memory tool bound to the agent persona). When the prompt ends, the parts of memory that are marked as identity are merged together into a new system prompt, then the context restarts with only system prompt and this tool call surviving. Then it just keeps going.

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drob518 2 hours ago | parent | prev [-]

Doesn’t that blow your token cache hit rate and balloon your costs (essentially everything is billed at the input token rate, not the cached rate)?

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haus20xx an hour ago | parent [-]

If it moved a block like

<system_prompt>

<tools_etc>

<project_prompts>

<user_call>

<llm_response>

<system_prompt>

<tools_etc>

//Removed project_prompts

<user_call>

<llm_response>

<user_call>

<project_prompts> //reinserted

<llm_response>

Cache would break but if you did instead

<system_prompt>

<tools_etc>

<project_prompts>

<user_call>

<llm_response>

<user_call>

<project_prompts> //Duplicated with new user messsage

<llm_response>

It wouldn't bust cache, it would just make your input prompts slightly larger. Technically inefficient as you're duplicating the same rules over and over but I imagine for a smallish checklist/principles that it is tremendously more efficient than a cache break every message

	▲	cyanydeez 12 minutes ago \| parent [-]
		so first, we treat context like a stack. Things are popped onto the stack; if we try to remove anything but the top of the stack, we'll break the cache. So a good harness should be popping tool prompts, user hints after every return from the top so the context of tools and user hints never repeats and are allways prefixed to the current user prompt. I've never inspected any harness, but opencode with the dynamic context compaction plugin appears to do this well.