I typically tell my agents to only treat document writing as a last "rendering" pass. LLMs are so good at taking sparse knowledge and compiling it, that I prefer to store knowledge as composable ideas/facts.

What has worked well in practice is giving the agent a directory, and tell it to make independent markdown files for facts/findings it locates - with each file having front-matter for easy search-ability.

This de-complects most tasks from "research AND store iteratively in a final document format" to more cohesive tasks "research a set of facts and findings which may be helpful for a document", and "assemble the document".

Only a partial mitigation, but find it leads to more versatile re-use of findings, same as if a human was working.