{"slug": "ask-hn-ideas-for-dealing-with-overly-do-gooder-agents", "title": "Ask HN: Ideas for dealing with overly-do-gooder agents", "summary": "A Hacker News user reports that their team is frustrated with LLMs defaulting to overly cautious, compliance-officer-like responses, attributing it to strict post-training. They have tried internal agents.md files and pre-prompt instructions with partial success and are seeking community advice on workarounds.", "body_md": "| ||||||||||||\n1 point by |\nHi HN- Our team has been recently getting fed up with every single LLM that we use defaulting to sounding like a corporate compliance officer or goody-two-shoes (‘erm, but xyz’ etc) on even simple things. We’ve come to the conclusion that the post training being done is so strict that models default to acting like Gomer Pyle. We’ve made some internal agents.md files and pre-prompt instructions to try to kick them out of the pattern — and it works sometimes — but how are all of you dealing with this or working around it? | |||||||||||\n|", "url": "https://wpnews.pro/news/ask-hn-ideas-for-dealing-with-overly-do-gooder-agents", "canonical_source": "https://news.ycombinator.com/item?id=49466147", "published_at": "2026-08-27 15:10:43+00:00", "updated_at": "2026-08-27 15:19:21.412125+00:00", "lang": "en", "topics": ["large-language-models", "ai-ethics", "ai-products"], "entities": ["Hacker News"], "alternates": {"html": "https://wpnews.pro/news/ask-hn-ideas-for-dealing-with-overly-do-gooder-agents", "markdown": "https://wpnews.pro/news/ask-hn-ideas-for-dealing-with-overly-do-gooder-agents.md", "text": "https://wpnews.pro/news/ask-hn-ideas-for-dealing-with-overly-do-gooder-agents.txt", "jsonld": "https://wpnews.pro/news/ask-hn-ideas-for-dealing-with-overly-do-gooder-agents.jsonld"}}