Original Reddit post

I stumbled on a markdown pattern online that fixes a massive headache with agentic workflows, and wanted to share it here. Most people use vector DBs or markdown wikis to give agents knowledge (context). But if your agent actually acts, knowledge isn’t enough. It needs a record of judgment. The author calls them Decision Notes —basically lightweight ADRs (Architecture Decision Records) for LLMs. Instead of just: Context → Action it forces a judgment layer: Sources ↓ Wiki Notes ↓ Decision Notes ↓ Agent Actions The core idea Keep a decision-notes/ directory tracking: Past choices Supporting evidence Explicit “Revisit when” triggers Before the agent executes a tool, it checks these notes for alignment. If a new action conflicts with a past human-accepted decision, the agent flags it instead of blindly running the task. It seems like an elegant way to prevent system prompt bloat and stop agents from drifting over time. Has anyone built something similar to manage agent policies? Are you using markdown or a structured DB? submitted by /u/adi1405

Originally posted by u/adi1405 on r/ArtificialInteligence