From bd7c9e8002f8cd75781ab7d8df531f6289c713ba Mon Sep 17 00:00:00 2001 From: angiejones Date: Tue, 6 Jan 2026 16:46:39 -0600 Subject: [PATCH] docs: credit HumanLayer in RPI tutorial --- documentation/docs/tutorials/rpi.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/documentation/docs/tutorials/rpi.md b/documentation/docs/tutorials/rpi.md index 25d17b9372ab..f6004fa680be 100644 --- a/documentation/docs/tutorials/rpi.md +++ b/documentation/docs/tutorials/rpi.md @@ -10,7 +10,7 @@ import planDoc from '../../static/files/thoughts/plans/2025-12-23-remove-tool-se Most people use AI agents by jumping straight to execution: "refactor this code", "remove this feature", "add this new feature". While sometimes this works well, especially for smaller changes or codebases, it often falls apart on complex changes. -**RPI (Research, Plan, Implement)** is a mental model that proposes a different way of working with AI agents. This approach trades speed for clarity, predictability, and correctness. +**[RPI (Research, Plan, Implement)](https://github.com/humanlayer/advanced-context-engineering-for-coding-agents/blob/main/ace-fca.md)**, introduced by HumanLayer, proposes a different way of working with AI agents. This approach trades speed for clarity, predictability, and correctness. This tutorial walks through how RPI works via a real demonstration. By the end, you should be able to run this same workflow on your own codebase.