RPI Strategy for Agentic Engineering

A framework for delivering software at scale using AI coding agents through a disciplined Research > Plan > Implement approach.

Overview

This repository documents the RPI Strategy methodology for engineering teams working with AI coding agents. The three-phase approach provides a repeatable discipline for successfully delivering software projects while leveraging the capabilities of modern AI development tools.

Research > Plan > Implement forms the core framework for:

• Understanding requirements and constraints
• Architecting solutions with AI assistance
• Implementing and deploying with confidence

What's Included

• Documentation of the RPI Strategy methodology
• Example workflows for popular AI coding tools:
  • Claude Code: "Analyze the authentication bug in user/session.py:245. Use Research phase: identify problem scope, validate with FAR scale ≥4.0, then plan atomic fixes."
  • GitHub Copilot: Use structured comments like // RESEARCH: User login fails intermittently - need factual evidence from logs before requesting code suggestions
  • Cursor: Apply FACTS scale validation to generated task breakdowns: "Validate this implementation plan using FACTS scale - is each task <4hrs and independently testable?"

Documentation Structure

Core Framework

• RPI Strategy Phases - Complete guide to Research → Plan → Implement
  • Research Phase - Build context & insight
  • Plan Phase - Decide what to do & how
  • Implement Phase - Ship & learn

Quality Framework

• Validation Scales - Improve GenAI and human results using structured validation
  • FAR Scale - Factual, Actionable, Relevant scoring
  • FACTS Scale - For comprehensive design validation

Quick Start (5 minutes)

Try this now with your next AI coding task:

1. Research: Before asking for code, prompt: "Help me understand the problem scope first. What's the specific issue, where in the codebase, and what evidence supports this?"

2. Validate: Score your findings using FAR scale: Factual ≥4, Actionable ≥3, Relevant ≥3

3. Plan: Request: "Break this into atomic tasks (single command calls, file edits, etc.). Validate each task is testable independently."

4. Validate: Check tasks against FACTS scale: Mean ≥3.0 across all dimensions

5. Implement: Execute one task, measure results, iterate

Example: Instead of "Fix the login bug", try: "Research: User login fails on mobile Chrome. Evidence: 3 support tickets, error 'session undefined' in console logs. Validate this against FAR scale before planning solution."

Getting Started

1. Start with the RPI Strategy Phases overview to understand the Research → Plan → Implement framework
2. Apply the Validation Scales to improve the quality of your AI interactions and outputs
3. Explore phase-specific documentation and example prompts for your preferred AI coding environment

*Inspiration to work on this came from watching this video.