agentic-engineering

Decompose engineering tasks into verifiable units with eval-first execution.

Updated Jul 27, 2026

Search Tags:#code quality #AI development #task decomposition #agentic engineering #model routing #eval-first

Ready to run

npx skills add https://github.com/kouiso/designdiff --skill agentic-engineering-kouiso

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agentic-engineering
Source: https://github.com/kouiso/designdiff/tree/main/.claude/skills/agentic-engineering
Command: npx skills add https://github.com/kouiso/designdiff --skill agentic-engineering-kouiso

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill streamlines complex engineering tasks by leveraging AI agents for implementation, guided by human oversight for quality and risk management.

Core Features & Use Cases

Eval-First Execution: Ensures quality by defining and running evaluations before and after implementation.
Task Decomposition: Breaks down large tasks into smaller, verifiable, and manageable units.
Cost-Aware Model Routing: Optimizes AI model usage based on task complexity, from simple edits to complex analysis.
Use Case: Automate the refactoring of a legacy codebase by decomposing the task, routing complex architectural decisions to a powerful model, and verifying changes with automated tests.

Quick Start

Use the agentic engineering skill to decompose the task of refactoring the user authentication module into agent-sized units.

Frequently Asked Questions about agentic-engineering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is eval-first methodology in AI-driven engineering workflows?▼

Eval-first methodology ensures code quality by defining and running evaluations before and after AI agents perform engineering tasks. This approach guarantees verifiable task completion and structured, test-backed development throughout the software lifecycle.

How do I break down a legacy codebase refactoring task for an AI agent?▼

Task decomposition breaks large engineering objectives into smaller, verifiable, and manageable units. This allows AI agents to handle specific sections of a legacy codebase while routing complex architectural decisions to a powerful model.

How does cost-aware model routing optimize AI-assisted implementation?▼

Cost-aware model routing optimizes AI model usage based on task complexity. It routes simple code edits to efficient models while allocating powerful, higher-cost models for complex analysis and architectural decisions, balancing resource allocation.

Can I use agentic engineering to automate quality assurance for software development?▼

Yes, agentic engineering automates quality assurance by leveraging AI agents for implementation guided by human oversight. It satisfies requirements for structured AI-driven development and verifiable task completion through automated tests.

What is the best way to manage resource allocation when using AI agents for software engineering?▼

The best way to manage resource allocation is using cost-aware model routing. This method matches AI model tier selection to task complexity, ensuring efficient resource usage while maintaining required code quality and verifiable outputs.