agentic-engineering

Name: agentic-engineering
Availability: InStock
Author: zescode

Standardize AI agent engineering workflows with eval-first execution and cost-aware model routing.

Updated Jul 8, 2026

Search Tags:#devops #evaluation #decomposition #engineering #agentic #model-routing

Ready to run

npx skills add https://github.com/ZESCODE/Zes-Orchestration-System --skill agentic-engineering-zescode

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agentic-engineering
Source: https://github.com/ZESCODE/Zes-Orchestration-System/tree/main/.agents/skills/agentic-engineering
Command: npx skills add https://github.com/ZESCODE/Zes-Orchestration-System --skill agentic-engineering-zescode

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the inefficiency and high cost of unmanaged AI-driven development by enforcing structured decomposition, rigorous evaluation, and intelligent model routing.

Core Features & Use Cases

Eval-First Execution: Ensures code quality by defining and running capability and regression tests before and after implementation.
Task Decomposition: Breaks complex engineering requirements into verifiable 15-minute units to minimize risk and improve focus.
Cost-Aware Routing: Dynamically selects between Haiku, Sonnet, and Opus models based on task complexity to balance performance and token expenditure.

Quick Start

Use the agentic-engineering skill to decompose the current feature request into verifiable units and establish the baseline evaluation criteria.

Frequently Asked Questions about agentic-engineering

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

FAQPage Schema

How do I manage AI agent workflows for complex software development lifecycles?▼

Manage AI agent workflows by enforcing structured task decomposition, rigorous eval-first execution, and cost-aware model routing. This standardizes engineering workflows to ensure high-quality code generation and reliable output for complex lifecycles.

How does cost-aware model routing balance performance and token expenditure?▼

Cost-aware model routing dynamically selects between Haiku, Sonnet, and Opus models based on task complexity. This balances performance and token expenditure by matching the AI model tier to the specific requirements of the engineering workflow.

How do I ensure code quality when using AI agents for engineering tasks?▼

Ensure code quality through eval-first execution by defining and running capability and regression tests before and after implementation. This rigorous evaluation protocol guarantees reliable output and adherence to defined model tiers.

What is the best way to control risk in AI-driven development?▼

Control risk in AI-driven development by applying systematic regression testing protocols and breaking requirements into 15-minute verifiable units. This structured approach minimizes unmanaged inefficiencies and ensures high-quality code generation.