agentic-engineering

Name: agentic-engineering
Availability: InStock
Author: officechbusinessservices-creator

Coordinate AI-driven engineering tasks with eval-first evaluation and tiered model routing.

Updated Feb 4, 2026

Search Tags:#decomposition #engineering #risk-management #agentic #ai-automation #model-routing #eval-first

Ready to run

npx skills add https://github.com/officechbusinessservices-creator/CompliCore --skill agentic-engineering-officechbusinessservices-creator

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables AI-driven engineering with agentic workflows that break work into verifiable units and verify outcomes using eval-first evaluation, ensuring safer and more controllable development.

Core Features & Use Cases

Task decomposition: split complex engineering work into independent, verifiable units.
Model routing: route tasks to Haiku, Sonnet, or Opus level models based on complexity.
Risk controls: enforce evaluation, regression checks, and guardrails during automation.
End-to-end automation: coordinate from concept to validated implementation in production pipelines.

Quick Start

Define the task, set evaluation criteria, and initiate the eval-first workflow.

Frequently Asked Questions about agentic-engineering

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

FAQPage Schema

What is eval-first task decomposition in agentic engineering workflows?▼

Eval-first task decomposition is an agentic engineering approach that splits complex software development into independent, verifiable units. It enforces explicit capability and regression evaluations upfront, ensuring safer and more controllable AI-driven code generation.

How do I route AI automation tasks to different complexity models?▼

You route AI automation tasks using a tiered model routing scheme. This cost-aware approach directs tasks to Haiku, Sonnet, or Opus level models based on complexity, balancing performance and cost tracking across multi-step development pipelines.

How do I manage risk when automating multi-step software development with AI?▼

To manage risk in AI-automated software development, apply guardrails that enforce explicit capability evaluation and regression checks. This eval-first workflow verifies outcomes for each decomposed task, maintaining control during end-to-end production pipeline execution.

Can I use this agentic workflow for automated code generation in production pipelines?▼

Yes, you can use this agentic workflow for automated code generation in production pipelines. It coordinates end-to-end from concept to validated implementation, applying session management and cost tracking to ensure safe, verified outcomes.

What's the best way to verify AI-generated architecture designs without losing control?▼

The best way to verify AI-generated architecture designs is using an eval-first evaluation process. By breaking work into verifiable units and enforcing regression checks, you maintain controllable development and validate outcomes before deployment.