agentic-engineering

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

1|Updated Mar 8, 2026

Search Tags:#decomposition #engineering #agentic #model-routing #regression-evals #risk-controls #eval-first

Ready to run

npx skills add https://github.com/vinitgirdhar/GRID_ --skill agentic-engineering-vinitgirdhar

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Coordinates AI-driven engineering tasks using eval-first execution, decomposition, and cost-aware model routing to improve quality, speed, and governance in complex projects.

Core Features & Use Cases

Eval-first loop: defines capability evaluation and regression checks before implementation.
Task decomposition: breaks work into independently verifiable agent-sized units.
Model routing: routes tasks to Haiku, Sonnet, or Opus tiers based on task complexity.
Cost discipline: tracks model usage, retries, and wall-clock time to manage spending and risk.
Session strategy: supports continuing sessions for closely-coupled units and fresh starts after milestones.
Review focus for AI-generated code: emphasizes invariants, edge cases, security, and risk controls.

Quick Start

Define a concrete engineering task and let the agentic system decompose, evaluate, and route it for implementation.

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 costs and quality when automating complex software engineering tasks?▼

Manage AI agent costs and quality by using eval-first execution, task decomposition, and cost-aware model routing. This approach routes work to different model tiers based on complexity, enforcing regression evals and human risk controls before implementation.

What is eval-first development for AI-generated code and how does it work?▼

Eval-first development for AI-generated code defines capability evaluation and regression checks before implementation begins. This mechanism ensures invariants, edge cases, and security risk controls are verified against unit decomposition prior to agent execution.

How do I decompose large development workflows into independently verifiable agent-sized units?▼

Decompose large development workflows by breaking work into independently verifiable agent-sized units. This task decomposition strategy supports continuing sessions for closely-coupled tasks and fresh starts after milestones to maintain execution safety.

Can I route AI engineering tasks to different model tiers based on complexity?▼

You can route AI engineering tasks to different model tiers using cost-aware model routing. Tasks are distributed across Haiku, Sonnet, or Opus tiers based on complexity, tracking model usage, retries, and wall-clock time to manage spending and risk.

What is the best way to review AI-generated code for invariants and security risks?▼

The best way to review AI-generated code is to emphasize invariants, edge cases, security, and risk controls. Using an eval-first loop ensures regression checks are defined before implementation, maintaining quality and governance across complex projects.