agentic-development

Build autonomous AI agents with structured architectures and tool orchestration.

1|Updated Jan 10, 2026

Search Tags:#workflow #planning #testing #ai #tooling #agent #memory

Ready to run

npx skills add https://github.com/artofrawr/claude-control --skill agentic-development-artofrawr

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Builds and coordinates autonomous AI agents by providing a structured architecture, multi-model integration, and reliable planning, action, and verification loops.

Core Features & Use Cases

Guided agent patterns for Python (Pydantic AI) and Node.js (Claude SDK) environments.
Explore-Plan-Execute-Verify workflow with memory, guardrails, and tests.
Templates and examples for tool definitions, memory, and evaluation.

Quick Start

Initialize an autonomous agent blueprint in your project using the provided templates and examples.

Frequently Asked Questions about agentic-development

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

FAQPage Schema

How do I build autonomous AI agents with structured workflows?▼

Build autonomous AI agents using an explore-plan-execute-verify workflow with strongly-typed models, memory management, guardrails, and tool orchestration. Templates and examples guide multi-step planning across Python and Node.js codebases.

What is the explore-plan-execute-verify pattern for AI agent development?▼

The explore-plan-execute-verify pattern is a structured agent architecture coordinating multi-step planning, tool action, and verification loops. It integrates memory and guardrails to ensure reliable autonomous execution.

Can I use strongly-typed models for AI agent orchestration in Python and Node.js?▼

Yes, guided agent patterns support Python (Pydantic AI) and Node.js (Claude SDK) environments. Both receive structured architectures for strongly-typed tool definitions and multi-step model integration.

How do I add memory and guardrails to an autonomous AI agent?▼

Add memory and guardrails to autonomous AI agents using provided templates for tool definitions and evaluation. These components enforce constraints and maintain context throughout the multi-step execution loop.

What's the best way to structure multi-step planning for AI agents across a codebase?▼

Structure multi-step planning with a strongly-typed agent architecture that supports tool orchestration and memory management. Templates initialize a blueprint to coordinate planning and verification loops across codebases.

How do I test and evaluate AI agent workflows before deployment?▼

Test and evaluate AI agent workflows using provided templates for evaluation and guardrails. The structured architecture supports testing within the verify phase to ensure reliable autonomous execution.