agentic-ai-dev

Your custom AI agent builder for scalable, secure, and observable workflows.

34|21|Updated Feb 1, 2026
One-click install
npx skills add https://github.com/kumaran-is/claude-code-onboarding --skill agentic-ai-dev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentic-ai-dev
Source: https://github.com/kumaran-is/claude-code-onboarding/tree/main/.claude/skills/agentic-ai-dev
Command: npx skills add https://github.com/kumaran-is/claude-code-onboarding --skill agentic-ai-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill provides production-ready patterns and templates for building AI agents using Python 3.13, LangChain, LangGraph, and FastAPI to accelerate robust, scalable agent development.

Core Features & Use Cases

  • Reusable agent patterns for single-agent and multi-agent setups, including RAG workflows, memory strategies, tool integrations, and HITL-ready testing.
  • Production-ready scaffolds with typed state management, guardrails, checkpointing, and observability hooks for LangSmith and Prometheus.
  • Use cases span agent development, RAG-powered retrieval, graph workflows, tool orchestration, and automated testing pipelines.

Quick Start

Instantiate a simple ReAct-style agent using the provided provider factory and then scale to multi-agent pipelines.

Frequently Asked Questions about agentic-ai-dev

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

FAQPage Schema
How do I build production-grade AI agents with LangGraph and FastAPI?▼

Production-grade AI agents with LangGraph and FastAPI require ready-to-use patterns that enforce typed state management, guardrails, checkpointing, and observability hooks for robust single-agent or multi-agent setups.

What patterns are needed for multi-agent setups and RAG workflows?▼

Multi-agent setups and RAG workflows require reusable patterns for memory strategies, tool integration, and graph orchestration. These templates ensure your agents can handle complex retrieval and coordinate tasks effectively.

Can I integrate observability hooks for LangSmith into my agent testing pipelines?▼

Yes, you can integrate observability hooks for LangSmith into your agent testing pipelines. The templates include built-in support for LangSmith and Prometheus to monitor agent performance and automate testing scenarios.

What's the best way to implement typed state management and guardrails for AI agents?▼

The best way to implement typed state management and guardrails is using production-ready scaffolds. These templates provide the necessary structure to enforce strict state controls and safety measures within your LangGraph workflows.

Does this approach support human-in-the-loop (HITL) testing for agent development?▼

Yes, this approach supports human-in-the-loop (HITL) testing for agent development. The templates are designed to be HITL-ready, allowing you to seamlessly integrate manual oversight into your automated testing pipelines.

Why do I need checkpointing in my multi-agent LangGraph workflows?▼

You need checkpointing in multi-agent LangGraph workflows to ensure fault tolerance and state persistence. It allows your agents to recover from failures and resume complex graph workflows without losing prior context.