AI 에이전트 개발이 진화 한 방법을 보여주는 세 가지 작업 예 - 전통적인 RAG 기반 접근 방식에서 현대 배터리 포함 SDK 및 기술 기반 프레임 워크.

진화

예제

rag-agent-demo/ - 전통적인 접근법 (Python)

AI 에이전트를 구축하기위한 기본 재생 책이었다 고전적인 RAG 패턴. 전체 파이프라인 표시: 펑크 문서, 생성 embeddings, 벡터 데이터베이스에 저장, 관련 펑크를 검색, LLM에 피드.

** 스택:** Pydantic AI + PostgreSQL/pgvector + OpenAI embeddings

이 작품 - 그리고 여전히 큰 문서 corpora가있을 때 바로 전화입니다. 그러나 많은 용도의 경우, 아래 새로운 접근은이 인프라를 완전히 제거해야합니다.

claude-agent-sdk-demo/ - 배터리 포함 SDK (TypeScript)

How Building AI Agents Has Completely Changed

Three working examples showing how AI agent development has evolved - from the traditional RAG-based approach to modern batteries-included SDKs and skill-based frameworks.

The Evolution

Examples

rag-agent-demo/ - The Traditional Approach (Python)

The classic RAG pattern that was the default playbook for building AI agents. Shows the full pipeline: chunk documents, generate embeddings, store in a vector database, retrieve relevant chunks, feed to an LLM.

cd rag-agent-demo
cp .env.example .env     # Add your OpenAI key + database URL
docker compose up -d     # Start PostgreSQL with pgvector
uv sync
uv run python -m ingestion.ingest --documents documents/ --clean
uv run python cli.py

Stack: Pydantic AI + PostgreSQL/pgvector + OpenAI embeddings

This works - and it’s still the right call when you have large document corpora. But for many use cases, the newer approaches below eliminate the need for this infrastructure entirely.

claude-agent-sdk-demo/ - Batteries-Included SDK (TypeScript)

A research agent that comes with tools out of the box. No RAG pipeline, no vector database, no embedding infrastructure. The SDK provides Read, Write, WebSearch, Bash, and Grep as built-in tools, and you can add your own via MCP servers.

cd claude-agent-sdk-demo
bun install
bun run agent.ts "How AI agent frameworks evolved in 2026"

What this demonstrates:

• Built-in tools - Read, Write, WebSearch, Bash, Grep with zero setup
• Custom MCP server - createSdkMcpServer() with save_note and search_notes tools
• Subagents - Researcher and Writer agents that the orchestrator delegates to
• Hooks - Real-time monitoring of tool usage

Uses your local Claude Code CLI credentials - no API key needed.

pydantic-ai-skills-demo/ - Framework with Skills (Python)

A Pydantic AI agent with a skill system implementing progressive disclosure. Instead of loading all instructions upfront, skills are discovered at ~100 tokens each and loaded on demand - letting an agent access hundreds of capabilities without overwhelming its context window.

cd pydantic-ai-skills-demo
cp .env.example .env     # Add your API key
uv sync
uv run python -m src.cli

What this demonstrates:

• Progressive disclosure - Skills load in 3 levels (metadata -> instructions -> resources)
• 5 working skills - Weather, research assistant, recipe finder, world clock, code review
• Type safety - Full Pydantic models and typed dependencies