Three working examples showing how AI agent development evolved - from traditional RAG to batteries-included SDKs and skill-based frameworks
Overview
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 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. 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. 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.
README
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
| Era | Approach | Example |
|---|---|---|
| 2024-2025 | Chunk docs, embed, vector search, wire the agent loop yourself | rag-agent-demo/ |
| 2026 (SDKs) | Built-in tools + custom MCP servers, no glue code | claude-agent-sdk-demo/ |
| 2026 (Frameworks) | Type-safe agents with progressive skill disclosure | pydantic-ai-skills-demo/ |
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()withsave_noteandsearch_notestools - 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
- Multi-provider - Works with OpenRouter, OpenAI, or Ollama
.claude/skills/pptx-generator/SKILL.md
A real-world skill file showing how skills are structured as packaged expertise in markdown. This is the PowerPoint generator skill from the Dynamous Second Brain - a concrete example of the SKILL.md format discussed in the video.
diagram.excalidraw
The Excalidraw diagram used throughout the video covering:
- The Old Way (2024-2025) - Pick framework, define tools, set up RAG, wire agent loop
- Batteries-Included SDKs (2026) - Claude Agent SDK, Codex, built-in tools, skills vs tools
- Frameworks Still Matter - Pydantic AI, LangGraph, OpenAI Agents, CrewAI
- The Decision - When to use an SDK vs a framework
- What Happened to RAG? - From naive RAG to agentic RAG and hybrid approaches
Links
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Install
npx skillfish add coleam00/evolution-of-ai-agents