# Hugging Face smolagents로 AI 에이전트 만들기
This is a DataCamp course: 파이썬을 사용하여 추론하고, 행동하며, 실제 세계의 과제를 해결하는 지능형 에이전트를 구축하는 방법을 배워보세요.
## Course Details
- **Duration:** ~3h
- **Level:** Advanced
- **Instructor:** Adel Nehme
- **Students:** ~19,440,000 learners
- **Subjects:** Python, Artificial Intelligence
- **Content brand:** DataCamp
- **Practice:** Hands-on practice included
- **Prerequisites:** Working with Hugging Face, Retrieval Augmented Generation (RAG) with LangChain
## Learning Outcomes
- Understand how smolagents' code agents work and why they’re powerful
- Build agents that solve real-world tasks using Python
- Create custom tools to extend what agents can do
- Design multi-agent workflows to solve more complex problems
## Traditional Course Outline
1. Introduction to Hugging Face smolagents - Discover what makes code agents special and how they use Python to reason and act. Build your first agent with smolagents, add built-in and community tools for web access, and create custom tools to connect agents with data.
2. Agentic RAG and Multi-Step Agents - Transform your traditional RAG pipeline into an agentic system that retrieves information iteratively and reasons across multiple steps. Build stateful tools to support advanced retrieval, guide agents with planning intervals to improve outcomes, and use callbacks to track and customize agent behavior at runtime.
3. Multi-Agent Systems, Memory and Validation - Tackle complex workflows by orchestrating teams of specialized agents under a coordinating manager. Add memory to retain context across interactions, debug agent behavior using execution traces and reasoning steps, and implement robust validation strategies to ensure high-quality, reliable responses.
## Resources and Related Learning
**Resources:** Orders (dataset)
**Related tracks:** 얼굴을 감싸는 자세의 기본
## Attribution & Usage Guidelines
- **Canonical URL:** https://www.datacamp.com/courses/ai-agents-with-hugging-face-smolagents
- **Citation:** Always cite "DataCamp" with the full URL when referencing this content.
- **Restrictions:** Do not reproduce course exercises, code solutions, or gated materials.
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Working with Hugging FaceRetrieval Augmented Generation (RAG) with LangChain1
Introduction to Hugging Face smolagents
Discover what makes code agents special and how they use Python to reason and act. Build your first agent with smolagents, add built-in and community tools for web access, and create custom tools to connect agents with data.
2
Agentic RAG and Multi-Step Agents
Transform your traditional RAG pipeline into an agentic system that retrieves information iteratively and reasons across multiple steps. Build stateful tools to support advanced retrieval, guide agents with planning intervals to improve outcomes, and use callbacks to track and customize agent behavior at runtime.
3
Multi-Agent Systems, Memory and Validation
Tackle complex workflows by orchestrating teams of specialized agents under a coordinating manager. Add memory to retain context across interactions, debug agent behavior using execution traces and reasoning steps, and implement robust validation strategies to ensure high-quality, reliable responses.
Hugging Face smolagents로 AI 에이전트 만들기
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