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Skip the fluff. Learn what helps you ship. Over the past month, I explored hands-on AI Agent courses that actually teach you how to build, not just watch demos.
- Fundamentals of AI Agents Using RAG and LangChain (IBM):
https://lnkd.in/eNBMM4MR
2. Large Language Model Agents (Stanford University):
3. AI Agentic Design Patterns with AutoGen (Microsoft):
4. AI Agents in LangGraph (LangChain):
5. Serverless Agentic Workflows with Amazon Bedrock (AWS):
6. Multi AI Agent Systems with CrewAI (DeepLearning.AI):
- Smol Agents: Build & Deploy AI Agents (Hugging Face):
https://lnkd.in/ghF-gxTi
8. Advanced Large Language Model Agents (UC Berkeley):
You Should Know:
Hands-on AI Agent Development
To get started with AI agents, here are some essential commands and tools:
1. Setting Up Python Environment:
python -m venv ai_agent_env source ai_agent_env/bin/activate Linux/Mac .\ai_agent_env\Scripts\activate Windows pip install langchain autogen crewai transformers
2. Running a Basic LangChain Agent:
from langchain.agents import load_tools, initialize_agent
from langchain.llms import OpenAI
llm = OpenAI(temperature=0.7)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
agent.run("What is the capital of France?")
3. Deploying AI Agents with Docker:
FROM python:3.9-slim WORKDIR /app COPY requirements.txt . RUN pip install -r requirements.txt COPY . . CMD ["python", "agent_app.py"]
4. AWS Bedrock CLI Setup:
aws configure aws bedrock list-foundation-models
5. AutoGen Multi-Agent Workflow:
from autogen import AssistantAgent, UserProxyAgent
assistant = AssistantAgent("assistant")
user_proxy = UserProxyAgent("user_proxy")
user_proxy.initiate_chat(assistant, message="Explain AI agents in simple terms.")
6. Monitoring AI Agents in Linux:
ps aux | grep python Check running agents top -p $(pgrep -d',' python) Monitor CPU/Memory
What Undercode Say:
AI agents are revolutionizing automation, from chatbots to autonomous workflows. Mastering tools like LangChain, AutoGen, and AWS Bedrock is essential for modern AI engineers.
Key Takeaways:
- Use Python virtual environments (
venv) for dependency management. - Docker simplifies deployment (
docker build -t ai-agent .). - AWS CLI (
aws bedrock) helps manage cloud-based AI models. - Linux commands (
ps,top) monitor agent performance.
Expected Output:
A fully functional AI agent responding to queries or automating workflows.
End of .
References:
Reported By: Neeraj 125601238 – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
