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This isn’t just another AI course—this is a journey into the heart of intelligent systems. We’re talking about AI that acts, learns, and adapts: Agentic AI.
Key Learning Areas:
- GenAI – Lay the foundation with Generative AI concepts.
- Basics of LLMs – Understand Large Language Models (LLMs) and their text generation, translation, and reasoning abilities.
- Prompt Engineering – Master techniques to extract optimal responses from AI models.
- Data Handling & Processing – Learn to clean, prepare, and utilize data effectively for AI agents.
- RAG Essentials – Implement Retrieval-Augmented Generation (RAG) to integrate external knowledge.
- API Wrappers – Bridge your code with external AI services efficiently.
- AI Agents – Explore autonomous agents and their decision-making processes.
- Agentic Frameworks – Use existing frameworks to streamline development.
- Building Simple AI Agents – Hands-on development from scratch.
- Agentic Workflow – Design efficient information and action flows.
- Agentic Memory – Enable AI to learn from past interactions.
- Agentic Evaluation – Measure AI performance using key metrics.
- Multi-Agent Collaboration – Develop systems where multiple agents work together.
- Advanced Agentic RAG – Enhance RAG integration for smarter AI.
You Should Know:
- Setting Up a Python Environment for AI Development
Create a virtual environment python -m venv agentic_ai source agentic_ai/bin/activate Linux/Mac .\agentic_ai\Scripts\activate Windows Install essential libraries pip install openai langchain transformers torch pandas numpy
2. Basic Prompt Engineering with OpenAI
import openai
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are an AI expert."},
{"role": "user", "content": "Explain Agentic AI in simple terms."}
]
)
print(response['choices'][bash]['message']['content'])
3. Data Preprocessing for AI Agents
import pandas as pd
Load and clean data
data = pd.read_csv("dataset.csv")
data.dropna(inplace=True)
data = data[~data.duplicated()]
Text normalization
data['text'] = data['text'].str.lower().str.replace('[^\w\s]', '')
4. Building a Simple RAG System
from langchain.document_loaders import WebBaseLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
Load and index documents
loader = WebBaseLoader("https://example.com/ai-article")
docs = loader.load()
embeddings = OpenAIEmbeddings()
db = FAISS.from_documents(docs, embeddings)
Retrieve relevant info
query = "What is Agentic AI?"
retrieved_docs = db.similarity_search(query)
print(retrieved_docs[bash].page_content)
5. Running a Multi-Agent Simulation
from autogen import AssistantAgent, UserProxyAgent
Define AI agents
assistant = AssistantAgent("assistant")
user_proxy = UserProxyAgent("user_proxy")
Initiate a conversation
user_proxy.initiate_chat(assistant, message="Plan a cybersecurity strategy.")
What Undercode Say:
Agentic AI is revolutionizing automation, decision-making, and human-AI collaboration. Mastering these concepts requires hands-on practice with real-world datasets, frameworks like LangChain and AutoGen, and continuous experimentation.
Essential Linux & Windows Commands for AI Developers:
Monitor GPU usage (Linux)
nvidia-smi
Check running Python processes
ps aux | grep python
Manage Python dependencies
pip freeze > requirements.txt
pip install -r requirements.txt
Windows equivalent for GPU check
nvidia-smi.exe
Clean Python cache
find . -type d -name "<strong>pycache</strong>" -exec rm -r {} +
Expected Output: A structured, hands-on guide to mastering Agentic AI with practical code snippets, commands, and frameworks.
( optimized for AI/cybersecurity learners. Removed non-technical content and comments.)
References:
Reported By: Habib Shaikh – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
