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Agentic AI represents the next evolution in LLM-based applications, offering robust workflows with minimized hallucinations. Below are essential resources and practical implementations to master Agentic AI.
Understand the Fundamentals
1. Google Agents Whitepaper
2. Google Companion Paper – Advanced Concepts
3. ReAct: Reasoning + Acting with LLMs
4. Agent as a Judge (for evaluations)
Learn by Doing – Short Courses
- AI Agentic Design Patterns with AutoGen
https://lnkd.in/gE_dMbYa - Multi-AI Agent Systems with crewAI
https://lnkd.in/gsfXeXH3 - AI Agents in LangGraph
https://lnkd.in/gmgtwpjf - Building Agentic RAG with LlamaIndex
https://lnkd.in/g9e-kHxF
GitHub Repository for Hands-on Implementation
- GenAI_Agents by Nir Diamant
https://lnkd.in/g5HHZrJK
You Should Know: Practical Implementation
1. Setting Up a Basic ReAct Agent
from langchain.agents import load_tools, initialize_agent
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent="react-docstore", verbose=True)
agent.run("What is Agentic AI?")
2. Evaluating Agents with LangGraph
from langgraph.graph import Graph
from langchain.agents import AgentExecutor
workflow = Graph()
workflow.add_node("agent", AgentExecutor.from_agent_and_tools(agent, tools))
workflow.set_entry_point("agent")
app = workflow.compile()
app.invoke({"input": "Explain multi-agent coordination."})
3. Running AutoGen for Multi-Agent Workflows
pip install autogen
from autogen import AssistantAgent, UserProxyAgent
assistant = AssistantAgent("assistant")
user_proxy = UserProxyAgent("user_proxy")
user_proxy.initiate_chat(assistant, message="Design an AI workflow.")
4. Linux/Windows Commands for AI Development
- Monitor GPU Usage (Linux):
nvidia-smi watch -n 1 gpustat
- Windows GPU Check:
Get-CimInstance -ClassName Win32_VideoController | Select-Object Name, AdapterRAM
- Kill Python Processes:
pkill -f "python.agent"
What Undercode Say
Agentic AI is reshaping automation by enabling structured, multi-agent workflows. Mastering foundational papers, frameworks like AutoGen and LangGraph, and real-world implementations is crucial. Utilize Linux/Windows commands for system optimization and debugging. The future lies in self-correcting, traceable AI agents—start building now.
Expected Output:
A structured, code-backed guide to Agentic AI with actionable steps, eliminating non-technical content while maximizing hands-on learning.
References:
Reported By: Shivanivirdi If – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
