LLM, RAG, Agentic AI, and AI Agents: A Technical Breakdown

By /

Listen to this Post

Featured Image

Introduction

Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and AI Agents represent different layers of AI evolution—from static text prediction to autonomous task execution. Understanding their distinctions is crucial for deploying the right AI solution in cybersecurity, IT automation, and enterprise workflows.

Learning Objectives

  • Differentiate between LLMs, RAG, Agentic AI, and AI Agents.
  • Learn practical implementations of each in cybersecurity and IT automation.
  • Explore command-line and API integrations for AI-enhanced workflows.

1. LLM: Text Prediction with Python API

Command/Code Snippet:

from transformers import pipeline 
llm = pipeline("text-generation", model="gpt-4") 
response = llm("Explain zero-day exploits in cybersecurity.") 
print(response[bash]['generated_text'])

Step-by-Step Guide:

  1. Install Hugging Face’s `transformers` library: pip install transformers.
  2. Load a pre-trained LLM (e.g., GPT-4) for text generation.
  3. Input a cybersecurity query to generate context-aware responses.

Use Case: Automating threat intelligence reports.

2. RAG: Real-Time Data Retrieval for LLMs

Command/Code Snippet:

from langchain.document_loaders import WebBaseLoader 
from langchain.embeddings import OpenAIEmbeddings

loader = WebBaseLoader("https://www.cisa.gov/news-events/cybersecurity-advisories") 
docs = loader.load() 
rag_embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

Step-by-Step Guide:

  1. Use `langchain` to load cybersecurity advisories from CISA’s website.

2. Generate embeddings for semantic search.

  1. Integrate with an LLM to answer queries with up-to-date threat data.

Use Case: Dynamic vulnerability analysis.

3. Agentic AI: Autonomous Decision-Making

Command/Code Snippet (AutoGen):

from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent("cyber_analyst") 
user_proxy = UserProxyAgent("admin") 
user_proxy.initiate_chat(assistant, message="Analyze this log for SQLi attempts: [bash]")

Step-by-Step Guide:

  1. Deploy AutoGen to create AI agents with memory and tool access.

2. Task the agent with analyzing attack logs.

  1. The agent autonomously classifies threats and suggests mitigations.

Use Case: SIEM log analysis.

4. AI Agents: API-Driven Task Execution

Command/Code Snippet (Bash + AWS CLI):

aws lambda invoke --function-name isolate_compromised_instance \ 
--payload '{"instance_id":"i-1234567890abcdef0"}' response.json

Step-by-Step Guide:

  1. Configure an AI agent to trigger AWS Lambda for incident response.

2. Pass compromised instance IDs via API calls.

3. Automate isolation of breached resources.

Use Case: Cloud incident response.

5. Cybersecurity Hardening with AI Agents

Command (Linux):

sudo apt install clamav && sudo freshclam && sudo clamscan -r / --remove

Step-by-Step Guide:

1. Install ClamAV for malware detection.

2. Update virus definitions.

3. Scan and remove infected files system-wide.

AI Integration: Pair with an AI agent to prioritize alerts.

What Undercode Say

  • Key Takeaway 1: LLMs alone lack real-time data; RAG bridges this gap for threat intelligence.
  • Key Takeaway 2: Agentic AI and AI Agents transform reactive security into proactive defense.

Analysis:

The fusion of RAG with Agentic AI enables autonomous cybersecurity systems—e.g., an AI agent fetching the latest CVE data, analyzing it via LLM, and patching vulnerabilities via APIs. By 2026, 40% of SOCs will deploy such AI agents, reducing response times from hours to seconds.

Prediction:

AI Agents will dominate penetration testing, automating exploits (e.g., SQLi, XSS) and mitigations via frameworks like LangChain and AutoGen. Ethical concerns will drive demand for AI-powered red-team tools with built-in compliance checks.

IT/Security Reporter URL:

Reported By: Greg Coquillo – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅

Join Our Cyber World:

💬 Whatsapp | 💬 Telegram

Related Posts: