From LLMs to Agentic AI: Understanding the Real AI Stack Everyone is talking about AI. But most people are…
Nitesh M. — Product Security Lead
From LLMs to Agentic AI: Understanding the Real AI Stack
Everyone is talking about AI. But most people are mixing up terms like LLMs, RAG, AI Agents, and Agentic AI as if they mean the same thing.
They don’t.
Here’s the simplest way to understand the AI stack 👇
🔹 LLM (Large Language Model)
This is the “brain” trained on massive amounts of data.
Examples:
* ChatGPT
* Claude
* Gemini
* Llama
An LLM predicts the next best word/token based on patterns it learned during training.
By itself, an LLM only knows what it was trained on.
It doesn’t automatically know your company data, live internet updates, or internal documents.
🔹 RAG (Retrieval-Augmented Generation)
RAG gives the LLM “memory” or “access to external knowledge.”
Instead of relying only on training data:
1. The system searches relevant documents/data
2. Retrieves the most relevant information
3. Injects that into the prompt
4. The LLM answers using that context
Think of it as: LLM + Search Engine + Company Knowledge
This is how enterprises build:
* Internal AI assistants
* Policy chatbots
* Secure enterprise search
* AI over PDFs, tickets, codebases, or SharePoint
🔹 AI Agents
Agents go beyond “chat.”
They can:
* Make decisions
* Use tools/APIs
* Execute tasks
* Interact with systems
* Take multi-step actions
Example:
Instead of just answering:
“Your flight is delayed.”
An AI Agent could:
✔ check flight APIs
✔ rebook tickets
✔ notify your calendar
✔ send an email confirmation
Agents = AI that can act, not just respond.
🔹 Agentic AI
This is where things become truly autonomous.
Agentic AI systems can:
* Plan objectives
* Break tasks into sub-tasks
* Reason through workflows
* Use multiple agents/tools
* Self-correct
* Continue until goals are achieved
Example:
“Find security vulnerabilities in our application, prioritize them, create Jira tickets, and generate remediation guidance.”
No single prompt.
No human guiding every step.
The system orchestrates itself toward a goal.
💡 In short:
✅ LLM = understands/generates language
✅ RAG = gives AI access to external knowledge
✅ AI Agent = AI that can take actions
✅ Agentic AI = autonomous goal-driven AI systems
We’re moving from:
“AI that talks” → “AI that works.”
And that shift is going to redefine software, cybersecurity, engineering, operations, and business workflows over the next few years.
#AI #LLM #RAG #AIAgents #AgenticAI #ArtificialIntelligence #CyberSecurity #MachineLearning #GenerativeAI #DevSecOps
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Topics: #artificialintelligence, #devsecops, #agenticai, #machinelearning, #ai, #llm, #rag, #generativeai, #cybersecurity, #aiagents