LLMs, RAG, AI Agents… and Agentic AI — Explained SimplyThe world of AI can feel overwhelming.Every week there’s a new term: LLM. RAG. AI Agent. Agentic AI.It can feel like jargon is multiplying faster than understanding.But these terms actually describe something simple.They represent different levels of capability — from thinking, to knowing, to acting, to operating independently.Let’s walk through them clearly.LLMs — The BrainAt the heart of modern AI is the Large Language Model (LLM).This is what powers tools like ChatGPT.An LLM is trained on massive amounts of text and code. Because of that, it can: Write and summarise Answer questions Generate ideas Translate languages Help with coding It’s very good at recognising patterns in language and predicting what comes next.But it has limits.It doesn’t automatically know your company policies. It doesn’t have access to real-time information unless connected to something. And sometimes it can confidently give incorrect answers.So think of an LLM as a very intelligent brain — but one working from its own training alone.RAG — Giving the Brain a LibraryRAG stands for Retrieval Augmented Generation.In simple terms, it gives the LLM access to relevant, up-to-date information.Instead of guessing an answer, the system can: Search your documents Retrieve the right information Use that information to respond accurately If an LLM is the brain, RAG is the library.It connects AI to real company knowledge, live documents, databases, or internal systems.This reduces hallucinations and makes responses more trustworthy.AI Agents — Moving From Thinking to DoingAn AI Agent takes things further.It doesn’t just answer questions. It can take action.An agent can: Send emails Update CRM records Run reports Search the web Call APIs Use software tools It can break a big task into smaller steps, plan what to do first, and execute those steps in sequence.Instead of just generating a response, it completes work.This is where AI moves from being a tool you talk to — to something that works alongside you.Agentic AI — When AI Starts Operating With GoalsAgentic AI is not a completely different technology.It’s what happens when AI systems are designed to operate with: Ongoing goals Long-term memory Feedback loops Self-correction Multi-step planning over time Rather than waiting for one prompt at a time, an agentic system can pursue objectives.For example:Instead of “Summarise this report,” you might say:“Monitor our competitors weekly and alert me if pricing changes.”An agentic system could: Check regularly Compare changes Adapt its approach Notify you when something meaningful happens It behaves less like a chatbot — and more like a junior operator inside your organisation.That’s the shift.The Simple Way to Understand the StackYou can think of it like this:LLM → Thinks RAG → Knows AI Agent → Acts Agentic AI → Operates with goalsEach layer builds on the one before it.And most companies today are still at the “thinking” stage.Very few are truly designing systems that act — and even fewer are building systems that operate with autonomy.Why This MattersThis isn’t about terminology.It’s about capability.If you only use LLMs, you increase personal productivity. If you add RAG, you improve accuracy and trust. If you add Agents, you automate workflows. If you design agentic systems, you create digital operators. That’s where the real transformation begins.