A few weeks ago I started digging into agentic AI, not just what it is, but how it actually works under the hood. The distinction hit me pretty quickly: a chatbot responds. An AI agent reasons, plans, and acts.
Imagine asking a regular AI chatbot to plan a trip to Japan. It gives you a solid list, but you still have to book flights, find hotels, and coordinate everything yourself. An AI agent would check your calendar, search for flights within budget, book the hotel, build an itinerary, and confirm everything, only checking in when a real decision is needed. That is the difference.
The Four Core Components
Every AI agent is built from the same four parts working together:
- The LLM (The Brain). A large language model acts as the central coordinator. It interprets your request, reasons through what needs to happen, and decides which tools to use and in what order. It is the project manager of the agent.
- Memory Modules. Agents have short-term memory (tracking current progress) and long-term memory (retaining past interactions and preferences). Without memory, every interaction starts from scratch.
- Planning Modules. This is what separates agents from chatbots. The agent decomposes complex tasks into steps, either upfront using Chain of Thought reasoning, or iteratively, adjusting based on results and feedback.
- Tools (The Hands). APIs, databases, RAG pipelines, even other AI models. Tools are how the agent actually interacts with the outside world. Without tools, an agent can only think. It cannot act.
The Perceive → Reason → Act → Learn Loop
The way an agent operates follows a four-step loop:
- Perceive. Take in the task, context, and relevant external data
- Reason. Break the goal into a plan of action using the LLM
- Act. Execute each step using tools and APIs
- Learn. Feed results back into the system to improve future performance
That last step, Learn, is where the data flywheel kicks in. Every real-world interaction generates data. That data gets used to fine-tune the model. The improved model performs better, generates better data, and the cycle accelerates.
RAG: How Agents Stay Grounded
LLMs have a knowledge cutoff and can hallucinate confidently wrong answers. Retrieval-Augmented Generation (RAG) solves this by letting the agent retrieve real, up-to-date information from external sources before generating a response, grounding its reasoning in actual facts rather than patterns from training data.
Key Takeaway
AI agents represent the evolution from AI that talks to AI that does. The building blocks, LLMs, memory, planning, and tools, are already mature. What is changing is how developers combine them into systems that can autonomously solve multi-step, real-world problems.