So far, we have built an LLM application with several parts. RAG gives the model external knowledge. Tools allow it to interact with external systems. But what happens when the LLM can decide what to do next? That is where the idea of an AI Agent starts to become useful.
From One Step to Multiple Steps
A normal chatbot might take a question, send it to an LLM, and return an answer. But an agent-style system can work differently. When a user asks a question, there may not be a single tool that can answer it. The system may need to go through several steps. At each step, the LLM can decide what to do next based on the result of the previous step.
An AI Agent
A basic agent starts with a goal, reasons about what to do, chooses a tool, takes action, observes the result, and reasons again. This process continues until it reaches a final result. The important difference is that the workflow does not always have to be fully defined in advance. The model can use the information it receives at each step to decide what to do next.
We can now connect everything. Prompt tells the model what we want. RAG gives it relevant knowledge. Tools give it capabilities. The LLM reasons about the information. And agent-style orchestration allows the system to repeat this process until it completes a task.
That is the bigger picture behind many modern AI applications. LLM engineering is no longer just about “How do I write a better prompt?” but also about “How do I build a better system around the model?”
