Key Concepts

• Knowledge Center (The External Memory): This is the source of factual, up-to-date, or specialized information (e.g., company PDFs, technical manuals, the live internet).

• Tools (The Hands and Senses): These are still the functions an agent can use. In an LLM-powered system, this becomes more concrete:

  • Retrieval System: A tool to search the knowledge base.

  • Code Interpreter: A tool to run Python code for calculations or data analysis.

  • Web Browser: A tool to access live information from the internet.

  • APIs: Tools to interact with other software (e.g., booking a flight, sending an email).

• Large Language Model (LLM) (The Brain): This is where the core intelligence resides. The LLM is the central component that enables the agent's advanced capabilities. It serves multiple critical functions:

  • Understanding and Reasoning: The LLM processes the user's request, understands the intent, and reasons about how to achieve the goal.

  • Planning and Tool Selection: Based on its understanding, the LLM creates a step-by-step plan and decides which tool to use at each step. For example, it might reason, "The user is asking about today's weather. I need to use the web browser tool to search for it."

  • Synthesizing Information: After a tool retrieves information from a knowledge base (or the web), the LLM takes this raw data and synthesizes it into a coherent, human-readable answer.

• Agent (The Unified Self): The Agent is the complete entity that is powered by the LLM. It is the LLM combined with its set of tools and its access to the knowledge base. The agent is what you interact with; it has a goal, it can plan, and it can act. Without the LLM, the agent would be a simple, rule-based system. With the LLM, it becomes an intelligent, reasoning entity.

• Multi-Agent System (The Collaborative Network): This is a network of LLM-powered agents. Each agent in the system has its own LLM "brain" and potentially its own specialized tools and knowledge. They collaborate by communicating with each other—sending messages that their respective LLMs process and act upon. For example, a "researcher" agent could find information and pass it to a "writer" agent to summarize.