The "Lore" of AI Agents

Before you build, you must understand. An Agent is not just a chatbot—it is a system that perceives, reasons, and acts.

🧠 The LLM (The Brain)

Large Language Models (like GPT-4 or Claude) provide the reasoning capabilities. They don't just predict text; they can plan steps, interpret instructions, and decide which tools to use.

Key Concept: In-Context Learning

🔄 The Agent Loop

Unlike a passive chatbot, an agent follows a loop: Observe (get state) → Think (LLM reasoning) → Act (use tool) → Observe again. This allows it to self-correct errors.

Pattern: ReAct (Reasoning + Acting)

🛠️ Tools (The Hands)

LLMs live in a text world. To affect the real world (or the digital one), they need Tools. These are functions defined in code (e.g., search_web(), write_file()) that the LLM can trigger.

Technique: Function Calling

💾 Memory (The Context)

LLMs are stateless. To make an agent "remember" past conversations, we use Short-term Memory (a list of past messages) and Long-term Memory (Vector Databases for semantic search).

Architecture: RAG (Retrieval Augmented Generation)

👁️ Perception

Modern agents can "see." By processing images (Multimodal LLMs), agents can analyze screenshots, read charts, or navigate visual interfaces, expanding their input beyond just text.

Modality: Vision Transformers

🛡️ Safety & Guardrails

Agents that can execute code are powerful but dangerous. We need "Guardrails"—supervisor agents or validation layers—to check if an action is malicious before it runs.

Protocol: Human-in-the-loop

Core Components

Agent Architecture Pipeline

Select components from the left to build your agent...


# Select components to generate Python code...
from langchain.agents import initialize_agent
from langchain.llms import OpenAI

# Waiting for architecture...