A Beginner's Guide to AI Agents: Core Principles and Learning Paths Explained

Introduction: Why Are Agents So Hot in the AI World?

Over the past two years, AI Agents have become one of the hottest topics in artificial intelligence. From various frameworks and open-source projects to industry talks, Agents seem to be everywhere. But for beginners, a core question persists: What exactly is an Agent? How does it relate to large language models? And how should I start learning from scratch?

This article is based on an introductory lecture on AI Agents by B-site creator Tang Yudi. Unlike typical presentations that pile on abstract concepts, the author deliberately avoided opening slides to recite definitions. Instead, he starts from practical experience to help people truly understand the essence of Agents. As he emphasizes: There is no single, perfect definition of an Agent — it's a very "fluid" concept.

What Is an AI Agent: Explained Through a "Go to Class for Me" Example

A Vivid Analogy to Help You Instantly Understand Agents

The word "Agent" is often translated as "proxy" or "intelligent entity." Here's a very down-to-earth example to explain it: Imagine one morning you can't get out of bed, so you tell your roommate, "Hey buddy, go to class for me."

This seemingly simple command actually needs to be broken down into many steps:

• When the teacher takes attendance, your roommate needs to raise their hand and say "here" for you
• When the teacher assigns homework, your roommate needs to submit it on your behalf
• When the teacher covers key points, your roommate needs to take notes for you
• By lunchtime, your roommate figures out you're still sleeping or playing games, and even brings food back for you

This is the core logic of an Agent: You give it a request or command, but that request can't be accomplished in a single step — it needs to be broken down into many sub-processes, each of which is completed individually.

The Fundamental Difference Between Agents and Large Language Models

Many people confuse Agents with large language models. To understand the difference, you first need to know what a large language model is. A Large Language Model (LLM) is a large-scale neural network model trained on the Transformer architecture, such as GPT-4, Claude, and Llama. These models learn statistical patterns and knowledge representations from massive text datasets through pre-training. Their core capabilities include text generation, reasoning, and code writing, but at their essence, they are still next-token prediction based on probability. Understanding this is crucial for understanding Agents — every decision an Agent makes fundamentally leverages the probabilistic reasoning capabilities of the LLM.

The key differences between the two are:

LLMs operate in a "question-and-answer" fashion — you ask a question, it gives an answer; you ask again, it answers again. This is a passive, one-shot interaction.

Agents follow a complete "understand → decompose → execute" pipeline:

1. Step one: understand your command
2. Step two: break the task into multiple sub-tasks based on that understanding (what to do first, second, third…)
3. Step three: execute each sub-task one by one, ultimately completing the overall goal

In other words, the LLM is the Agent's "brain," while the Agent is a complete "action system" built around that brain — combining capabilities like perceiving the environment, making plans, calling tools, and remembering history.

How Agents Work: Everything Is Driven by the LLM

How Agents Decompose Tasks

Many people assume that an Agent "has a mind of its own" and can truly understand and think. But the truth is: Inside an Agent, everything is accomplished using the LLM.

Using the "go to class for me" example, the Agent's actual workflow looks like this:

• First, it takes the command along with the user's contextual information (e.g., "Tang Yudi doesn't like going to class but loves reviewing notes before exams") as a Prompt
• This is sent to the LLM, which breaks the overall task into several sub-commands
• The LLM returns: do A, do B, do C

After completing each sub-task, the Agent queries the LLM again: "What environment am I in now? What command did I receive? What should I do next?"

So the essence of an Agent is that it engages in extensive interaction with the LLM. For every decision it makes, for every task it performs, it asks the LLM, "What should I do?" This is critically important — an Agent is not something entirely separate from the LLM; it's a way to better leverage the LLM.

This pattern of making multiple calls to the LLM is known in academia as the ReAct (Reasoning and Acting) paradigm or the Plan-and-Execute paradigm. The ReAct pattern has the model alternate between Thought and Action, with each step based on observations from the previous step to determine the next operation. This design enables Agents to handle complex tasks requiring multi-step reasoning, rather than just generating a one-shot answer.

The Importance of Rule Design: Why Agents Still Depend on Human-Crafted Prompts

This is one of the most valuable insights for understanding Agents:

As of now, Agents cannot truly understand every human behavior. This is because LLMs suffer from hallucinations — their outputs can be unstable or even "divergent."

LLM hallucination refers to the model generating content that appears reasonable but is actually incorrect or fabricated. This stems from the LLM's generation mechanism — it generates text by sampling from probability distributions and doesn't possess genuine fact-verification capabilities. In Agent scenarios, the hallucination problem is particularly severe because an erroneous output from one sub-task can be amplified by subsequent steps, causing the entire task chain to go off track. This is one of the fundamental reasons why Agents need human-crafted rules as constraints.

So how do Agents ensure stable execution? The answer is — rules.

Rules, put simply, are human-written Prompts. In Agent architectures, a Prompt is far more than just the user's raw input — it includes system instructions (System Prompt), contextual information, conversation history, tool call results, and more, all concatenated into a multi-layered information package. Well-designed Prompts can significantly improve an Agent's task completion quality. For example, for the class-attendance task, you'd explicitly specify: "sign in for me, do this for me, do that for me" — hardcoding the key processes to ensure the Agent completes the task smoothly and correctly.

Many frameworks, projects, and talks today over-mystify Agents, as if they can analyze and understand everything on their own. But virtually all truly successful cases to date are the result of carefully human-designed rules.

Take software development as an example: first, a Product Manager (PM) defines the step-by-step process, then hands it off to the corresponding developers to implement each step. The whole process relies on thorough workflow design. Agents themselves still struggle to possess genuine autonomous thinking — it's not that they absolutely can't, but if you don't design the rules well, the results will be poor.

This reality has given rise to an emerging engineering role — Agent engineers or Prompt engineers. Their core work involves designing reasonable task decomposition workflows, writing high-quality system prompts, defining tool-calling specifications, and building error handling and fallback mechanisms to ensure Agents operate reliably across various edge cases.

Learning Paths for Beginners: Different Approaches for Different Goals

Depending on your learning objectives, beginners can be divided into two groups, each with very different recommended paths.

Category 1: Programmers Who Want to Build and Develop Agents

If you want to go deep into the underlying technology and do development, here's the recommended systematic learning path:

1. Build a solid programming foundation: Master Python, frameworks, data analysis, and other foundational skills first
2. Go straight to LLMs: You can skip the tedious NLP fundamentals and jump directly to large language models, with a focus on the Transformers library. Transformers is an open-source library developed by Hugging Face that provides a unified interface for thousands of pre-trained models, supporting tasks like text classification, generation, and question answering. It's currently the most mainstream toolkit for LLM development.
3. Model deployment: Learn how to fine-tune, quantize, and deploy LLMs. Fine-tuning means continuing to train a pre-trained model with domain-specific data to adapt it to a particular task. Quantization compresses model parameters from high precision (e.g., FP32) to low precision (e.g., INT4/INT8) to reduce memory usage and speed up inference. Common tools include GPTQ, AWQ, and bitsandbytes.
4. Knowledge base construction: Learn RAG (Retrieval-Augmented Generation) technologies. RAG is a key technique for addressing LLM knowledge freshness and accuracy issues. The core idea is: before generating an answer, retrieve relevant document fragments from an external knowledge base, inject them as context into the Prompt, and then have the LLM generate the final answer. RAG involves document chunking, vector embedding, vector databases (e.g., Pinecone, Milvus, FAISS), similarity search, and more — it's a foundational capability for building enterprise-grade Agent applications.
5. Choose an Agent framework: Pick an open-source framework for deep study — LangChain is recommended. LangChain is currently one of the most popular LLM application development frameworks, created by Harrison Chase in 2022. It provides core abstractions like Chain, Agent, Memory, and Tool, helping developers rapidly build complex LLM-based applications. LangChain's Agent module supports multiple reasoning paradigms including ReAct and Plan-and-Execute, allowing models to dynamically select tools and execute multi-step operations. Similar frameworks include LlamaIndex (focused on data indexing and retrieval), AutoGen (Microsoft's multi-Agent collaboration framework), and CrewAI (a role-playing multi-Agent framework), each with different strengths. Learners can choose based on their needs.

For developers, here's an important piece of advice: Don't just learn how to use a framework — learn to read the source code. Figure out which algorithms the framework uses, what each algorithm does, which modules it employs, and what role each module plays. Aim for source-code-level understanding. While this takes significant time, for anyone who wants to evolve from a "user" to a "developer," it's absolutely worth it.

Category 2: Non-Technical Background, Just Want to Use Agents to Build Applications

If you simply want to build applications and create tools for yourself, the advice is straightforward:

Just pick between Claude Code and Codex.

Claude Code is a programming Agent tool from Anthropic built on the Claude model. It can understand codebases, write code, and execute commands in a terminal environment to complete complex software development tasks. Codex is OpenAI's programming Agent product with similar autonomous coding and task execution capabilities. These two tools represent the cutting edge of Agent applications in software development — users simply describe their needs in natural language, and the tool autonomously handles code writing, debugging, deployment, and more, dramatically lowering the barrier for non-technical users to build applications.

Both tools are excellent and can help you with development, office tasks, image editing, and various other tasks. Which one to choose depends on your specific needs and use cases.

Summary: How to Properly Understand AI Agents

Returning to the original question, here's a clear summary of what an Agent is:

An Agent is designed to help us accomplish a task, but to do so, it decomposes the task — breaking the overall task into many sub-tasks, then for each sub-task, combining your "rules of the game" (i.e., prompts) to call the LLM, ultimately producing the desired output.

From a technical architecture perspective, a complete Agent system typically contains these core components: an LLM (for reasoning and decision-making), a memory system (short-term memory for current conversation context, long-term memory for historical interaction information), a toolset (external capabilities like search engines, code executors, database queries, etc.), and a planning module (for task decomposition and execution strategy formulation). These components work together to enable the Agent to handle complex tasks far beyond the scope of a single LLM call.

The design philosophy of an Agent can be summed up in one sentence: Have the Agent mimic human behavior — however a human would do it, have the Agent follow that same logic.

The value of this understanding framework is that it reveals both the Agent's real capabilities (essentially orchestrating and calling LLMs) and its practical limitations (it can't do without human rule design). For every learner looking to get started with Agents, building this pragmatic and clear-eyed understanding is far more important than chasing mystified concepts.

It's worth noting that Agent technology is evolving rapidly. From the earliest single-Agent systems to today's Multi-Agent collaboration architectures, to advanced Agents with self-reflection and self-improvement capabilities, this field sees major breakthroughs every few months. But no matter how the technology iterates, the core principles outlined in this article — LLM-driven, task decomposition, rule constraints — will remain the fundamental paradigm of Agent systems for the foreseeable future.