Understand AI Agents in 5 Minutes: The Core Differences Between LLMs, Workflows, and Agents

What Exactly Is an Agent?

If an LLM is the brain, then an Agent is the employee — a "digital worker" that operates 24/7, never gets sick, never takes time off, and costs far less than human labor. Who wouldn't want one?

In recent years, "Agent" (intelligent agent) has become one of the hottest buzzwords in the AI world. Yet many people still think of it as little more than a souped-up chatbot. Drawing from educational content by Bilibili creator Kai, this article breaks down two core questions: What are the real differences between LLMs, Workflows, and Agents? And what exactly makes up an Agent?

Starting with LLMs: Where It All Begins

To understand Agents, you first need to understand LLMs (Large Language Models). Products like DeepSeek, GPT, and Doubao are all essentially LLM-based chatbots — they predict the next token based on what they've already seen.

For an LLM, the workflow is straightforward: input → LLM processing → output. It's like chatting with AI — you type "I'm sorry," it replies "No worries"; you say "Thank you," it responds in kind.

But here's the limitation: LLMs can only "listen and chat." If you hand it a PDF file, it can't read it — because an LLM has no built-in ability to perceive or process complex information from the external world.

Workflow: A Human-Designed Pipeline

To help LLMs handle more complex tasks, we can bolt on extra capabilities.

For example, to process a lengthy PDF, you might first connect a PDF reader tool; then, noticing the file contains chat logs and location screenshots, you add a key information extractor; finally, you chain these steps together to produce a condensed output.

That's a Workflow.

Here's a critical insight: no matter how many steps you chain together, it's still just a Workflow — not an Agent. The essence of a Workflow is executing a fixed sequence of steps defined by a human in advance — every action and its order is pre-designed by the developer.

Agent: Deciding What to Do on Its Own

Given the same PDF task, an Agent operates completely differently. It behaves more like an employee who figures things out independently:

• Encountering a PDF, it proactively thinks, realizes it can't read the content, and calls the PDF reader tool on its own;
• Hitting confusing chat logs, it autonomously analyzes the relationships between people mentioned;
• Coming across an unclear address, it actively connects to a maps tool to look it up;
• After completing the task, it self-reviews, considers whether any information was missed, and may even scrape additional data to generate a relationship diagram;
• Finally, it delivers the complete result to you.

In one sentence, here's the core difference between a Workflow and an Agent:

A Workflow executes steps defined by humans. An Agent decides for itself which steps to execute.

This is the most fundamental distinction — autonomy.

The Four Core Components of an Agent

Think of an Agent as a "digital employee" built from four essential parts.

1. LLM: The Brain

The LLM is the Agent's core brain — responsible for understanding natural language, analyzing task requirements, and forming execution plans. Without a brain, nothing else matters. But a brain alone isn't enough; you also need a body and hands to actually get things done.

2. Prompt: The Work Instructions

Prompt is often translated as "prompt" or "hint," but a more accurate framing is "work instructions."

The difference between a good and a bad Prompt can be enormous. A poor prompt just says "give me a simplified version"; a good prompt clearly defines the role, objective, processing logic, output format, and even constraints (such as prohibiting certain actions or maintaining a specific tone).

The purpose of a Prompt is to define the Agent's behavioral boundaries, response style, and operating constraints.

3. Memory: The Memory System

As conversations grow longer and tasks become more complex, an Agent can lose track of context — which is why Memory is needed to retain conversation history and task progress.

What you might not realize is that not all information is worth remembering. Storing too much can actually interfere with decision-making. Learning to "select and forget" is just as important as remembering for an Agent to operate efficiently.

4. Tools: The Toolkit

Tools are the key leap that takes AI from "knowing how to chat" to "knowing how to work."

AI can't perceive the external environment on its own — Tools are what give it eyes and hands. It can read PDFs, manipulate Excel files, create PowerPoint presentations… Give it access to a computer and, in theory, it can handle any task you could do yourself. Much of the dramatic improvement in AI capability comes down to tool-calling ability.

Combine LLM, Prompt, Memory, and Tools — and you have a complete Agent.

Closing Thoughts: B2B Is Here, B2C Is Coming

By integrating diverse capabilities, an Agent ultimately gains the ability to "pursue a goal and complete work autonomously." This is exactly why the analogy of "LLM as brain, Agent as employee" holds up so well.

Looking at industry trends, the current AI wave is primarily making waves in the B2B (enterprise) space, where many companies are highly interested in this low-cost digital workforce. The B2C (consumer) transformation has yet to fully arrive — which means Agent applications aimed at everyday users may represent the next major opportunity.

For most people, understanding the building blocks of an Agent — brain (LLM), instructions (Prompt), memory (Memory), and tools (Tools) — is the first step to keeping pace with this AI wave.