From Chatbots to Colleagues: A Deep Dive into AI Agents and How They Differ from LLMs

The paradigm of Artificial Intelligence is shifting from "conversation" to "execution." While AI has been perceived as a "smart encyclopedia" that answers questions, we are now entering the era of AI Agents—entities that can think and act autonomously. Beyond merely generating text, what makes these agents so different in their ability to use tools and solve complex problems?

1. LLM vs. AI Agent: The 'Brain' vs. The 'Body'

Large Language Models (LLMs) and AI Agents are often used interchangeably, but they are better understood through the analogy of a 'Brain' and a 'Body.' If an LLM is a brain filled with vast knowledge, an AI Agent is an independent entity that uses that brain to perform tasks in the real world.

"An LLM answers your questions, but an AI Agent solves your problems."

Autonomous Reasoning While an LLM reacts to immediate prompts, an Agent defines the sub-tasks necessary to achieve a larger goal and plans the sequence of execution (Planning).

Tool Use Agents don't just rely on pre-trained data; they directly select and manipulate tools like web search, code execution, or external API calls to fetch real-time information.

Memory & Feedback Going beyond short-term context, Agents store past results and user feedback. They are capable of "self-reflection," correcting their path if they encounter a failure.

Actionable Execution Instead of just outputting text, Agents have a tangible impact on the digital environment—sending emails, registering calendar events, or operating software.

2. Clearing Up Common Misconceptions

Agent vs. Workflow

The key distinction lies in "who decides the next step."

• Workflow: A 'Deterministic' path where A leads to B, and if C happens, go to D. The designer must pre-define every possible scenario.
• Agent: A 'Dynamic' path where only the final goal is provided. The AI analyzes the situation and generates its own route, finding workarounds for unexpected variables.

Agent vs. System Prompt

The System Prompt is just the 'Instruction'
A System Prompt is a guideline telling the LLM, "You should act as a ~." In contrast, an Agent refers to the entire system that uses those guidelines to operate tools, manage memory, and repeat execution loops.

3. The 4 Core Components of an AI Agent

To function as a true "Agent" rather than just a model, the following components are essential:

• Planning: The ability to break down problems (Chain of Thought) and revise plans based on self-critique.
• Memory: The capability to store task data and retrieve it when needed (utilizing RAG and long-term storage).
• Tool Use: Interfaces that allow the model to interact with external resources like calculators, search engines, or databases.
• Profile: The specific persona or identity assigned to the agent, such as a "Code Expert" or "Marketing Analyst."

4. How to Develop an AI Agent?

There are four levels of development based on complexity and flexibility.

Lv 1. No-Code Platforms GPTs, Dify, Coze
Build agents by entering prompts and connecting APIs via a UI. The fastest way to experience agentic capabilities without coding.

Lv 2. Agent Frameworks LangChain, CrewAI
The industry standard. Ideal for assigning roles to multiple agents (Multi-Agent) and connecting complex tool chains.

Lv 3. Low-Level Control OpenAI Assistants API
Manage threads and messages directly via API. Perfect for embedding high-performance agent features directly into your own applications.

Lv 4. State-Based Optimization LangGraph
The most advanced level, allowing for precise control over states, loops, and conditional logic in complex agentic graphs.

Conclusion: From Tools to Teammates

Soon, instead of asking "What's the price of a flight to Paris tomorrow?" we will say, "Plan a trip to Paris within my budget and book the flights and accommodation for me."

Business Value: AI Agents go beyond simple automation to enable decision support and the delegation of complex workflows. This marks the birth of true "Digital Assistants" that allow humans to focus on creative tasks.

AI Agents are no longer static software; they are active partners moving towards a goal. As this technology matures, the boundary between our work and daily life will undergo another profound transformation.

"Where past software only did what it was told, future agents will simply get it done."