With the rapid development of artificial intelligence (AI), AI agents are updating our lifestyle. From voice assistants in mobile phones to smart NPCs in games, these digital intelligent entities are becoming smarter and smarter. But have you ever wondered how these AI agents gain their “intelligence”? The answer lies in the training materials they use.

Just as we need high-quality teaching materials to learn knowledge, AI agents also need diverse, high-quality data to develop their capabilities. This article will provide you with a detailed introduction to the 5 key sources of information needed to build AI agents in 2025, explaining these complex concepts in simple and easy-to-understand language to help you understand the "learning materials" behind AI.

What is an AI Agent? Why is data so important?

Simply put, an AI agent is an artificial intelligence program that can autonomously perceive the environment, make decisions, and perform actions. Unlike ordinary AI models, AI agents have stronger autonomy and interactive capabilities.

Imagine an NPC character in a video game: if it can only take fixed actions, it’s regular AI; but if it can adjust its strategy in real time based on your behavior, or even learn new tricks from your interactions, it’s an AI agent.

Data is as important to AI agents as textbooks are to students. The type of training data used directly determines the upper limit of the AI agent's capabilities. Poor-quality data can cause AI to perform poorly or even engage in harmful behavior—just as learning with the wrong materials can lead to incorrect knowledge.

Structured database: AI's "textbook"

Structured data is the most basic and indispensable data type for building AI agents. It is like a well-designed library where all information is neatly stored according to strict classification standards to establish a clear data association network. This highly organized nature makes it the most reliable source of data for training AI agents.

Main data forms

The most common structured data carriers currently include:

• Relational database systems: such as MySQL, PostgreSQL, etc., which store data in table form

• Spreadsheet files: Excel, Google Sheets, and other office documents

• Knowledge graph system: Wikidata and other semantic network databases

Core Value Analysis

The core value of structured data to AI agents is reflected in:

• Provide accurate factual references: Ensure that the information obtained by AI is accurate

• Establish clear logical connections: Help AI understand the inherent connections between data

• Support reliable decision-making basis: Provide a traceable basis for AI judgment

Taking medical diagnosis AI as an example, by analyzing the correspondence between symptoms and diagnosis results in the structured medical record database, AI can learn to establish professional diagnostic logic.

Cutting-edge development trends

In 2025, the field of structured data will usher in important innovations:

• Smart dynamic database: Realize real-time automatic update of data association

• Self-evolving knowledge graph: AI systems can autonomously discover and improve relationships in knowledge networks

• Multimodal structured storage: a unified storage solution that integrates multiple data formats such as text and images

These technological advances will enable structured data to play a more powerful role in AI training, providing AI agents with a richer and more timely knowledge base.

Web crawling: AI's "extracurricular reading"

Think of the Internet as an “unlimited learning buffet” for AI! Just like you browse different websites to research a school project, AI agents browse online content to expand their knowledge.

What's on the menu?

• News Articles (Daily Specials)

• Social media posts (e.g., hot restaurant gossip)

• Product List (Digital Shopping Mall)

Real World Examples

Customer service AI studies how people complain on Twitter — it’s like learning slang from the cool kids so they can talk like a real person!

Sensor data: AI's "five senses experience"

Sensor data generated by Internet of Things (IoT) devices allows AI agents to gain “sensory experience”.

How AI experiences the world

Just as humans use their five senses to perceive their surroundings, AI agents rely on sensor data to “feel” the physical world. These electronic senses help intelligent machines interact with the real world in amazing ways!

AI’s digital perception includes:

• Electronic Eyes - Camera signals allow AI to identify objects and people

• Digital Ear - Microphone, captures sound and voice

• Environmental Sensors - Sensors that measure temperature, humidity, etc.

Real-world superpowers:

• Home robot uses camera vision to avoid stepping on your dog

• Smart Farms Analyze Soil Sensors to Grow Healthier Crops

• Security system combines motion and sound detection to identify intruders

Examples of real-world interactive materials:

• Customer Service Chat (personal information removed)

• Decision-making patterns of video game players

• How people ask questions to smart assistants like Siri or Alexa

Why this matters for AI:

By studying thousands of human interactions, AI agents can:

• Understanding Natural Conversation Flow

• Recognize the different ways people express their needs

• Develop an appropriate response strategy

Analogy: AI's "digital training ground"

Imagine being able to practice being a doctor on a robot patient before treating a real person — that’s what simulated data can do for AI! When real-world data is too expensive, scarce, or dangerous to collect, scientists create digital playgrounds for AI to train on.

Constructing the AI Matrix:

• Video game technology: Using engines like Unreal Engine to build hyper-realistic digital cities (perfect for self-driving car AI)

• Digital Twins: Creating Perfect Copies of Real-World Places and Systems

• AI vs AI: Building two neural networks to compete and improve each other (like basketball training, both sides get better)

Why this is awesome:

• Can create crazy "what if" scenarios (like practicing meteor strikes!)

• Won’t hurt anyone (great for medical AI training)

• Let the AI make millions of mistakes in a matter of seconds – without fail!

Crowdsourcing: The "collective wisdom" of AI

Human-labeled data collected through crowdsourcing platforms can significantly improve AI performance.

Common forms:

• Image annotation (such as identifying objects in images)

• Text classification (such as sentiment analysis)

• Speech Transcription

How to choose the right source of information?

• Factors to consider when choosing sources:

• Task requirements: Different AI tasks require different data types

• Data quality: accuracy, completeness, timeliness

• Acquisition cost: including money and time cost

• Compliance requirements: privacy, copyright and other legal issues

Data preprocessing: AI's "digestive system"

Raw data needs to be processed before it can be effectively used by AI:

1. Cleaning: removing errors and duplicate data

2. Annotation: Add a new description tag

3. Enhancement: Expanding the amount of data through technology

4. Standardization: Unified data format

Future Outlook: After 2025

Get ready for some exciting changes in the way AI learns! Here’s what the next generation of artificial intelligence will eat:

1. Truly useful data

AI will be trained using more computer-generated samples

These “synthetic datasets” serve as practice tests before actual training

Assist when real data is too private or difficult to obtain

2. Teamwork without shared secrets

''Federated learning'' allows AI to learn together while keeping data independent

Just like a study group, everyone can keep their notes private

Your phone gets smarter, no need to send photos to the cloud

3. Data shopping becomes more convenient

The online market for high-quality datasets will flourish

Like the App Store, but for AI training materials

It is easier to find safe and legal data for your project

4. AI that can create its own study guides

Advanced AI will generate its own exercises

Synthetic data will become incredibly realistic

Form a virtuous cycle of self-improvement

Conclusion

Data is the "new oil" in the AI era, and understanding how to obtain and use high-quality data will become one of the most important skills in the future. Hopefully, this guide has given you a clearer understanding of the data requirements of your AI agent. Who knows? Maybe you, who are reading this article, will develop an AI agent that updates the world in the future!