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AI Gets Dumber the Longer You Chat — Here's the Real Reason

Why does your AI get dumber the longer you chat? From Token to Agent, thoroughly understand the secrets of AI Agents ㊙️

I believe many developers have had similar experiences.

When you first start using AI:

Help me analyze this project architecture.

The AI's response is very clear.

But after chatting for a while:

After dozens of rounds, the AI starts to:

At this point, many people's first reaction is usually:

Has the model become dumber?

But often, the problem is not a decline in model capability.

It's that:

Your context has pushed the AI to its limit.

To truly understand this phenomenon, we need to start from the most fundamental operating mechanism of AI.

Step one: First, understand Token.

Chapter 1: Token -- The Basic Unit in the AI World

1. Does AI really understand text?

Many people think AI reads text like a human.

In reality, AI does not directly understand:

"Help me write a login interface."

What it sees is:

Human Language
↓
Tokenizer
↓
Token Sequence
↓
Numeric ID
↓
Neural Network Computation
↓
Generate New Tokens
↓
Convert to Human Language

Token is the bridge connecting human language and the AI world.

2. What exactly is a Token?

A Token can be understood as:

The smallest unit of information when AI processes text.

It is not equal to:

It is a segmentation method defined by the model based on its training data.

English Example

Input:

I love programming

The Tokenizer might segment it as:

I
love
program
ming

Then map it to numbers:

I       → 1045
love    → 5321
program → 8821
ming    → 2938

Ultimately, the model sees:

[
1045,
5321,
8821,
2938
]

AI truly processes numbers, not English.

3. Why does Chinese also need Tokens?

For example:

人工智能正在改变软件开发

Might be segmented as:

人工
智能
正在
改变
软件
开发

Or possibly:

人工智能
正在改变
软件开发

Different models have different Tokenizers, so the number of Tokens can also differ.

4. What exactly happens from text to numbers?

For example:

帮我写一个登录接口

After the Tokenizer:

帮我
写
一个
登录
接口

Then mapping:

帮我 → 12341
写   → 88291
登录 → 53221
接口 → 99281

The model ultimately receives:

[
12341,
88291,
53221,
99281
]

Complete process:

Text
↓
Token
↓
Numeric ID
↓
Neural Network Computation

This is the foundation of how AI understands language. After understanding what a Token is, let's return to context.

Chapter 2: Why does AI get "dumber" the longer the context?

1. What is a Context Window?

Context Window:

It is:

The total amount of information an AI can see at one time.

For example:

128K Context

Represents that it can process a maximum of about 128,000 Tokens at a time.

Note:

The key here:

At one time.

2. AI doesn't really remember the chat history

Many people think:

AI remembers all previous chats.

In reality:

With every request, the application re-sends the historical messages to the model.

For example:

First time:

System Prompt

+

User Question

Second time:

System Prompt

+

First Chat

+

AI Answer

+

Second Question

Third time:

System Prompt

+

All Previous Content

+

Current Question

3. Context Growth Process

New Conversation

┌───────────────┐
│ System Prompt │
├───────────────┤
│ User Question │
│ 100 Token     │
└───────────────┘

Multi-turn Conversation

┌───────────────┐
│ System Prompt │
├───────────────┤
│ Round 1       │
├───────────────┤
│ Round 2       │
├───────────────┤
│ Round 3       │
├───────────────┤
│ Current Q     │
└───────────────┘

Long-term Project Development

┌────────────────────┐
│ System Prompt      │
├────────────────────┤
│ Project Rules      │
├────────────────────┤
│ Round 1 Req        │
├────────────────────┤
│ Round 2 Design     │
├────────────────────┤
│ Lots of Code Talk  │
├────────────────────┤
│ Current Question   │
└────────────────────┘

At this point:

A single request might carry:

50000+
Tokens

4. Why does longer context lead to worse results?

First: Attention Dilution

AI needs to find key points from a large amount of information.

The more information, the harder it is to find the correct content.

Second: Old Information Pollution

For example:

Earlier:

Don't modify the database structure

Later:

You can adjust the database

Two rules exist simultaneously.

AI needs to judge which one is valid.

Third: Increased Computational Cost

The longer the context:

Summary:

AI isn't afraid of too little information, but of messy information.

Chapter 3: How does AI actually work?

We spent a lot of time earlier understanding Token and Context.

Because this is the foundation for understanding AI.

If you don't know how AI processes information, then the later concepts:

will all become "memorizing concepts."

When many people use AI tools, they actually only stay at the application layer.

For example:

Open ChatGPT.

Open Cursor.

Type a sentence.

Then wait for the result.

But after truly understanding AI, you will find:

Behind these tools is actually a whole layered system.

I. The Four-Layer Structure of an AI System

A complete AI application can roughly be broken down into:

Application Layer

↓

Agent / Workflow Orchestration Layer

↓

Model Service Layer

↓

Raw Model Layer

1. Raw Model: The Actual Large Model

The Raw Model is the bottom layer.

For example:

It is responsible for:

Simple understanding:

The Raw Model is like a super-smart brain.

But it has one problem:

It knows nothing.

It doesn't know:

It can only generate answers based on the input information.

2. Model Service: The Model Service Layer

The Raw Model needs a service layer outside it.

Its function is similar to:

Wrapping the model's capabilities into a callable service.

For example:

Your Application

↓

API Request

↓

Model Service

↓

Large Model

This layer is responsible for:

For instance:

You call the AI:

Please analyze this code

Behind the scenes, it doesn't connect directly to the model.

Instead:

The application sends a request.

The service layer processes it.

Then calls the model.

3. Application: The Application Layer

The software we usually use:

For example:

all belong to the application layer.

The greatest value of the application layer:

is not providing the model.

But:

Adding capabilities to the model.

For example:

Raw Model:

User Question

↓

Model

↓

Answer

Application Layer:

User Question

↓

Prompt Enhancement

↓

Context Management

↓

Tool Calling

↓

Model

↓

Result Processing

This is why:

The same model.

Different products have completely different experiences.

II. What exactly happens during an AI request?

When a user inputs:

Help me analyze this project

It looks very simple.

But a series of processes happen behind the scenes.

Complete flow:

User

↓

Application

↓

Assemble Context

↓

Add Prompt

↓

Add Tools

↓

Tokenization

↓

Send to Model

↓

Model Inference

↓

Generate Tokens

↓

Return Result

↓

Display to User

III. Prompt: Telling AI How It Should Work

Many people understand Prompt as:

Just a sentence.

For example:

Help me write a login interface

But in reality:

A truly effective Prompt is not just a question.

It includes:

An Ordinary Prompt

Help me optimize the code

Problem:

AI doesn't know:

An Engineering-Level Prompt

You are a senior backend engineer.

Please analyze the current order module.

Goals:
1. Improve query performance
2. Maintain interface compatibility
3. Do not modify the database structure

Output an analysis plan before execution.

The difference:

It's not about making the AI smarter.

But:

Making the AI clearer.

IV. Tool: Giving AI Hands and Feet

If we say:

The large model is the brain.

Then Tools are the hands and feet.

Without Tools:

AI can only answer.

With Tools:

AI can act.

For example:

User:

Help me analyze the project structure

Ordinary Model:

Please copy the project code to me.

Agent:

Can:

Call read tool

↓

Read directory

↓

Analyze code

↓

Generate report

V. The Core Mechanism of a Coding Agent

The essence of a Coding Agent:

is:

Model + Tools + Loop Control.

Structure:

User

↓

Agent

↓

Model

↓

Tool

↓

Execution Environment

↓

Result Returned to Model

For example:

User:

Summarize this project

Agent sends:

System Prompt:

You are a development Agent.

Current path:

/project

Available tools:

- read
- write
- command

The model judges:

It needs to view files.

Returns:

{
  "tool": "command",
  "action": "ls"
}

Agent executes:

ls

Gets:

src
package.json
README.md

Then continues to send to the model.

This process loops continuously.

Until the model believes:

The task is complete.

VI. Skill: Solving the Context Explosion Problem

As AI usage deepens, a problem emerges:

More and more rules.

For example:

Project specifications:

Code standards
Database standards
Git standards
Interface standards
Component standards
Testing standards

If all are stuffed into the Prompt:

The context will rapidly expand.

Thus, Skill emerges.

The core idea of Skill:

Don't load all knowledge upfront, but load it when needed.

Before:

Start AI

↓

Load all rules

↓

Consume a lot of Tokens

Now:

Start AI

↓

Tell AI what skills are available

↓

Load detailed content when needed

Similar to:

Human work:

You don't memorize all company policies every day.

Check the documentation when needed.

VII. MCP: The Standard Way for AI to Connect to the External World

The problem MCP (Model Context Protocol) solves:

Before:

Every AI tool needed a separately developed connection method.

For example:

Connecting to:

Each required re-development.

MCP provides a unified protocol.

Simple understanding:

MCP is a set of Tool standards.

Structure:

AI Agent

↓

MCP Client

↓

MCP Server

↓

External System

For example:

User:

Query the person with the most Git commits yesterday

AI:

Calls Git MCP.

MCP:

Accesses Git service.

Returns:

Zhang San:
45 commits

Li Si:
32 commits

AI then generates natural language.

VIII. How to use AI correctly?

After understanding all the principles, you will find:

Tools are always changing.

But methods do not change.

The core is always:

1. Give AI a clear goal

Don't:

Help me write code

Should:

Implement what function, solve what problem, with what constraints.

2. Control context quality

Don't:

Chat in one session for months.

Should:

One task, one session.

3. Let AI work in stages

Don't:

Directly develop the entire system

Should:

Analyze requirements

↓

Design solution

↓

Implement

↓

Test

↓

Optimize

IX. The Core Competency of Future AI Users

In the future, it won't be about who:

Types the fastest.

But who:

Knows better how to orchestrate AI.

Truly advanced AI usage:

is not:

"Let AI write code for me."

But:

"Design a system that allows AI to stably complete complex tasks."

Chapter 4: The True Principle of Coding Agents ------ Why can AI work like a programmer?

Previously we discussed:

But there is still a key question:

Why can current AI tools, such as:

directly operate on code?

How exactly do they manage to:

These things?

Answer: Agent

I. What is an Agent?

Many people understand Agent as:

"A stronger chatbot."

Actually, that's not accurate.

Chatbot:

User

↓

Model

↓

Answer

Agent:

User

↓

Agent

↓

Model

↓

Tool

↓

Real Environment

↓

Feedback

↓

Continue Reasoning

The biggest difference:

A chatbot can only answer.

An Agent can act.

II. The Core Idea of Agent: Giving the Model the Ability to Act

The large model itself:

is very smart.

But it lacks:

It can only generate text.

For example:

User:

Analyze the current project structure

Ordinary Model:

Please send me the code so I can analyze it.

Because it doesn't know your project.

But an Agent:

Read directory

↓

View files

↓

Analyze code

↓

Output report

This is the difference.

III. Core Components of an Agent

A Coding Agent typically includes:

Agent

├── Prompt
├── Context Management
├── Tools
├── Memory
├── Planning
└── Model Invocation

1. Prompt

Tells the model:

Who you are.

What you can do.

What rules you should follow.

For example:

You are a software development Agent.

Current project path:

/workspace/project

You can use:

read
write
command

Please analyze first, then execute modifications.

2. Tools

Tools are the key to an Agent's ability to act.

Common ones:

command

read

write

delete

Corresponding to:

command:

Execute commands.

For example:

npm test
git status
ls

read:

Read files.

For example:

src/user/UserService.java

write:

Modify or create files.

delete:

Delete files.

IV. Tool Calling: How does the model call tools?

This is the most core part of an Agent.

Many people think:

The model directly executes code.

It doesn't.

In reality:

The model is only responsible for:

Generating a tool call request.

For example:

User:

Analyze project structure

The model judges:

I need to view the directory.

So it generates:

{
  "tool": "command",
  "arguments": {
    "command": "ls"
  }
}

Note:

The model didn't execute.

It just said:

"I need to call this tool."

Then:

The Agent receives this JSON.

Executes:

ls

Gets:

src

package.json

README.md

Then:

The Agent sends the result back to the model.

The model continues to judge:

The next step needs:

Read package.json.

So:

Calls the tool again.

This process:

Loops continuously.

V. Agent Loop: Why can an agent complete complex tasks?

The core loop of an Agent:

is called:

Agent Loop.

Usually:

Observe

↓

Think

↓

Act

↓

Feedback

↓

Continue Loop

For example:

Task:

Help me add a user login feature

Agent:

Step 1: Observe

View project structure.

Call:

command ls

Step 2: Understand

Read:

UserController

UserService

Database

Step 3: Plan

Decide:

Need to add:

Step 4: Act

Modify code.

Step 5: Verify

Execute:

npm test

Find errors.

Step 6: Fix

Continue modifying.

Until:

Task complete.

This is also why current Coding Agents are so impressive.

Because it's not:

Generating code in one go.

But:

Continuously observing the environment, then adjusting behavior.

Chapter 5: Sub Agent ------ One AI becomes a team

For complex tasks in the future:

One Agent is not enough.

Thus emerges:

Sub Agent.

One Main Agent:

Responsible for:

Task management.

Below it:

Multiple Sub Agents.

For example:

Main Agent

├── Planner Agent
│
├── Coding Agent
│
├── Test Agent
│
└── Review Agent

Planner Agent

Responsible for:

Analyzing requirements.

Breaking down tasks.

Coding Agent

Responsible for:

Writing code.

Test Agent

Responsible for:

Testing.

Review Agent

Responsible for:

Code review.

This is like:

A software team.

The difference:

Before:

A project needed:

5 people.

Future:

Possibly:

1 person managing 5 AI Agents.

Chapter 6: Vibe Coding to Spec Coding

When AI programming first started:

Many people liked:

Vibe Coding.

Chinese:

氛围编程.

Simple understanding:

Say whatever comes to mind.

Let AI generate for you.

For example:

Help me make an e-commerce website

AI:

Starts writing directly.

Very satisfying.

But the problems are also obvious:

So future enterprises will definitely move towards:

Spec Coding.

That is:

Specification-driven development.

Process:

Requirements

↓

Spec Design

↓

AI Implementation

↓

Automated Testing

↓

Review

AI is responsible for execution.

Humans are responsible for defining rules.

Summary:

The greatest value of AI:

is not generating code for you.

But:

Allowing one developer to have a development team.

But the premise is:

You need to understand:

Because:

People who don't understand the principles:

Can only use AI.

People who understand the principles:

Can command AI.

The greatest competitiveness of future developers:

is not:

"How fast I can write code."

But:

"Can I design a system that allows AI to stably complete complex software engineering."

Chapter 7: How to truly use AI tools to improve development efficiency?

We spent a lot of time earlier talking about:

This content seems quite low-level.

Many people might think:

"What's the use of knowing this?"

Actually, it's very useful.

Because after understanding the underlying principles, you will find:

The ability to use AI tools is essentially not about whether you can input a Prompt.

But:

Whether you have the ability to design a human-AI collaboration workflow.

I. Why do many people's efficiency actually drop after using AI?

This is a very interesting question.

Many developers first encounter AI:

Very excited.

Feeling:

"Will I not need to write code in the future?"

But after using it for a while, they find:

Efficiency hasn't significantly improved.

Even:

Spending more time fixing the garbage code AI generates.

Why?

Because many people just treat AI as a search engine.

1. Treating AI as a code generator

For example:

User:

Help me write an order system

AI:

Generates hundreds of lines of code.

Then:

The developer starts modifying.

Finally finds:

Might as well have written it myself.

Where is the problem?

It's not that AI is incapable.

But the way the task is described is wrong.

Complex software development was never about:

Generating from a single sentence.

But:

Requirement analysis.

Architecture design.

Module breakdown.

Coding.

Testing.

Optimization.

AI is the same.

2. Not giving AI enough context

Another common problem:

User:

Help me optimize this method

Then sends:

public void test(){

}

AI:

Can only guess.

It doesn't know:

So:

The output is naturally unstable.

3. Stuffing too much content into one session

Many people like:

Using one chat window for months.

Inside it contains:

Finally:

The context becomes more and more chaotic.

The correct way:

One task.

One session.

II. Core Principles for Using AI Correctly

Principle 1: Let AI think first, then let AI execute

Many people:

Directly:

Help me modify the code

A better way:

Step 1:

First analyze the problems with the current code.
Don't modify.
Output a plan.

Step 2:

Confirm the plan.

Step 3:

Execute modifications according to the plan.

This way, stability will improve significantly.

Principle 2: Let AI complete tasks in stages

Don't:

Help me develop a complete e-commerce system

Should:

Break it down:

Phase 1:

Design database model.


Phase 2:

Implement user module.


Phase 3:

Implement order module.


Phase 4:

Add payment.


Phase 5:

Test and optimize.

AI is best at:

Clear, small tasks.

Principle 3: Set boundaries for AI

AI's biggest problem:

is not inability.

But not knowing the limits.

For example:

Wrong:

Optimize this system.

Correct:

Optimize query performance.

Constraints:

1. Do not modify database structure.
2. Do not change interfaces.
3. Maintain code compatibility.
4. Output modification plan before execution.

The clearer the constraints:

The more stable the result.

III. Recommended AI Programming Workflow

A relatively mature AI development process:

Requirements

↓

Analysis

↓

Design

↓

Execution

↓

Verification

↓

Optimization

Step 1: Requirement Analysis

Don't write code directly.

First, let AI understand:

For example:

Analyze this requirement:

Add a membership system.

Please output:

1. Involved modules.
2. Data changes.
3. Potential risks.
4. Recommended implementation plan.

Step 2: Generate Technical Plan

Let AI act as an architect.

For example:

Based on the current project structure.

Design the implementation plan for the membership system.

Do not modify code.

Only output the plan.

Get:

Step 3: Execute Development

After confirming the plan:

Let AI modify.

For example:

Execute according to the confirmed plan.

Requirements:

1. Maintain existing code style.
2. Do not modify unrelated files.
3. Explain the reason for each modification.

Step 4: Verification

Don't trust AI.

Must verify.

For example:

Let AI:

Run tests.

Check:

1. Compilation errors.
2. Unit tests.
3. Potential issues.

Step 5: Review

Finally:

Let AI self-check.

For example:

Now act as a code reviewer.

Check the modifications just made.

Focus on:

Performance, security, maintainability.

IV. What does an enterprise-level AI workflow look like?

In the future, enterprises truly using AI:

Won't just be:

Employees chatting.

But:

AI connecting to enterprise systems.

For example:

Boss asks:

Analyze the risk of recent project delays.

Traditional way:

Manual:

Check Jira.

Check Git.

Check meeting notes.

Several hours.

AI Workflow:

User

↓

AI Agent

↓

Jira MCP

↓

Git MCP

↓

Database MCP

↓

Analysis Model

↓

Generate Report

Output:

Project A:

High delay risk.

Reasons:

1. Backend interface delayed by 5 days.
2. Bug count increased by 30%.
3. Insufficient testing resources.


Suggestions:

Increase testers.
Adjust release date.

This is where AI truly changes enterprise efficiency.

V. The Future Software Development Model

The future development process might become:

Product Manager

↓

Requirement Spec

↓

AI Planner

↓

AI Developer

↓

AI Tester

↓

AI Reviewer

↓

Human Final Confirmation

Humans are responsible for:

AI is responsible for:

VI. Developer Competency Model in the AI Era

Before:

Excellent programmer:

Strong coding ability

↓

Writes code fast

↓

Familiar with frameworks

Future:

Excellent developer:

Understands business

+

System design ability

+

AI orchestration ability

+

Engineering management ability

Coding ability is still important.

But:

Code is just a means of expression.

What's truly important is:

The ability to solve problems.

Final Summary: In the AI era, what is the greatest competitiveness?

AI won't simply replace programmers.

But:

Programmers who can use AI,

will gradually replace programmers who cannot use AI.

In the future:

An excellent developer might no longer be:

"The person who writes the most code every day."

But:

"The person who can design a system that allows multiple AI Agents to collaborate efficiently."

Truly mastering AI:

is not learning a few tools.

is not memorizing a few Prompts.

But understanding:

Why AI works this way.

When you understand:

Token.

Context.

Model.

Agent.

Tool.

MCP.

You are no longer just using AI.

You are building your own AI workflow.

This is the ability developers should truly master in the AI era. This era will definitely belong to technical people with ideas.