AI Agent

#AI Agent

Overview

AI Agent (artificial intelligence agent) is an AI system that can ** autonomously perceive the environment, make decisions and execute actions**. Unlike traditional chat-based AI, Agent can:

Actively call tools
Plan multi-step tasks
Handle complex workflows
Automatically retry on failure

Core Features: Autonomy, interactivity, reactivity, initiative

Core concepts

1. Definition of Agent

Agent is able to:

Perceive (Perceive): Obtain environmental information
Reason (Reason): Analyze the situation and make a plan
Action (Act): Perform specific actions
Learn (Learn): Improve from feedback

2. The difference between Agent and Chatbot

Features	Chatbot	AI Agent
Interactive	Question-and-answer	Task-oriented
Proactive	Reactive response	Proactive planning
Tool Usage	Limited	Rich
Task Complexity	Single-step task	Multi-step task
Memory	Session level	Long-term memory
Autonomous decision-making	None	Yes

Agent architecture

Basic architecture

┌─────────────────────────────────────────────────────────┐
│AI Agent Architecture │
├─────────────────────────────────────────────────────────┤
│                                                         │
│  ┌─────────┐     ┌─────────┐     ┌─────────┐          │
│ │ Input │ ──▶│ Inference │ ──▶│ Output │ │
│  │ Input   │     │ Reasoning│     │ Output  │          │
│  └─────────┘     └────┬────┘     └─────────┘          │
│                       │                                 │
│                       ▼                                 │
│              ┌─────────────┐                           │
│ │ Tool call │ │
│              │   Tools     │                           │
│              └─────────────┘                           │
│                       │                                 │
│                       ▼                                 │
│              ┌─────────────┐                           │
│ │ Environmental feedback │ │
│              │  Feedback   │                           │
│              └─────────────┘                           │
│                                                         │
└─────────────────────────────────────────────────────────┘

Core components

1. Planning module (Planning)

Task breakdown
Step ordering
Resource allocation
Time estimate

2. Memory module (Memory)

Short-term memory (current session)
Long-term memory (vector storage) -Context management
Knowledge retrieval

3. Tools module

File operations
API calls
command execution
Database query

4. Reflection module (Reflection)

Result verification
error handling
Strategy adjustments
Retry mechanism

Type of Agent

1. Single Agent system

All tasks are completed by one Agent:

┌─────────────────┐
│    Agent       │
│  ┌───────────┐  │
│  │ Planning  │  │
│  │ Memory    │  │
│  │ Tools     │  │
│  │ Action    │  │
│  └───────────┘  │
└─────────────────┘

Features:

Simple to implement
Suitable for single domain tasks
Easy to debug

2. Multi-Agent system

Multiple Agents work together:

┌─────────────────────────────────────────────────────────┐
│                   Multi-Agent System                    │
├─────────────────────────────────────────────────────────┤
│                                                         │
│  ┌──────────┐    ┌──────────┐    ┌──────────┐         │
│  │  Planner │ ──▶│  Coder   │ ──▶│  Tester  │         │
│  │  Agent   │    │  Agent   │    │  Agent   │         │
│  └──────────┘    └──────────┘    └──────────┘         │
│       │               │               │                │
│       └───────────────┴───────────────┘                │
│                       ▼                                 │
│              ┌──────────────┐                           │
│              │ Coordinator  │                           │
│              │    Agent     │                           │
│              └──────────────┘                           │
│                                                         │
└─────────────────────────────────────────────────────────┘

Features:

Specialized division of labor
Can be processed in parallel
Suitable for complex tasks

Common Roles:

Agent role	Responsibilities
Planner	Task planning and decomposition
Coder	Code generation, modification
Reviewer	Code review, verification
Tester	Test case generation
Debugger	Problem location and repair
Docuenter	Document generation

3. Hierarchical Agent system

There is a superior-subordinate relationship between Agents:

┌─────────────────────────────────────────────────────────┐
│              Hierarchical Agent System                  │
├─────────────────────────────────────────────────────────┤
│                                                         │
│  ┌───────────────────────────────────────┐             │
│  │        Manager Agent (L1)             │             │
│ │ Task allocation, progress monitoring, coordination │ │
│  └─────────────┬─────────────────────────┘             │
│                │                                         │
│     ┌──────────┼──────────┐                             │
│     ▼          ▼          ▼                             │
│  ┌──────┐ ┌──────┐ ┌──────┐                           │
│  │Coder ││Tester││Doc   │ (L2)                         │
│  │Agent ││Agent ││Agent │                              │
│  └──────┘ └──────┘ └──────┘                           │
│                                                         │
└─────────────────────────────────────────────────────────┘

Mainstream Agent framework

1. LangChain Agent

from langchain.agents import create_openai_functions_agent
from langchain.tools import Tool

#define tools
tools = [
    Tool(
        name="calculator",
        func=lambda x: eval(x),
description="Perform mathematical calculations"
    )
]

#Create Agent
agent = create_openai_functions_agent(
    llm=chat_model,
    tools=tools,
    prompt=prompt
)

2. AutoGen

from autogen import AssistantAgent, UserProxyAgent

#Create Agent
assistant = AssistantAgent(
    name="assistant",
    llm_config={"model": "gpt-4"}
)

user_proxy = UserProxyAgent(
    name="user_proxy",
    code_execution_config={"work_dir": "coding"}
)

# Start conversation
user_proxy.initiate_chat(
    assistant,
message="Calculate the 10th term of the Fibonacci Sequence"
)

3. Claude Code Task Agent

// Use Claude Code's subagent
import { Task } from '@anthropic-ai/claude-code';

const result = await Task({
description: "Analyze project code structure",
  subagentType: "explore",
  model: "claude-opus-4-5"
});

4. Cursor Composer

Cursor’s Agent system:

Composer: multi-step task execution
Background Agent: background processing
Multi-Agent Interface: Parallel multi-agent

Agent design pattern

1. ReAct (Reasoning + Acting)

Thought → Action → Observation → Thought → ...

Example:

Thought: I need to read the file content
Action: ReadFile(path="src/main.js")
Observation: 100 lines of code read
Thought: I see the problem
Action: EditFile(...)

2. Chain of Thought

Reason step by step and show the thinking process:

Issue: User reports login failure

Thinking steps:
1. Check the authentication logic
2. View log files
3. Verify API configuration
4. Locate the cause of the problem

3. Plan-and-Execute

Plan first, then execute:

1. Planning stage:
- Analyze needs
- Make a plan
- Break down tasks

2. Execution phase:
- Follow the steps
- Record progress
- handle exceptions

4. Self-Refine

Self-reflection and improvement:

Generate initial plan
Self-censorship
Identify the problem
Improvement plan
Repeat 2-4

Best Practices for Agents

1. Clear goals

Clearly define task boundaries
Set success criteria
Clarify the output format

2. Tool design

Tool function is single
Clear input and output
Improved error handling

3. Memory management

Set up the context window appropriately
Use vectors to store long-term memory
Regularly clean up irrelevant information

4. Security considerations

Limit the actions that can be performed
Implement permission control
Record all actions

5. Observability

Document the decision-making process
Monitor execution status
Track resource usage

Agent application scenarios

Scenario	Agent Type	Description
Code Generation	Coder Agent	Generate and modify code
Code Review	Reviewer Agent	Review code quality
Automated Testing	Tester Agent	Generate test cases
Bug Fix	Debugger Agent	Locate and fix problems
Document Generation	Documenter Agent	Generate technical documentation
Data Analysis	Analyst Agent	Processing data tasks
Operation and Maintenance Automation	Ops Agent	Automated Operation and Maintenance Operations

Reference resources

paper

Framework documentation

Document updated: December 2025

Overview​

Core concepts​

1. Definition of Agent​

2. The difference between Agent and Chatbot​

Agent architecture​

Basic architecture​

Core components​

1. Planning module (Planning)​

2. Memory module (Memory)​

3. Tools module​

4. Reflection module (Reflection)​

Type of Agent​

1. Single Agent system​

2. Multi-Agent system​

3. Hierarchical Agent system​

Mainstream Agent framework​

1. LangChain Agent​

2. AutoGen​

3. Claude Code Task Agent​

4. Cursor Composer​

Agent design pattern​

1. ReAct (Reasoning + Acting)​

2. Chain of Thought​

3. Plan-and-Execute​

4. Self-Refine​

Best Practices for Agents​

1. Clear goals​

2. Tool design​

3. Memory management​

4. Security considerations​

5. Observability​

Agent application scenarios​

Reference resources​

paper​

Framework documentation​