AI Agent: An Easy AI Tutorial

*Source: Easy AI learning platform. Tutorial created for general AI education.*

What Is an AI Agent?

An AI Agent is not just an "answer machine" — it is an "intelligent system that gets things done."

Moving from traditional AI's one-shot output to an Agent's "plan → execute → reflect" closed loop, AI Agents simulate human thinking processes and autonomously complete complex tasks.

Core Capabilities of an Agent

1. Planning

Automatically decompose complex tasks into executable sub-steps with an execution plan.

Example: creating a quarterly marketing plan

• Receive task: user requests a Q4 marketing plan
• Task decomposition: market research → goal setting → strategy design → budget planning
• Build a timeline: assign time and resources to each sub-task
• Identify dependencies: determine prerequisites between steps

2. Memory

Store and manage historical information — conversations, data, and experience — supporting contextual understanding.

Example: customer email organization project

• Store interactions: record all user conversations and operation history
• Data management: save email classification rules and results
• Experience learning: learn user preferences from past operations
• Smart retrieval: reuse existing data next time

3. Tools

Call external capabilities and APIs to break through the limits of language models.

Example: complex data analysis

• Identify need: detect that 12345 × 67890 must be computed
• Select tool: automatically invoke a calculator tool
• Execute: obtain the precise result: 837405300
• Integrate: incorporate the result into the answer

4. Action

Execute concrete operations such as sending emails, generating documents, or controlling devices.

Example: automated office workflow

• Prepare execution based on the plan
• Send email: automatically draft and send meeting invitations
• Generate PPT: create presentations with data charts
• Report back to the user on task completion

Agent System Architecture

Input Perception Layer

• Multimodal perception — handles images, audio, and other input modalities

Core Processing Layer

• Large language model — the core thinking engine for understanding, reasoning, and generation
• Scheduling/orchestration system — coordinates components and decides when to call which tools
• Memory & learning — stores dialogue history, learned experience, and knowledge graphs

Execution Output Layer

• Tool calling — performs operations such as computation, search, and API calls
• Execution engine — turns decisions into concrete actions

Four Typical Agent Patterns

Based on Andrew Ng's classification:

1. Reflection Agent

Like a "programmer + reviewer" combo, with self-checking and iterative improvement:
• Dual roles: generator + reviewer
• Iterative optimization and multi-round verification
• Best for high-quality content generation

Workflow: initial generation → self-review → feedback improvements → iterative refinement

2. Tool Use Agent

A rich external toolbox that extends pure language models:
• Integrates external tools, retrieves real-time data, supports complex computation
• Example: a shopping assistant that calls a coupon-finder tool

3. Planning Agent

Handles complex multi-step tasks by chaining tools and models:
• Multi-step decomposition, model collaboration, seamless tool switching, end-to-end solutions
• Example: dance tutorial generation chaining Openpose, Vision, GPT, and FastSpeech

4. Multi-Agent Collaboration

Simulates human team division of labor:
• Specialized agent teams, inter-agent communication, full project management, 1+1>2 team effects
• Example: software development project (CEO → CTO → programmer → tester)

Traditional AI vs. Agent

Traditional AI flow: receive prompt → generate result in one shot → done.

Agent flow: receive task → plan (outline) → act (search the web) → execute (write draft) → reflect (self-check and revise) → optimize (iterate).

Traditional AI is like a "one-shot writer": it produces final output directly from a prompt with no ability to adjust its approach or fetch external information.

An AI Agent has human-like thinking: it plans tasks, calls tools, reflects on itself, and continuously improves results through a closed-loop cycle.

The Simplest Agent Implementation

Even a simple combination constitutes a complete AI Agent:

1. Prompt template — "Please translate the following text into English: {text}" 2. LLM + trigger — user input is auto-concatenated for one-click translation

This is the essence of an agent: brain + tools + instructions.

--- *Source: Easy AI learning platform. This tutorial was created for AI knowledge popularization.*