Easy AI Tutorial: A Beginner's Guide to AI Agents

Forum topic · 小凯 · 2026-03-27

Summary

This tutorial from the Easy AI learning platform explains what AI Agents are and how they differ from traditional AI systems. It covers the four core capabilities of an agent: planning (decomposing complex tasks into subtasks), memory (storing conversation history and learned preferences), tool use (calling external tools and APIs, such as a calculator for 12345×67890 = 837405300), and action (executing operations like sending emails or generating presentations). The article outlines a typical agent system architecture with an input perception layer, a core processing layer (LLM, orchestration, memory), and an execution/output layer. Following Andrew Ng's classification, it describes four agent patterns: Reflection, Tool Use, Planning, and Multi-Agent Collaboration, each with practical examples. It contrasts traditional AI's one-shot output with the agent's plan-act-reflect loop, and shows that even a simple prompt template plus an LLM constitutes a minimal agent.

AI Agent Tutorial

What is an AI Agent?

An AI Agent is not just a "question-answering machine" — it is an intelligent system that *gets things done*.

Moving from traditional AI's one-time output to an agent's closed loop of "planning → execution → reflection", agents simulate human thought processes to autonomously complete complex tasks.

Core Capabilities of an Agent

1. Planning

Automatically decompose complex tasks into executable sub-steps.

Example: creating a quarterly marketing plan

Receive the task: user requests a Q4 marketing plan
Task decomposition: market research → goal setting → strategy design → budget planning
Timeline: allocate time and resources to each subtask
Dependency identification: determine prerequisites between steps

2. Memory

Store and manage historical information

Example: customer email organization project

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

3. Tools

Call external tools and APIs

Example: complex data analysis

Detect need: compute 12345×67890
Select tool: automatically invoke a calculator tool
Execute: get the exact result: 837405300
Integrate: incorporate the result into the answer

4. Action

Perform concrete operations

Example: automated office workflow

Prepare execution based on the plan
Send meeting invitation emails automatically
Generate presentations with data charts
Report task completion to the user

Agent System Architecture

Input perception layer: multimodal sensing of images, audio, and other input types
Core processing layer:
Large language model — the core thinking engine for understanding, reasoning, and generation
Orchestration system — coordinates components and decides when to call which tools
Memory & learning — stores conversation history, experience, and knowledge graphs
Execution/output layer: tool calling (compute, search, API calls) and an execution engine that turns decisions into actions

Four Typical Agent Patterns

Based on Andrew Ng's classification:

1. Reflection Agent

Dual roles: generator + reviewer
Iterative optimization and multi-round quality checks
Workflow: initial generation → self-review → feedback → iterate

2. Tool Use Agent

Calls external tools, accesses real-time data, supports complex computation
Example: a shopping assistant that invokes a coupon-finding tool

3. Planning Agent

Multi-step decomposition, model collaboration, seamless tool switching
Example: dance tutorial generation chaining Openpose, Vision, GPT, FastSpeech

4. Multi-Agent Collaboration

Specialized agent roles, inter-agent communication, full project management
Example: software development project (CEO → CTO → programmer → tester)

Traditional AI vs Agent

Traditional AI: receive prompt → generate result once → done
Agent: receive task → plan outline → search online for materials → write draft → self-review and revise → refine repeatedly

A traditional AI is like a "one-shot writer" that cannot adjust its approach mid-task. An AI Agent plans, calls tools, and reflects, continuously improving results through a closed loop.

The Simplest Agent Implementation

Even a simple combination forms a complete AI Agent:

1. Prompt template — "Please translate the following text into English: {text}" 2. LLM + trigger — user input is automatically concatenated, 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.