Easy AI Tutorial: Understanding AI Agents — Planning, Memory, Tools, and Action

Forum topic · 小凯 · 2026-03-27

Summary

This tutorial from the Easy AI learning platform introduces AI Agents as systems that go beyond one-shot AI outputs by operating in a plan–execute–reflect loop. It covers the four core capabilities of an Agent: intelligent planning (decomposing complex tasks like quarterly marketing plans into subtasks), memory (storing conversation history and learned preferences), tool use (calling calculators, APIs, and external services to extend an LLM's abilities), and action (executing real operations such as sending emails or generating presentations). The post outlines a three-layer Agent architecture — input perception, core processing (LLM plus orchestration and memory), and execution/output — and describes Andrew Ng's four Agent patterns: reflection agents, tool-use agents, planning agents, and multi-agent collaboration, with examples like a software team simulated by CEO, CTO, programmer, and tester roles. It contrasts traditional AI's single-pass generation with Agent-style iterative workflows, and shows that even a prompt template plus an LLM trigger forms a minimal Agent: brain + tools + instructions.

AI Agent Tutorial (Easy AI)

This is an English translation of a tutorial originally published on the Easy AI learning platform via zhichai.net.

What Is an AI Agent?

An AI Agent is not just an "answering machine" — it is an intelligent agent that *gets things done*.

Moving from traditional AI's one-shot output to an Agent's closed loop of planning → execution → reflection, this tutorial explores how AI can simulate human thought processes and autonomously complete complex tasks.

Core Capabilities of an Agent

1. Intelligent Planning

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

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
Create a timeline: allocate time and resources for each subtask
Identify dependencies: determine preconditions between steps

2. Memory

Store and manage historical information

Example: customer email organization project

Store interactions: record all user conversations and operation history
Data management: save classification rules and results
Learn from experience: pick up user preferences from past operations
Smart retrieval: reuse existing data next time

3. Tools

Call external tools and APIs

Example: complex data analysis

Detect the need: a calculation like 12345×67890 is required
Select a tool: automatically invoke a calculator tool
Execute: get the precise result — 837405300
Integrate: fold the result into the answer

4. Action

Execute concrete operations

Example: automated office workflow

Prepare execution based on the plan
Send an email: automatically draft and send a meeting invitation
Generate a PPT: create a presentation with data charts
Report back: inform the user of task completion status

Agent System Architecture

Input perception layer
Multimodal perception — process images, audio, and other input modalities
Core processing layer
Large language model — the core thinking engine for understanding, reasoning, and generation
Orchestration/scheduling system — coordinates components and decides when to call which tools
Memory and learning — stores conversation history, learned experience, and knowledge graphs
Execution/output layer
Tool calling — performs concrete operations such as calculations, searches, and API calls
Execution engine — converts decisions into actual actions

Four Typical Agent Patterns

Based on Andrew Ng's classification:

1. Reflection Agent

Dual roles: generator + reviewer
Iterative optimization: continuous self-improvement
Quality assurance: multi-round checking and validation
Best for: high-quality content generation

Workflow: initial generation → self-review → feedback and improvement → iterative refinement

2. Tool Use Agent

Tool integration: call various external tools
Capability extension: overcome language model limitations
Real-time data: access up-to-date information
Versatility: support complex calculations and analysis

Example: a smart shopping assistant that calls a coupon-finding tool.

3. Planning Agent

Complex decomposition: multi-step task planning
Model collaboration: chain different AI models together
Process automation: seamless tool switching
End-to-end: complete solutions

Example: a dance tutorial generator chaining Openpose, Vision, GPT, and FastSpeech.

4. Multi-Agent Collaboration

Role specialization: a team of specialized agents
Collaborative dialogue: agents communicate with each other
Project management: full development workflow
Team effect: 1+1>2

Example: a software development project (CEO → CTO → programmer → tester).

Traditional AI vs Agent

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

Agent flow: receive task → plan an outline → search the web for materials → write a first draft → reflect and self-check → refine iteratively.

Key difference: Traditional AI is like a one-shot writer that produces the final result directly, unable to adjust its approach or fetch external information mid-process. An AI Agent has a human-like thinking mode — planning tasks, calling tools, and self-reflecting in a closed loop that continuously optimizes results.

The Simplest Possible Agent

Even a minimal combination forms 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

That is the essence of an Agent: brain + tools + instructions.

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Source: Easy AI learning platform | Tutorial created for AI knowledge popularization.