I Spent 1 Hour Building an AI Agent - Now It Handles 80% of My Repetitive Work | 程序员晚枫

Hello everyone, this is programmer Wan Feng actively working on various AI projects.

Recently AI Agents have been particularly hot, with related projects dominating GitHub trending daily.

Many people ask me: What exactly is an Agent? Can ordinary people use it?

Today I spent 1 hour building one—now it handles 80% of my repetitive work every day.

After reading this article, you can build one too.

What Is an AI Agent? Explained in One Sentence

Traditional AI: You ask a question, it answers.

Agent is: You give it a goal, it figures out how to complete it on its own.

For example:

Traditional AI: "What's the weather in Beijing today?" → "Sunny, 25 degrees"
Agent: "Help me plan my trip to Shanghai tomorrow" → Check weather→Check flights→Check hotels→Generate complete plan

Agent = AI brain + Tool usage ability + Autonomous decision-making

What Does My Agent Do?

The Agent I built handles daily affairs specifically:

Scenario 1: Auto-reply WeChat messages

Identify common questions ("Are you there?" "How much?")
Auto-reply based on knowledge base
Complex issues transfer to human

Scenario 2: Daily morning news generation

Auto-grab news at 8 AM
Filter areas I care about (AI, Python)
Generate summary sent to Feishu

Scenario 3: Automatic file organization

Monitor download folder
Classify by type (documents, images, installers)
Rename and move to corresponding directory

These originally took me 1-2 hours daily, now fully automated.

How to Build? Super Simple

Using OpenClaw framework, 30 lines of configuration:

# agent.yaml
name: My Smart Assistant
skills:
  - wechat-reply      # WeChat auto-reply
  - daily-news        # Daily morning news
  - file-organizer    # File organization

memory:
  type: local         # Local storage memory
  path: ./memory

llm:
  provider: openai    # Use GPT-4
  model: gpt-4

Then start:

1	openclaw run agent.yaml

That's it—Agent starts running.

Core: How to Make Agent Smarter?

The key is two things:

1. Tools

Tell the Agent what it can use:

Send WeChat messages
Read Excel files
Search web information
Call API interfaces

More tools = stronger Agent capabilities.

2. Memory

Let the Agent remember:

Who you are (identity, preferences)
What you've talked about (context)
What methods work (experience)

An Agent with memory understands you better over time.

Advanced Play: Multi-Agent Collaboration

For more complex scenarios, use multiple Agents with division of labor:

Content Creation Team Example:

Topic Agent: Monitor trending topics, find topics
Writing Agent: Write articles based on topics
Review Agent: Check content quality
Publishing Agent: Distribute to various platforms

They collaborate with each other, like a small team.

Who Is Suitable for Agents?

✅ Office workers —— Automate repetitive work, leave on time
✅ Self-media creators —— Auto collection, writing, distribution
✅ Small business owners —— Low-cost customer service, operations automation
✅ Developers —— Quickly build prototypes, validate ideas

❌ Complete beginners —— Should learn basic programming first
❌ Those with overly high expectations —— Agents aren't all-powerful, need debugging and optimization