Hello everyone, this is programmer Wan Feng.
Recently the term "AI Agent" has been particularly hot.
But honestly, after reading many articles, I was still confused: What exactly is an Agent? What does it have to do with me?
Until I built one myself—that's when I understood—This thing can actually help you get work done.
Today sharing my hands-on experience, teaching you step by step from zero.
What Exactly Is an AI Agent?
Don't be intimidated by the jargon, keep it simple:
Agent = Large Model + Tools + Memory + Autonomous Decision-Making
Regular ChatGPT: You ask a question, it answers, done.
AI Agent: You give it a goal, it autonomously breaks down steps, calls tools, executes, provides feedback, adjusts, until completion.
Like an intern—explain the task clearly, and they figure out how to get it done.
What Agent Did I Build?
Requirement was simple: Every morning at 8 AM, automatically organize my messages and to-dos, send me a daily report.
Sounds simple, but involves:
- Reading WeChat unread messages
- Checking Feishu calendar
- Checking GitHub Issues
- Aggregating into structured report
- Sending to my email
Before: Manually open 4 apps, copy-paste, at least 20 minutes.
Now: Fully automatic, I just look at the result.
Building Process (Super Detailed)
Step 1: Choose Framework
There are many Agent frameworks:
- LangChain (most popular, but a bit heavy)
- AutoGPT (fully automatic, but easy to go off track)
- OpenClaw (lightweight, easy to get started)
I chose OpenClaw because of clear documentation and active community.
Step 2: Define Agent Capabilities
What I needed this Agent to do:
- Read WeChat messages (using itchat library)
- Query Feishu calendar (Feishu API)
- Get GitHub Issues (GitHub API)
- Generate daily report (GPT-4)
- Send email (SMTP)
In OpenClaw, these are called "Skills."
Step 3: Write Prompt
This is the most critical step. Tell the Agent:
- Who you are (a daily report assistant)
- Your task (collect information and generate daily report every morning)
- Your workflow (A first, then B, then C)
- Output format (Markdown, including which sections)
1 | You are a daily report assistant. Every morning at 8 AM, you need to: |
Step 4: Debug and Optimize
First run, Agent missed a few WeChat group messages.
Checking found: Group whitelist not configured.
Added configuration, ran again, perfect.
This is the core of Agent development: iterative optimization.
Running Results
Every morning at 8:05, I receive the email on time:
1 | 【Daily Report】2026-02-28 |
Clear at a glance, no longer need to scroll through 4 apps.
Advanced Play
After basic version was working, I added several features:
Auto-Reply Common Questions
Students often ask "how to install Python"—I let Agent identify keywords and auto-send tutorial links.
Monitor Competitor Updates
Scheduled scraping of competitor websites, notify me of any updates.
Data Report Generation
Every Monday auto-generate last week's data report, send directly to group.
Each new feature is just 1-2 hours of development time.
Pitfalls I've Encountered
❌ Prompt not clear enough → Agent misunderstood task, needed repeated debugging
❌ Permissions not configured → Couldn't read some data, troubleshooting took half the day
❌ No error handling → If one API crashed, entire process stalled
✅ Solution: Add logging, add retries, add fallback plans
Who Is Suitable for Agent Development?
✅ Programmers —— Next big thing, get ahead early
✅ Product Managers —— Understand technical boundaries, design better products
✅ Operations/Sales —— Automate repetitive work, 10x efficiency
✅ Entrepreneurs —— Achieve AI capabilities at low cost
No need to be an algorithm expert, but basic programming ability is required.
Want Systematic Learning?
If you:
- Are interested in AI Agents but don't know where to start
- Want to master Python programming basics
- Hope to build truly usable automation tools
I recommend "AI Python Zero Foundation Practical Camp":
- 📚 Learn Python from 0 to 1
- 🤖 Special chapter on AI Agent development
- 🛠️ Build 3 complete Agent projects
- 👥 Community Q&A, help when problems arise
