Building with OpenClaw: What I Learned About AI Agents
🇬🇧 English Version
Building with OpenClaw: What I Learned About AI Agents
TL;DR: OpenClaw showed me what AI agents can truly achieve: proactive problem-solving, self-healing systems, and continuous evolution. But this is just the beginning — I’m witnessing a new era accelerating forward. Future software won’t limit users; it will dynamically adapt to everyone’s needs. The hardest question is no longer “What can you do?” but “What matters most to you?”
Let me tell you what happened yesterday. I woke up to a notification: “Security scan completed. 3 issues found and fixed automatically.” I didn’t ask for this. I didn’t even know these issues existed. My AI agent saw a problem, wrote the solution, and deployed it while I was asleep.
I’ve been using OpenClaw as my personal assistant for a while now, and moments like this have completely shifted my perspective on what AI agents can truly become. Here are my takeaways.
The Brain Is Everything
The model — the “brain” — is the most important part. Choose the best one you can have.
Some say OpenClaw underperforms. But often, it’s because they’re not using the best model available. The UI is just the interface — the model underneath is what actually does the thinking. And with the right model, the difference is night and day.
What Makes a True Agent
OpenClaw demonstrates something powerful: agents that are proactive, self-healing, and self-enhancing.
Before vs After
Before OpenClaw:
- Manually check for security issues (when I remember)
- Write scripts, test them, hope they work
- Set up cron jobs, then forget to update them
- React to problems after they’ve caused damage
After OpenClaw:
- Agent proactively scans for issues daily
- Writes and deploys fixes automatically
- Evolves the system with new detection modules
- Prevents problems before I even notice them
Example: Security as a Living System
Yesterday, OpenClaw proactively identified security risks in my system — not because I asked, but because it understood the importance. It didn’t just report issues; it wrote a script to automatically fix file permissions, scan for exposed tokens, and check for vulnerabilities.
Then it went further: it stored the script in its knowledge base, scheduled daily scans, and evolved the system with 9 detection modules. Each day, it learns what new risks to watch for.
Over the past week alone, it’s:
- Fixed 12 file permission issues automatically
- Detected 3 exposed API tokens
- Prevented 2 potential security breaches
- Saved me an estimated 4 hours of manual security audits
That’s proactive (spotting problems I didn’t see), self-healing (fixing them automatically), and self-enhancing (improving the system over time). This assured me and increased my confidence to trust it more.
A true agent knows how to satisfy user requirements. It knows when to write code to solidify its work — reducing uncertainty and randomness. It understands who you are through short-term memory, long-term memory, and semantic search. In most cases, it remembers remarkably well, or enhances itself to remember what matters about you.
By delegating to agents, you free up your precious time for what matters most. Would you hire a secretary or a personal assistant? An agent can be exactly that — tailored to your requirements.
The Future of Software & UI
Software and UI will transform into something completely dynamic, adapting to each person’s preferences. Every time you consume data, you’ll see a different form — a different representation layer — tailored to what you want.
Example: Imagine your news feed. Instead of a fixed grid layout everyone sees, an AI agent could present it as:
- A morning briefing with priorities ranked by your interests
- A visual dashboard for data-driven readers
- An audio summary for your commute
Data is the foundation. UI is merely the way to process, consume, and present it. For decades, we’ve used UI and software to constrain what people can see and what people can do with their own data.
Software should adapt to users’ evolving requests — not constrain users within its boundaries. Yet constraining users is exactly what most software does today.
The New Question
The most difficult question for everyone now is no longer “What can you do?” or “What are you good at?”
It’s: What is the most important thing to you?
This is why I use OpenClaw — it helps me focus on what truly matters, while handling everything else in the background.
🇨🇳 中文版本
OpenClaw 让我看到的：AI Agent 的可能性
一句话总结：用 OpenClaw 这段时间，我看到了 AI Agent 能做到什么程度：主动发现问题、自己修 bug、还能不断进化。但这仅仅是开始——我看见新世代正在加速前进。未来的软件不会限制用户，而是根据每个人的需求动态适应。最难的问题不再是”你能干什么”，而是”什么对你最重要”。
昨天早上醒来，收到一条通知：”安全扫描完成。发现 3 个问题，已自动修复。”我没要求它做这个。我甚至不知道这些问题的存在。我的 AI Agent 自己发现了问题，写好了解决方案，趁我睡觉的时候部署上线了。
用 OpenClaw 做个人助手这段时间，这样的时刻彻底改变了我对 AI Agent 的理解。
大脑才是关键
模型——也就是”大脑”——是最重要的。能用最好的，就别将就。
很多人说 OpenClaw 不行。但其实是他们没用对模型。界面只是表面，底层的模型才是真正在思考的那个。选对模型，体验天差地别。
什么才算真正的 Agent
OpenClaw 让我看到了一个强大的特质：主动、自愈、进化。
使用前 vs 使用后
用 OpenClaw 之前：
- 想起来的时候才检查安全问题
- 写脚本，测试，祈祷能跑通
- 设好定时任务，然后忘了更新
- 等出了问题才去救火
用 OpenClaw 之后：
- Agent 每天主动扫描
- 自动写脚本并部署修复
- 系统不断演化出新的检测模块
- 问题还没发生就被预防了
实例：安全扫描系统的自我进化
昨天 OpenClaw 主动发现了我系统里的安全问题——我没要求它做这个，是它自己意识到的。它不只是报告问题，而是直接写了个脚本自动修复文件权限、扫描泄露的 token、检查漏洞。
然后它更进一步：把脚本存到自己的知识库里，设置每天自动扫描，还演化出了 9 个检测模块。每天它都在学习新的风险点。
就过去一周，它已经：
- 自动修复了 12 个文件权限问题
- 检测到 3 个暴露的 API token
- 预防了 2 次潜在的安全漏洞
- 帮我省下了大约 4 小时的手动审查时间
这就是主动（发现我没注意到的问题）、自愈（自动修复）、进化（系统越来越完善）。这让我更放心把事情交给它。
真正的 Agent 懂得怎么满足你的需求。它知道什么时候该写代码把工作固化下来，减少不确定性。它通过短期记忆、长期记忆、语义搜索来理解你是谁。大多数情况下，它记得很清楚，或者会主动进化自己来记住你在意的事。
把事情交给 Agent，你就能把宝贵的时间用在真正重要的事上。你会雇秘书或私人助理吧？Agent 就是这个角色——为你量身定制。
软件和 UI 的未来
软件和界面会变成完全动态的，根据每个人的偏好适应。同样的数据，每次你看到的呈现方式都可以不一样——取决于你想要什么。
举个例子：想象你的新闻源。不再是大家都看到的固定网格，AI Agent 可以把它变成：
- 早晨简报，按你的兴趣排序优先级
- 可视化仪表盘，适合数据驱动的读者
- 音频摘要，通勤路上听
数据是基础。UI 只是处理、消费、呈现数据的方式。几十年来，我们用 UI 和软件限制人们能看到什么、能对自己的数据做什么。
软件应该适应用户不断变化的需求——而不是把用户限制在边界内。但现在大多数软件做的就是限制用户。
新的问题
现在最难的问题不再是 “你能做什么？” 或 “你擅长什么？”
而是：什么对你最重要？
这就是我用 OpenClaw 的原因——它帮我专注于真正重要的事，其他的都在后台搞定。