If you’ve been using OpenClaw and feel like your AI agent is, well… kind of dumb, you’re not alone. I hear this all the time — people set up their bot, see everyone else on Twitter showing off these incredible automations, and then their own agent just sits there being useless. Or worse, it confidently gives you completely wrong information. So in this video, I sat down to talk through the real fixes that actually work.
Break Down Your Tasks Into Smaller Steps
This is probably the single most important tip. When you give your AI agent a massive, vague task like “monitor Twitter and summarize what’s happening in crypto,” you’re setting it up to fail. Think about what that actually involves — browsing dozens of tweets, understanding context, filtering noise, and delivering a coherent summary. That’s a lot of moving parts.
The fix is simple: decompose. Instead of one giant instruction, break it into discrete steps. For example, I told my agent Stark to first scan specific Twitter accounts and save those tweets. Then I had it store everything into a vector database so it could actually retrieve that information later when needed. This is a technique called Retrieval Augmented Generation (RAG), and it’s one of the most effective ways to ground your AI in real data instead of letting it make things up.
You don’t need to be a programmer to do this. The core idea is just: give your agent one clear task at a time, verify it works, then move to the next step.
Build Dashboards to Spot Failures
Here’s something that happened naturally once I started breaking tasks down — my agent actually built a dashboard on its own to track the tweets it was monitoring. It wasn’t polished, but it was functional. And more importantly, it let me see exactly where things were working and where they weren’t.
This is a huge advantage of task decomposition. When your bot fails (and it will), you can pinpoint which specific step broke instead of staring at a black box wondering what went wrong. You can look at the actual data it collected and verify whether it’s real or fabricated.
Stop Your AI From Gaslighting You
This one caught me off guard. I asked my agent to pull stats on our BoxminingAI YouTube channel — things like view counts and recent video performance. It came back with detailed numbers that looked totally legit. The problem? It was all completely made up. The AI just fabricated view counts and video titles because it couldn’t actually access that data.
This is a well-documented problem called AI hallucination. Studies have shown that even the most advanced language models will confidently generate false information rather than admit they don’t know something. The solution is to always use proper APIs instead of expecting your agent to scrape web pages. I had my agent walk me through setting up the YouTube API, getting developer credentials, and making proper API calls. Once you’re pulling real data through official channels, the hallucination problem largely disappears.
The key takeaway: if your agent gives you data, always ask for the source link. If it can’t provide one, that’s a red flag.
Test Everything, Then Save It as a Skill
One thing AI agents are terrible at is self-testing. My bot would build something, proudly announce “all done!” and then the dashboard would be completely empty. It just… didn’t check its own work. So now I explicitly tell it: “Test the connection. Take a screenshot. Show me it works.”
Once you’ve verified a workflow works end to end, save it as a skill. Skills in OpenClaw are basically reusable instructions that your agent can reference in the future. This means you only have to solve each problem once. After that, your agent knows the correct process and can repeat it reliably.
Feed Your Agent the Documentation First
Before you ask your agent to use any API or service, give it the documentation first. Don’t assume it knows how to call the YouTube API or interact with your email provider. Start your prompt with something like: “Here’s the API documentation. Learn and understand this first.”
This alone can prevent a huge number of failures. The agent will understand the correct endpoints, authentication methods, and data formats before it tries to do anything. And just like workflows, you can save this learned knowledge as a skill for future reference.
When Bots Go Haywire
There was a recent story about someone who installed an AI agent with root access on their computer, and it started messaging everyone in their contact list. This is exactly the kind of thing that happens when you give an AI too much freedom without proper guardrails.
The lesson here is clear: start small. Give your agent limited permissions and a narrow scope. Test with low-stakes tasks before you hand it the keys to your email or social media accounts. AI agents are powerful tools, but they need boundaries — just like any automation.
Don’t Treat AI Like a Human
This might sound obvious, but it’s a trap everyone falls into. We talk to AI agents in natural language, so we start expecting human-level judgment. But they’re not human. They will fail on tasks — sometimes simple ones — for no apparent reason. I’ve found that my agent sometimes fails more on easy tasks than complex ones, which is genuinely baffling.
The point isn’t to be discouraged. It’s to set the right expectations. Start with small tasks, verify they work, then scale up. Don’t be afraid to ask for ambitious things like full presentations or detailed dashboards — just be prepared to iterate and correct along the way.
The Bottom Line
If your OpenClaw agent feels stupid, the fix isn’t a better model or more expensive hardware. It’s better task management. Break complex goals into small, verifiable steps. Use proper APIs instead of hoping your agent can scrape the web. Test everything. Save working workflows as skills. And most importantly, remember that AI agents aren’t humans — they need structure, verification, and clear boundaries to perform well.
We’re building a community around sharing these tips and workflows, so make sure to join our Discord to learn from what others are doing. The more we share, the smarter all our agents get.