tool built upon coding agents corresponding to Claude Code. It lets you have a coding agent running 24×7, working proactively and reactively to unravel tasks. I’ve arrange multiple OpenClaw instances already and have learned a good few things through lively usage. I’ve also discussed it so much with colleagues who work with OpenClaw agents every day, and in this text, I’ll share a few of the suggestions and tricks I’ve learned on the way to get essentially the most out of OpenClaw and a few mistakes that I’ve made, which I’ll inform you the way to avoid.
Why arrange OpenClaw
The essential reason you must arrange OpenClaw is that it will probably make you simpler as an engineer. Where you previously needed to run every part in Claude Code and be in your computer and able to work in any respect times, OpenClaude could be run from a separate computer and accessed from anywhere through applications like Telegram or Slack. This makes it incredibly easy to interact with a coding agent corresponding to Claude Code, and you’ll be able to interact with it from anywhere.
Moreover, you’ll be able to easily arrange cron jobs and skills, which make the agent run code at specific intervals and all the time remember to do this. And it will probably have skills that it loads on demand to higher perform specific tasks.
All in all, OpenClaw simply makes your coding agents a greater assistant. It makes it more available and higher capable of perform tasks.
Mistake 1: Not running in Docker
The primary mistake I made was not running OpenClaw in Docker containers. There are various reasons you must run OpenClaw agents in Docker containers, and I’ll list just a few of them here.
- It’s safer. Your agent can’t access every part in your computer; it will probably only access what’s available within the Docker image.
- It’s super easy to make safety copies of your agent and move them anywhere, since you’ll be able to simply download a Docker image and use it someplace else. This works because a Docker image is a totally separate container that could be run entirely by itself.
- If you happen to run multiple agents on the identical computer, it separates them higher in order that there’s no overlap between your agents.
Overall, there’s no real reason to not run in Docker. It’s also super easy to establish running OpenClaw in Docker since you’ll be able to simply ask your coding agent to set every part up for you. In point of fact, you don’t should do anything yourself, and the coding agents are extremely proficient at organising the Docker system for you. After I did this myself, I principally didn’t should do anything except prompt the model to establish OpenClaw in Docker, and it implemented it with no problem.
Mistake 2: Not give agent proper training
Mistake number two is just not giving your agent the right training and setup help that it must perform well. After I arrange my first agent, I spent a maximum of ten minutes explaining what it was purported to do, gave it the mandatory permissions, and hoped that that might be enough.
It turned out that’s not the way you do it in any respect. What ended up happening is that my agent wasn’t really capable of do any of the tasks it was purported to do since it hadn’t received specific training on the way to execute those tasks. I, for instance, gave my agent access to AWS without telling it the way to access AWS, the way to use it, the way to interact with people through Slack, and so forth.
What ended up happening specifically for me was that the agent began interacting with people on Slack in messages it shouldn’t have replied to. And when it was specifically tagged, it didn’t know exactly what to do in those situations.
To resolve this problem, it is advisable to give your agent super-specific training and tell it what it’s purported to do, what it’s not purported to do, and the way it’s purported to do the tasks you ask it to do.
For instance, for those who give it AWS access and let it interact with people through Slack, you must:
- Explain to it the AWS docs in order that it knows exactly the way to use it and doesn’t make incorrect API calls or SDK calls.
- Explain to the agent which messages it should reply to and which messages it shouldn’t reply to, which of them are relevant, principally.
- Explain to it the various questions people might ask and the way it should approach answering those questions. For instance, if someone asks about a selected customer, it should look up that customer in the client table, take a look at the various instances which can be relevant to this customer, and ask the user clarifying questions.
Mistake 3: Not giving your agent enough permissions
Mistake number three is if you arrange your agent appropriately but you haven’t given it enough permissions to do what it’s purported to do. For instance, if you’ve asked your agent to perform a bunch of AWS tasks but not given it enough access, for instance, it will probably only access DynamoDB but not S3 completely, it’s very hard for the agent to perform a task.
While you arrange an agent, you must consider it as if it’s a human. If you happen to gave an intern a bunch of tasks to perform, but you didn’t give the AWS permissions the intern needed to perform the duty, it might be very hard for the intern to know what to do.
The intern wouldn’t know to ask for permissions, for instance, or won’t know because they’ve never handled this case before. Or it’d think it’s purported to figure that stuff out itself, while in point of fact, you’ve to provide it the permissions it needs.
Thus, you must do the next when organising an agent.
- Think thoroughly through every part the agent is purported to do and make sure that it has access to all relevant resources. And for those who don’t give it access to specific resources, make sure that to tell the agent that it doesn’t have access to this and the way to reply to people in the event that they ask questions that require such access.
- Give the agent access to every part it’d need, after all, inside security concerns. This likely includes read access to almost every part you’ve, just because read access is non-destructive.
- Monitor the agent’s performance, especially in the beginning of its setup. If you happen to notice the agent fighting specific tasks, you must help the agent by telling it the way to resolve such tasks. And you must either provide or revoke access that the agent needs or doesn’t need.
Overall, all of it comes all the way down to monitoring your agent and ensuring that it really works as expected.
Conclusion
In this text, I’ve discussed three common mistakes which can be made when organising OpenClaw and that I’ve made specifically myself once I’ve arrange OpenClaw agents. These mistakes severely limit the effectiveness of OpenClaw, so I highly recommend following all of the guidelines I’ve provided in this text and avoiding the three mistakes I’ve listed. Overall, nevertheless, all of it comes all the way down to monitoring your OpenFlow agent and helping it where you’ll be able to notice the agent is struggling. If the agent is fighting specific tasks, it’s probably not an agent problem, but quite a setup problem or a user error. Thus, you must monitor your agent and make sure that it’s effective on the tasks you ask it to do.
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