10 OpenClaw Lessons for Building Agent Teams

0:00Today on the AI Daily Brief, 10 Open Claw and Agent Orchestration Tips. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

0:34And if you are interested in sponsoring the show, send us a note at sponsors at AIDailybrief.ai. Now, AIDailybrief.ai is also where you can find out about everything else in the AIDB ecosystem. We've always got a bunch of things cooking over here. Free training programs, data, research, you name it. You can find that all on AIDailybrief.ai. Now, for this weekend long read slash big think episode, we're turning our attention back to OpenClaw. It's now been a little over a month since the initial burst of excitement around OpenClaw. And this would be the time that you started to see people get disaffected.

1:08A normal hype cycle would tend to see people coming out of the woodwork at this point saying, here's all the ways this is actually much harder and less useful than the people who are telling you that story are actually letting on. And to be fair, there is absolutely some of that and not from AI haters or anything like that. Peter Levels, one of the best known and most admired solopreneurs out there, recently tweeted about his experience with Open Claw, which sort of comes down to just meh. Peter said that he's run Open Claw for over a month. He's had it in a group chat with 26 friends who all played with it, tried to hack it.

1:38He made a cool game, tried to make it make its own money, but ultimately found that his most used use case is actually a girlfriend who uses his OpenClaw via Telegram instead of ChatGPT. Basically, his girlfriend prefers the interface of using Telegram as opposed to the native app interface, and because she also uses Nano Banana Pro, she can do that from there without having to switch between different models. Peter writes, Essentially 99 % of the purpose of OpenClaw, for her at least, is that it's just a really good implementation of an LLM app over Telegram in our native chat interface. All the other stuff isn't important and she doesn't use that and I don't use it.

2:12Now he talks about how there are certain other things that he could see being useful if the models were just a little bit smarter, but ultimately are not for him right now, like briefings of news and conversations on X. Ultimately, he concludes, TLDR, just the best LLM experience on Telegram right now, better than the LLM apps, also helps it is just a continuous convo going on forever. Now he qualifies, I do think this is the direction everything will be going. Like you have autonomous agents just managing your life based on intents you set. We're just in the hype stage of it now and it doesn't work so well yet, but that's obvious.

2:41And like I said, there are plenty of takes out there like that. Even the folks who are getting a lot of value out of this thing are not pretending that it's super easy to do so. Tom Osmond writes, Everyone I know who has gotten to a good open-claw setup has chewed glass for four weeks. It's a battle, but it's worth it in every way. Wichir writes,

3:40In other words, even the people who are really liking OpenClaw have some nuance to it. And this certainly comports with my experience as well. It is abundantly clear to me that there are certain use cases that are way more valuable than others at this stage. For me, by far the most valuable sounds like it's similar to what Wachir found out, is the research agents that I have going, but for example, I tend not to use the OpenClaw coding agent. And I agree that while OpenClaw has dramatically increased what I would call our autonomy ambition, it certainly isn't fully autonomous yet. It requires a lot of interaction.

4:13And yet for all of that, I would argue that the excitement around this product has not only persisted, but in fact grown, even if it has grown in nuanced ways. Azeem Azhar from Exponential View, who, if any of you consume his content you know, is nothing like a Twitter hype person, recently reported that his OpenClaw agent has changed how he works more than anything since the browser. For him, the two reasons are, one, it takes initiative, doesn't wait to be told to do, but spots what needs doing and gets on with it. Two, he writes, I can trust it with real work on its own. Last week, I asked for a knowledge dashboard and six sub-agents built it overnight, arguing about the database schema at 3am and shipping it by the morning.

4:48We've also had very loud takes from people like NVIDIA's Jensen Huang, who called it, quote, probably the single most important release of software probably ever. Matt Schumer writes, just went to go buy a Mac mini for OpenClaw. They're sold out throughout New York City. The staff knew exactly what I was buying it for without me even mentioning it. Pretty crazy. What's more, we also got reports this week around how big OpenClaw is getting in China. The information reported that it has just become huge there, and a steady stream of photos from OpenClaw-related events in China seem to verify that.

5:19Investor Dovi Wan wrote,

5:34She even showed that they give your OpenClaw a birth certificate once it's installed. Others shared pictures from that same Tencent event. Which again is not to say that it's easy or without problems. Tezo's co-founder Arthur Bratman wrote, I imagined OpenClaw got very popular because it provided a seamless onboarding experience. Boy, was I wrong. I guess that is the fabled type of product that users are willing to crawl over barbed wire for. And interestingly, even at all these OpenClaw meetups, it seems like people have a really clear sense of what they're getting into. Allie K. Miller reported back from the sold-out OpenClaw meetup in New York this last week and shared a bunch of interesting observations.

6:12One, she said that not a single person thinks that their setup is 100 % secure, with one expert even saying, if you're not okay with all your data being leaked onto the internet, you shouldn't use it. It's a black and white decision. And it also sounds like people have a pretty realistic expectation about where the technology is right now. General consensus, Allie writes, is that the agents are not reliable enough on their own or lie often, like telling you they finished a task when they didn't. Solutions include secondary agents to check on the first, human checking or requiring more standardized info from the agent.

6:41Another problem that people experience is still token usage. Even when optimized, the cost can get really high really fast. The point is, a little more than a month in, OpenClaw is still exciting, useful, and challenging. It really is a glimpse of the future more even yet than the future itself. But since so many people are sticking with these tools and building with them, we're getting an increasingly broad set of knowledge around it. And at this point, X is basically just a platform for giving you agent orchestration tips and tricks. So let's talk about 10 OpenClaw and agent orchestration best practices that I've seen shared on X and in other places over the last week or so.

7:19We kick off with a few that actually zoom up a level from just OpenClaw itself to the broader agent era that we are now firmly in. These come from a piece by Peter Yang called Your New Job is to Onboard AI Agents, How AI Native Companies Actually Operate. Peter writes, I've spent the last few months interviewing leaders at AI Native Companies. I'm now convinced that that onboarding and managing AI agents is the job, no matter what your function is. So for this piece, Peter talked to three leaders at companies including Linear, Ramp, and Factory to share some lessons on how these AI-native companies actually operate.

7:56One of the first lessons that stands out across all three of these companies is that everyone is an AI builder. At Linear, for example, not only do they insist that every developer should default to a leading agentic coding tool, Peter writes that they insist that designers and PMs work directly on the code base. Quote, agents like Claude open a low friction path for PMs and designers to make changes directly in the code base. Everyone should strive to be a builder. What's more, for the PMs and marketers, the linear team says that they should default to an AI interface. In fact, arguing that 80 to 100 % of their work should be done through a chat interface.

8:30At Ramp, they not only expect AI proficiency across all employees, but have a system for moving people up the path of AI fluency, which is our second tip or best practice. Ramp organizes it into four categories. Level 0 is disengaged or performative. Level 1 is a competent user. Level 2 is a non-technical AI builder. And Level 3 is a technical grade AI builder. In 2025, 25 % were in the L0 category, 50 % were in the L1 category, 5 % were in the L2 category and 20 % were in the L3 category. This year, their goal is to move everyone out of L0, because it sounds like that'll be grounds for dismissal, and into the other categories with a goal of 25 % in L1, 50 % in L2, and 25 % in L3.

9:16Now, given that this is part of the way that they are architecting their company, they're also putting in systems that actually support that type of adoption. The leader that Peter interviewed from Ramp shared that the company tries very actively to remove friction, like giving people access to popular AI tools without tons of constraints. They make adoption visible through things like public Slack channels where people can share what they build. They provide hands-on support through office hours. And with a champion system where there are people whose entire job inside of Ramp is to evangelize, get people set up, and help them implement AI.

9:46This is something that I talked about in my predictions actually for 2026 that we were going to see internal forward deployed vibe coders. And Ramp also tracks usage and makes this a hiring requirement. PM interviews, Peter writes, now include a dedicated session where you need to build a working product and then explain why you built it and how it works. Another best practice that again operates on the larger agentic level, not just in OpenClaw, comes from Linear, whose head of product Nan Yu says, agents should be first-class employees. You should be able to add them to projects, assign them to issues, and mention in comments.

10:17Which is not to say that you're dismissing the humans, but I think this is less some philosophy thing and more about making sure that agents have the full context of your company. This is something that I'm seeing as well, that the companies that are really AI native have agents operating inside the communication systems that make their teams work. The place that I've been building most recently is inside of Slack, connecting agent experiences that have web presences to Slack to be able to get context in the place that people actually operate. In any case, whatever the set of tools that people use.

10:48This idea of agents as first-class employees I think is going to be an important one.

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14:37But from there, let's move on to some tips from some of the folks who have done that glass chewing to really figure out how to dial in specifically their open-claw setup. Shubham Sabhu is a senior AI product manager at Google. He recently published a piece, How I Built an Autonomous AI Agent Team that Runs 24-7. He writes,

15:09Now, he had actually shared a previous post about his team and got a ton of people asking, how do I actually set this up? And one of his first pieces of advice was one agent per task. That becomes our fourth tip. He actually has a whole section called Why a Team and Not a Tool. He writes, Running Unwind AI and the awesome LLM apps repo means doing six things daily. Research what's trending in AI, write tweets, write LinkedIn posts, draft the newsletter, review GitHub contributions on the repo, triage community issues. Each task, 30 to 60 minutes. Six tasks. That's my entire day gone before I do any real work.

15:46I tried solving this with a single agent. One massive prompt that researchers and writes and reviews, it produced mediocre everything. The context filled up, the quality degraded, one agent couldn't hold six different jobs in its head. So I hired six AI agents. This is one of the most common things I am seeing that is different between successful and less successful open claw and agent implementations more broadly. The design paradigm that has really opened up isn't just the agent design paradigm, it's the agent team design paradigm. And that's why I think you have so many people sharing not just their super cool single agent who does all these cool things, but their larger agent teams who do a set of things in a synchronized fashion.

16:27Every time we built agents at Superintelligent or around any AIDB project, we basically constantly find ourselves refining and refining and focusing and focusing more. Sometimes, in fact, frequently, I will even default to more separation than I ultimately want in order to make sure as I'm getting the agent right in the first place, that it's not distracted by all the other things that I might want it to do in the future and can just focus on its core mission. Our fifth tip has to do with security, and once again is still from Shubham and his setup. Like we heard from Ali in the OpenClaw meetup, everyone acknowledges that security is a challenge.

17:00And not just because of malicious actors, but because of agents having access to systems that are important to you and accidentally doing things that end up being bad. Shubham's approach to this, which I think is a good enough starting point to make it our fifth tip or best practice, is that agents get their own world. He writes, security is in your hands. My approach is simple. The agents get their own world. I do not give them access to mine. The Mac mini is their computer. They have their own email accounts, their own API keys, their own scoped access. Nothing on that machine connects to my personal accounts.

17:32API keys for Gemini, 11 Labs, and other services are scoped specifically for this OpenClaw instance. I can monitor usage and kill access in seconds if something looks wrong. I never give agents access to my personal accounts. If I want them to look at an email, I forward it to them. If I need them to review a document, I share it on Telegram. They see exactly what I want them to see, nothing more. This is the same principle you would use with a new employee. You do not hand them the keys to everything on day one. You give them their own workspace, their own credentials, and share information as needed.

18:02Now, I think that in general, this is a great way, especially for people who are just setting these systems up in the first place, to approach this question of security. Basically default to don't give them access to anything that could screw up. It's the best way to prevent problems. What's more, there are tons of really valuable use cases with these open claw agents that do not require them to have access to these other systems for them to be valuable. And to the extent that your objective is to minimize security concerns while maximizing the utility that you're getting from these agents, there's a lot you can do by taking Shubham's advice and giving agents each their own world.

18:40Now that said, I will note that there are going to be times when a lot of the value from these agents, the possible value that you could get, would come from giving them access to some system or another. Our approach to that at AIDB and Superintelligent is just to move very slowly and precisely with it. Rather than adding them across a million different tools and a million different systems all at once, we're taking it one at a time and really honing in on where we think they would be most valuable. The specific area where we've been most willing to test is around sales and sales prospecting with access to our CRM systems.

19:15Could things go wrong? Yes. But is the risk worth it to us based on the type of agentic automation that we can do with them? Also, yes. But like I said, we're moving very slowly and precisely. And for anyone who doesn't even want to get that far, just take this fifth tip and give agents their own world. Now, if you are working with all of these different agents who are each doing their own tasks, one thing that will come up is coordination, multi-agent coordination. Is that going to require some fancy mission control center or orchestration framework or API calls? Shubham argues no, that the coordination is the file system.

19:51As he puts it, it's just files. Dwight does research and writes finding to intel-slash-daily-intel.md. Kelly wakes up, reads that file, and drafts tweets from it. Rachel reads the same file, drafts LinkedIn posts. Pam reads it and writes the newsletter. The coordination is the file system. Dwight's sole.md file tells him exactly where to write. Kelly's agents.md file tells her exactly where to read. No middleware, no integration layer. Dwight writes a file. Kelly reads a file. The handoff is a markdown document on disk. This sounds too simple. It is simple. This is why it works. Files do not crash.

20:28Files do not have authentication issues. Files do not need API rate limit handling. They are just there. The structured data lives in JSON. The human readable summaries live in markdown. Agents read the markdown. The JSON is the source of truth for deduplication and tracking over time. Point being, tip number six is that you don't need a bunch of really fancy coordination systems. you just need a clear process of handoffs between the different documents that represent the relevant work from one agent to the next. A last tip that comes from Shibam is around memory, and the fact that you have to program memory.

21:00He writes, agents wake up with no memory of previous sessions. Every conversation starts fresh. This is a feature, not a bug, but it means memory must be explicit. Now he writes up exactly how he does that explicit memory design, but the key takeaway is that you do have to be explicit about this. You have to build a system where they can make their own memory over time. One of the things that I think we're all learning by doing is about design principles for agents, and memory remains one of the great undersolved issues of AI and agentic systems. So for now at least, we just have to build ways to approximate memory by giving our agents access to context that they can recall at the right moments.

21:41This has to be an intentional process, and if you are building agents, programming memory is going to be a key part of your job. An eighth tip and best practice is to use skills. Skills are, in the simplest form, simple text documents that give agents information on how to do something. They are a standard that started with clawed code and very quickly became adopted by everyone, although some have pointed out that calling them a standard is even a little bit weird, given that it's literally just markdown files. One of the places that I see people jump from beginner to more intermediate and advanced usage is when they actually give their agents distinct types of skills.