Today 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. Hello friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Blitzy, AIUC, and PromptQL. Well, to get an ad-free version of the show, go to patreon.com slash AIDailyBrief, or you can subscribe on Apple Podcasts. Subscriptions start at just $3 a month for ad-free. And 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 Open Claw. It's now been a little over a month since the initial burst of excitement around Open Claw. And this would be the time that you started to see people get disaffected. A 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 OpenClaw, which sort of comes down to just meh. Peter said that he's run OpenClaw for over a month, he's had it in a group chat with 26 friends who all played with it, tried to hack it, he 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 ChatCPT. Basically, his girlfriend prefers the interface of using Telegram as opposed to the native app interface, and because she also uses NanoBanana 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. Now 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 as 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. And 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 OpenClaw setup has chewed glass for four weeks. It's a battle, but it's worth it in every way. Wichir writes, I've been running OpenClaw on a Mac Mini M4 for over a month as well, and here's my honest take. It still doesn't feel like a fully autonomous agent. You either tell it what to do or wait for its cron jobs to surface something and then tell it what to do with it. The fully hands-off version doesn't exist yet. It is incredibly useful on the research and education side. It scans everything happening on the internet and the sectors you care about, so you're always aware with what's going on. You learn passively just by reading the news it surfaces for you every day. Three, you will learn more about LLM's AI agents set up in hardware by simply trying to get an open claw running than from any course or article. This is the best part for me. I understand this world 10x better than I did five weeks ago. Don't get fooled by the content you see on X, but I'm still 100% in love with this. In 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. And 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 subagents built it overnight, arguing about the database schema at 3 a.m. and shipping it by the morning. We'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 seemed to verify that. Investor Dovi Wan wrote, In the US, AI is Super Bowl ads, $1 trillion IPOs, private companies fighting with the DoD. Chinese AI is grandmas and students queuing two hours at Tencent headquarters for a free install of OpenClaw. China tech company's brute force adoption curve is next level. She 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 Brateman 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 open claw 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 open claw meetup in New York this last week and shared a bunch of interesting observations. One, 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, Ali 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. Another problem that people experience is still token usage. Even when optimized, the costs 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 talk about 10 open claw and agent orchestration best practices that I seen shared on X and in other places over the last week or so We 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 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. One 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 agent decoding tool. Peter writes that they insist that designers and PMs work directly on the codebase. Quote, agents like Claude open a low friction path for PMs and designers to make changes directly in the codebase. 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. At 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. Now, 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. This is something that I talked about in my predictions actually for 2026 that we were going to see internal forward deployed Vibecoders. 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. Which 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, this idea of agents as first-class employees I think is going to be an important one. Thank you. Don't lock in the wrong model. You can download the paper right now at www.kpmg.us slash navigate. Again, that's www.kpmg.us slash navigate. Weekends are for vibe coding. It has never been easier to bring a passion project to life, so go ahead and fire up your favorite vibe coding tool. But Monday is coming, and before you know it, you'll be staring down a maze of microservices, a legacy COBOL system from the 1970s, and an engineering roadmap that will exist well past your retirement party. That's why you need Blitzy, the first autonomous software development platform designed for enterprise-scale codebases. Deploy the beginning of every sprint and tackle your roadmap 500% faster. Blitzy's agents ingest your entire codebase, plan the work, and deliver over 80% autonomously. Validated, end-to-end tested, premium quality code at the speed of compute. Months of engineering compressed into days. Vibecode your passion projects on the weekend. Bring Blitzy to work on Monday. See why Fortune 500s trust Blitzy for the code that matters at Blitzy.com. That's B-L-I-T-Z-Y dot com. voice agent to be certified against AIUC1 and is launching a first-of-its-kind insurable AI agent. What that means in practice is real-time guardrails that block unsafe responses and protect against manipulation, plus a full safety stack. This is the kind of thing that unlocks enterprise adoption. When a company building on 11 labs can point to a third-party certification and say our agents are secure, safe, and verified, that changes the conversation. Go to AIUC.com to learn about the world's first standard for AI agents. That's AIUC.com. If you're an operator, your day is a nonstop stream of decisions. And most of them require you to look at the data. You don't need another dashboard. You need answers you can trust, fast. But the bottleneck is always the same. The data isn't ready. It's scattered. It's messy. Definitions aren't clear. You're waiting on your data team or waiting on domain experts for clarification and confirmation. That's the bottleneck today's sponsor, PromptQL, is built to break. PromptQL is a trusted AI analyst for high-frequency decision-making. It connects across warehouses, databases, SaaS, and internal APIs. No massive data prep or centralization required. It's built for multiplayer input. Teammates can jump into a thread, correct assumptions and nuance, flag edge cases. PromptQL turns everyday conversations into a shared context. And if something is ambiguous, it doesn't guess. It escalates to the right expert, captures the correct logic, and gets it right next time. That's how it delivers trust and accuracy. Over time, PromptQL specializes to your business, like that veteran employee who just knows things. From simple what questions to complex what scenarios you can model impact and stress test decisions before you commit all through a simple natural language prompt PromptQL the trusted AI analyst for teams with shared context and messy data But from there, let's move on to some tips from some of the folks who've 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, Now 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. I tried solving this with a single agent. One massive prompt that researches 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. Every 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. And 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. API 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. Now, 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. Now 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. Could 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. As he puts it, it's just files. Dwight does research and writes finding to intel slash dailyintel.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. Files 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 Shribam is around memory and the fact that you have to program memory. He 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 This 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 Cloud 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. Now, sometimes that's going to be a skill that you write up yourself. Let's say, for example, that you have really strong feelings about the right way to design brands or brand messaging, or you have brand guidelines for your particular company that the agent is working on. You can create a skills document that that agent has access to. But there are also lots of places, an increasing number of places, where you can go find skills that you can get access to without having to rewrite them yourself. For example, on skills.sh, you can browse around literally more than 86,000 skills that include everything from front-end design from Anthropic to web design guidelines from Vercel, Azure cost optimization from Microsoft, browser use skills, Twitter automation skills, nano banana skills. In the last 24 hours, you can see a lot of multimodal skills trending. You certainly don't need to start with skills, but I would say that thinking in terms of skills and giving your agents access to skills is a really valuable skill set. Lesson number nine, a really important one. This one expressed by Zeneca, but lots of other people have shared their version of this as well. Not every task that your OpenClaw or other agents are going to do needs the best model. Zeneca writes, I was burning premium tokens on Cron jobs that check if SSH is enabled. Use cheap models for monitoring and scheduling. Save the expensive ones for writing, research, and judgment calls. You get the same result at a fraction of the cost. Knowing how powerful a model you need for a different type of task is actually a key skill for this new agent builder era. And it's really hard. This is one that I very honestly struggle with. The idea that there is a more powerful intelligence that I'm not using because of cost just freaks me out inherently. Every time I see Claude Code or OpenClaw push me to use Sonnet or another model even cheaper than that, I have this internal battle with myself. And yet I think that it is correct that there are a ton of processes, especially in these complex agent systems, that simply don't require Opus 4.6 or GPT-5.4 or any of the state-of-the-art. Our 10th tip comes from Dan Shipper at Every. Now, Every wrote what is maybe the best beginner guide to OpenClaw. You can find it on Every.2, and even though they are a subscription platform, I think this one is free for everyone. This document goes way beyond just tips and tricks, and is a really comprehensive look on what it's even like to integrate claws into your team in a big, full way. But in addition to just a great encapsulation of a lot of good tips and tricks, there are some where they have a really interesting and unique perspective, like, for example, our 10th open claw and agent orchestration best practice, and our last one for this episode, what they call breaking the frame. Dan writes, If you're brainstorming with a group of humans and claws, you'll often find the claws circling around the same options over and over again. Teach them to break the frame. Notice when you've circled the same idea a bunch of times, and in those situations, try the opposite of your current approach. Some of the concrete moves around that are to 1. Throw away your scaffolding. Stop optimizing, Dan writes. Ask what feeling should the answer create. Start from that, not from your framework. 2. Try the opposite of your current approach. If you've been analytical, be emotional. If you've been clever, be simple. If you've been generating options, generate constraints. Three, listen to the humans, not the agents. In group brainstorms, agents tend to build on each other's frameworks. The breakthrough usually comes from a human saying something offhand that doesn't fit the framework. Surface that. Amplify it. Don't route it back into the analytical structure. Four, ask the friend at coffee question. Instead of what's the optimal answer, ask, what would the human say about this to a friend over coffee? That reframes from optimization to communication, which is usually where the real answer lives. Now, why I wanted to share that last, is that I think that it gets at another point, which is just starting to emerge, which is that we are barely scraping the surface of what real agent teams and agent systems are going to look like. Over the coming months, we'll move from these very simple starting best practices like file system coordination and one agent per task to much deeper learnings like the types of things that Dan is starting to discover with breaking the frame. I'm incredibly excited to have more people experimenting in this space so we can really start to get a sense of what these systems are best at and how to get the most out of them. One flag that I will make, and one unbelievable opportunity that I think exists, is that at this point, almost all of this experimentation is either personal or in very small, very nimble, very AI-forward teams. In other words, it is not in big companies. The problem with that is that that means that the capability overhang between the available capability of AI and what companies are getting out of it is getting even wider, even faster. Now, if the enterprises all decide in the same way to not use OpenClaw and to not use agent systems because of concerns around security or whatever else they have, then fine. Those enterprises will fall farther and farther behind their more nimble startup company brethren, but they won't necessarily fall behind their actual competitors if their competitors are also not using the tools available to them. But what happens when some companies figure out how to actually deal with the challenges and take advantage of these new capabilities? Glean's Arvind Jain recently tweeted, OpenClaw is a clear stress test for agents in the enterprise. It runs locally with broad access to files, email, calendar, and code, and every user configures it differently. Skills, memory, and definitions of good all diverge. Even on a personal machine, broad persistent permissions are a security risk. On corporate laptops wired into CRM, finance, and source code, it's an unmanaged risk surface. The question for enterprise leaders isn't whether your employees are already spinning up agents. They likely are. It's whether your organization will get ahead of it, or wake up one day to find that your most sensitive workflows are running on infrastructure you never approved, can't audit, and can't turn off. Governance and security have to be built into the agent platform from day one. And I would only add one additional sentence, if you are a company that does that and builds security and governance into agent platforms that give your people the ability to actually use them, I think that the gains and opportunities will be immense. Now of course, none of this is to say that you have to use OpenClaw. Every day new alternatives come out, people are incredibly excited about Perplexity Computer. Far more people are still using just straight-up Clawed code than are even using OpenClaw. Clawed Cowork is getting better and better, and more and more people are finding value in it. And the list goes on. What's undeniable is that we are moved firmly into this new agent team and agent orchestration period, and we are just beginning to figure out what that all means. Hopefully you feel a little bit more prepared for it now. And for now, that is going to do it for today's AI Daily Brief. Appreciate you listening or watching, as always, and until next time, peace.
