One Agent Is NOT ENOUGH — Agentic Coding BEYOND Claude Code

If you’ve been building with agents and pushing what you can do with your agent coding tool, you’ve realized something critical. One agent is not enough. Imagine this. It’s the end of 2026 and you’ve evolved your engineering from a single agent to multiple agents to agent teams. They’ve been building and learning just like you because you stopped using agents that forget and you started using agent experts. Each agent team you have contains agents that have their own skills, expertise, and domain knowledge, supercharging them so that they outperform any other agent. In fact, you have a few agent teams that are outperforming your co-workers. Engineers, one agent is not enough. Multi-agent orchestration and tools like Claude Code are the current frontier. But today, I want to show you a system that pushes beyond cloud code. This is multi-team agentic coding. Here we use

[00:01:02] specialized teams of agents to outperform the normal distribution of results. Now, if you’re a cost minmaxer and you care about saving money over getting results, this video is not for you. But if you’re an engineer working on mid to large size production code bases, stick around and let’s take a peek at the performance gains you can get from multi-team agentic coding. Here we have a customized PI coding agent. We’ve re-engineered the entire experience so that we’re inside of a chat room with our teams of agents. Let’s start out simple and then expose the valuable properties this system has. We’ll start with my favorite ping. We’re talking to our orchestrator. It’s given us a classic ping-pong response. Let’s talk to our team leads. Ping each team lead. Now our orchestrator is going to call every team lead and you can see there our team leads are now responding.

[00:02:02] Notice here we have a two-layer architecture where we have an orchestrator talking to our team leads. All of our team leads responded and now our orchestrator is composing the results and giving us the final response. Of course in the footer you can see all of our details. We have our orchestrator leads context and total cost summed together. So the orchestrator cost here is the cost of the entire multi-team system. But this goes even deeper. As you saw here in the beginning, these are multiple teams of agents. Let me show you exactly what I mean. Have engineering and present tree structure of the most important files. And so we’re delegating directly to our engineering team. So our engineering lead is going to take over. We’re at-mentioning them. And now engineering is going to do something incredible. It’s going to delegate to front-end developer. Our engineering lead tried to access files that it doesn’t have permission to. It stepped out of its domain and so it realized this and it delegated to its team members who have the right tools. So this might seem like

[00:03:01] an annoyance or like a waste of tokens, but this is actually very valuable. The engineering lead prompted the back-end developer and it also prompted the front-end developer for an answer. And so you can see both these agents getting work done here in a chat-like interface. And so what we have here is three tiers of agents. We have our orchestrator, we have our leads, and we have our workers. They both responded to the lead. And now the lead is going to respond to the orchestrator. This is a very, very powerful multi-team orchestration tool. There it is. Engineering nailed it. Here’s the rundown. And now our orchestrator is giving us a full breakdown of what’s going on. This multi-team tool has advantages already over the normal prompting back and forth experience. If we hit / toggle workers, we can see our actual full team breakdown. We’ve been conversing with our engineering team and engineering team has looked up files. It’s read its context and it’s done something incredible as well. If you realize here

[00:04:01] that out of the box for a simple command like that, the context is actually quite high. You’ve noticed something really important about this system. Our agents are loading memory files. They’re loading their agent expertise. More on that in a moment. If you can see how powerful this system can be, definitely like, subscribe so you let the algorithm know that you’re interested in multi-agent orchestration like this. Let’s keep pushing this system further. We’re operating in a prompt routing codebase. This is a common problem a lot of LLM based applications have. You don’t want to pay for intelligence that you don’t need. Specifically, when you have thousands and thousands of users running your application that hits agents or hits some LLM at some point, your simple prompts don’t need to hit expensive models. For example, you don’t want Opus 4.6 to run with high reasoning when you can just have Claude IQ respond or Sonnet respond. There’s a great breakdown of a highly intelligent model, a medium intelligent model, and a not so intelligent but very cheap model. And we can go even cheaper than this. Of course, there are cheaper, frankly better models than Haiku, but just as an

[00:05:01] example, this is a common problem you’re going to want to solve when building with LLMs. You don’t want to pay for intelligence that you don’t need. So, what we have here is a prompt complexity classifier. It’s very simple and we can run it right now. So if we open up a terminal inside this codebase and we type J for just, we’re of course using just file for quick commands and J is an alias for just to be super clear. We can do just predict summarize this codebase. And now our system should predict that only a medium or a low tier model is needed to satisfy this prompt. If we hit enter here, you can see our classifier running. And you can see our label is medium. There’s all the confidences out of 100 or 100%. This type of prompt would route to claude sonnet 4.6 or whatever your medium tier model is set to. So that’s the problem and that’s what this code base is. But of course, we’re not building this. We are having our agents and we’re teaching our agents how to build it. And in this case, we actually have an entire agent team that knows how to run this codebase better than anyone.

[00:06:01] I was a lead data scientist. And I would spend a lot of time building out these classifier models by hand. Now, we can just have agents do it for us. So, I want to showcase one of the advantages of having teams that you work with instead of just individual agents, especially individual one-off agents. So, let’s go ahead and ask our teams a question. What we’re going to get here is a multi-perspective answer. So I’ll say ask all teams what are two additional scikit-learn classifiers we can test our current classifier against and so once again we’re entering that chat pattern always only talking to our orchestrator and then our orchestrator decides who it needs to delegate the work to. I asked a question it thought it’s delegating work out to its individual team. So you can see there it wrote the exact same prompt to every single team. It’s very important. It’s very consistent and we’ll showcase how it’s able to do that in a moment. The individual team leads have taken over. So we have our planner, we have our engineer, and we have our validator. It’s completely up to them whether they decide to delegate additional work.

[00:07:00] Looks like the engineering lead is delegating work to the backend dev. Our validation lead here has delegated to the security reviewer. QA engineer is there. You can see all the tools that’s running. Planning lead is running. So on and so forth. And so these are all going to run and put together all the results. So the flow here is orchestrator talks to the leads, leads talk to the workers, and then we get one unified response. We are conversing with our orchestrator and no one else. So this is super super important. Let me point something out here. All three teams weighed in. There’s strong consensus. This is what you want to see. Linear SBC calibrated classifier CV unanimous pick across all teams. Digging in further, split recommendation. They both favor compliment NB. engineering prefers SGD classifier and then everyone agreed to skip certain things. This is a chat experience and so one of the key elements of that is that every agent can see the conversation. Not to get too ahead of things here, but if I come to

[00:08:01] the session ID and I just search for this and I go to the conversation, you can see the entire conversation that’s happened so far in a JSONL file, you can see all of the leads and you can see the devs. I point this out just to say that every agent is aware of the conversation happening. And then our orchestrator starting to guide the experience. Want me to have engineering draft and implementation for the benchmark. So let’s go ahead and have our team build this out. And I want to utilize the team structure that we’ve built here to get better results. So how can we do that? Let’s do this. I’ll say plan engineer and then validate. Make sure to add just commands to test both models. And we’ll call it just prompt both. All right. And then we’ll fire that off. So plan engineer and then validate. Our orchestrator agent is going to write out a great detailed plan and then our lead is going to dive into things. So you’ll notice here really interesting thing that my plan lead does. It first always gets all the context. We are not afraid to spend to win here. We’re not afraid to give our agents all the relevant context they

[00:09:01] need. And this is something critical we talked about in our previous week’s video, the CEO agents. We talked about that key idea of giving your agents more context. use that powerful 1 million context window model inside of your Opus and your Sonnet models so that they have all the information they need to succeed. This is a massive advantage you can be building into now as long as you’re not afraid to spend to get the advantage. Something else important here to mention is why am I building and sharing a system like this with you? You always want to be thinking about where the ball is going, not where it is. We have a couple converging trends that are super super important that this application embodies, right? We have incredible intelligence with increasing context windows. Right? We have the new 1 million context window here from Claude. This is very very powerful and it unlocks a new frontier of what is possible. And then as you’ll see here in a moment, we can specialize our agents even further by making them agent experts. And then we’ve scaled that idea thanks to a customizable agent coding tool. We are using the PI agent harness

[00:10:00] to customize this entire experience. So we have powerful context windows. We have agents that learn. Let me be super clear about this. As you’ll see, every one of these agents, they have a working mental model about how things work. And this grows over time. Every time you run your team, they’re all taking notes. They’re all building up their mental model. And then they’re loading it at the beginning. As you can see here, a lot of read tools happen in the beginning. The the book here represents the read tool calls. And that’s because they’re always loading the conversation and their own specialized mental model. So we have large context windows coming in at cheaper costs. You’re going to have so many tokens available to you. The question is are you using them? Then we have agent expertise. Thanks to the context window, you can really load up the specialization and the memory of every single specialized agent. And then of course we have the pie coding agent, the agent harness. You can build customized agentic experiences. Not just agents, not just teams of agents, but the entire agent harness can be customized when you’re using a tool like

[00:11:00] PI. So, I hope you can see when you stack these things together, we’re getting far away from the normal distribution of results. It all starts with the py file. Unlike Claude where you have agents, prompts, skills, so on and so forth. When you’re customizing the agent harness, as we mentioned in last week’s video, CEO agents, you can customize the experience down to the folders, down to the individual files. Everything is customizable here. With that, we’ve customized the experience to look like this. We have a multi-team config. I really like this configuration-based approach because it means at any point in time, we can just copy our config, change up the teams, and specialize how we need. We have our orchestrator. We’re passing in a path to the system prompt of the orchestrator. We’ll break that down in a moment. And we have individual teams. If we jump into planning, we have our lead specified. It’s just a system prompt and a name. All right. Then we have a couple additional properties. just the team color and then we have the members and we’re repeating the pattern.

[00:12:01] We’re giving the path to the system prompt of the agent and this pattern is going to repeat for engineering. You can see there’s the engineering team front end backend that I’ve just copied the kind of default template. We don’t need a front-end dev for prompt routing. This is a data science backend focused type of codebase but nonetheless we have a front end dev there. We can get rid of that. We can change that whenever we want. Three key nodes of building software planning engineering and validation. So the pattern repeats nice and simple. The whole idea here is I’m setting up reusable, customizable systems that are easy to change and update. That’s the configuration file that puts all the teams together. And you can see here we have a couple of key paths where the agents are, where the sessions are, and where some of the logs are. Let’s go and open up that orchestrator, right? What does our orchestrator look like inside this system? We have our name, our model. Of course, for the orchestrator, we want to use the best intelligence available so that it coordinates everything properly. And then here we start deviating from the normal out of the box agentic coding experience you’re probably used to. We have expertise. Our orchestrator has a

[00:13:02] mental model of everything that’s happened. It has complete control over it. So it’s updatable. And we have max lines. We don’t want this to grow too big. Frankly, with a million contexts available, this could be much larger, but 10,000 lines is still a ton of context. Here we have skills. You’re likely used to this. The interesting thing here is that let’s say we open up our engineering lead. Our engineering lead also has zero micromanagement. So you can see the zero micromanagement skill is shared between the orchestrator and all the leads. The whole idea is don’t micromanage. Delegate the right work at the right time to the workers. And for the orchestrator, delegate the right work to the individual teams. Okay. So this is very powerful. We are sharing skills when necessary. You can see here we also have a conversational response skill and this is shared between every other agent. If we open up our backend agent here, it does not have that conversational response skill because we want our back-end dev, we want our developers, we want our builders to be very very verbose and very detail oriented. But when we get to

[00:14:00] our leads and our orchestrator, we don’t really need that. We have the tools block here. And you can see the special skill that our orchestrator has is the delegate. And our lead also has delegate because remember this is a three tier architecture. We have an orchestrator, leads, and workers. And then here comes something really powerful. We have domain. So this specifies what the agents can and cannot do and where they can and cannot go. And if you’re working in a large codebase, this is very very powerful. Let’s look at our planning lead. They have read access to everything. Here’s that path. Read update access to the .py directory where they can, you know, look at and write their expertise. They also have full read access to the specs directory, right? Where plans are actually written. But they can’t update it. they must delegate that work. As you can see here, zero micromanagement always use this. You’re a leader. Delegate, never execute. So the leaders aren’t actually doing any raw file changing related work unless it’s related to their mental model which is stored in the expertise

[00:15:00] directory. So this is powerful. Think about what you can do with this. If you’re operating a large large codebase, we’re talking thousands of files. You can build specific agents that operate specific parts of the codebase. This is a simple example, but my planners, they can read the entire codebase, but they can’t change anything. I don’t want them to change anything. I want them to create plans. And so, we have this in force. You can imagine this dials right into the Pi hooks system. If you’re building out something like this with Claude, although you’ll be a lot more limited in what you can do. You could also plug into the Cloud Code hooks capability. Pi has the exact same system. You have a bunch of hooks you can tap into. A very specialized experience. This front matter is custom-built. We’re building teams and we’re building agents that do a couple things, right? So, it has expertise. These agents remember every time this agent boots up, it’s going to load from its expertise file. And so, if we hop back to our system, let’s see what’s going on here. QA engineer is running. Our validation lead is looking

[00:16:00] at some stuff and the validation is kicking off security reviewer. Security reviewer says, “Ship it no issues.” But we can see here our QA engineer has found a couple things. And so super important, we have multiple agents working on these teams and they’re going to find different things. They all have different context windows, different perspectives, and if we wanted to, they could all be running different models. So it looks like the keyway engineer has some information that the security validator doesn’t. And if we scroll down here, the full life cycle is complete. And the orchestrator has put together the results of the system and communicated to us. To be super clear here, it is running all of these skills that it has, right? The orchestrator knows not to just give me a giant block of text back because it has the conversational response skill. Always use when writing responses. And so our agent gave us a nice, you know, relatively concise summary here. And now we have a next step of course. But we had a whole team run through this workflow planning, engineering, and validating. And so very, very powerful stuff, right? 18 minutes so far, lots of work done. And now we can run train

[00:17:00] both, we can run prompt both. So we can see how our classifiers are working. Now new terminal window here J you can see we have those new methods that are multi-team agent coding tool built summarize this codebase predict both there you go and you can see the kind of difference between them they both are routing to mid which is good they agree on this and then we can see some individual stats from our individual scikit-learn models right so very powerful stuff we can go ahead and test another prompt to see how they both perform and what we really want is to probably run this eval do you want to run head-to-head email on the hold out data set. So, let’s just go ahead and do that. Run head-to-head. Again, we’re going to see the power of locking the domain. Our orchestrator can’t actually do this work. It knows that. So, it’s delegating to engineering. And so, the lead again, lead cannot do work. It is a thinker. It’s a planner. It’s a coordinator. And so, the lead pass has that to backend. It’s going to read what we have and then it’s going to run them head-to-head. We’ll go ahead and let that run and come back to the results in a moment. And the way that you’re building

[00:18:01] your agent decoding system is the edge now. And as I talk about all the time on the channel, we want to increase the trust we have in our agents. And in order to do that, we need specialized experiences and and we want to dial things down to the agent harness. How can you build customized specialized systems that outperform, you know, normal distribution of results everyone else is getting? A great way to do that is to really know how to control the core four context model prompt tools and then orchestrate, right? Scale it. Below here is just a mostly standard system prompt with a couple of key differences. This is the orchestrator system prompt. I am injecting some variables at runtime before the agent loads a system prompt. Right? We have the session directory. We have the conversation log. And then we have the teams. So the teams is coming dynamically in from our YAML file. We have its individual tools. And then I’m also giving the file paths, right? I’m actually loading in the expertise and skills block, right? I’m taking it out of the front matter and I’m placing it inside the system prompt for the agent. Our orchestrator is acutely aware of all

[00:19:00] the skills it has. You know, it knows how to write mental models. You can see I have a mental model skill for tracking and keeping its mental model updated as well as other things, right? For instance, I have an active listener skill. This is something that you just might not want. You might not want your agent to always read the conversation log before every response. But this is something that I do want in my orchestrator. And in fact, if you just search this, you can see that I have this inside of every team member. Every single team member is, and let me just dial this into this one path here, multi-team and agents. You can see we have our 10 agents, all specialized, and they’re all active listeners read the conversation log before every response. And so by composing skills like this, we’re able to control and quickly modify the capabilities of every single agent because often times there’s shared capability you want. There could be an agent somewhere that we don’t want to always active listen. We want it to be independent and not rely on the current conversation. Easily changeable, modifiable from this multi-team agent decoding tool. This is how the system is

[00:20:01] kind of composed. Of course, we have a skills directory. Nothing really new here. Every one of these skills is modifiable. You can see everything we have there. Some are shared, some are not. We can jump into this is a classic prompt architecture. Nothing really huge to note here. But you can see here this is how agents are keeping track of their mental models instructions personal expertise file when to read when to update how to structure you know an improvement that can be made a customization a specialization you can make to your agents is teaching them exactly how you want them to write their mental models and to keep track of this. You want to be kind of careful here though when you’re building agent experts. You don’t want to be over rigid about this. Our agents are storing and maintaining their own mental models. They’re storing what they think is most relevant to their success and it’s going to grow over each session. Again, when we talk about advantages over other systems, every time I boot this up, my agent teams down to the individual workers is going to have a stacking compounding set of memory that’s

[00:21:00] allowing them to specialize further and further and further over and over and over. And so if you’re building legitimate production software, this becomes very important to always go through your team because your team is the one accumulating knowledge. This is where domain specifying becomes really really important. For example, like front-end dev, you might have something like this. And of course, your front-end dev can do whatever they need to here. So you might have this and you will probably also want them to be able to read the back end, but never touch it. They can’t actually touch this ever. One example there. And of course, we might want the exact opposite thing inside of our backend dev. So, we could paste this. And then we go back and we fix this. And now our backend engineer and our front-end engineer sit next to each other. We can look at these side by side here. And this means that our agents can both read any file that they need to, but our front end can only update and write to the front-end directory, whereas our backend can only write and update and delete from our backend directory. Over time, the specialization

[00:22:01] becomes more and more important as each member is building up their own mental model of the system. And thanks to the powerful new Claude 1 million true context windows, having expertise like this is more viable than ever. If we hop into the mental model for our back-end developer, it’s actually got information stored about its previous codebase that I had it operating in. So, it’s remembering all this. I actually need to get rid of this and tune the mental model. There’s an application that it’s tracking in its mental model. Its mental model is 5,000 tokens and this is very powerful. Last explorer there’s a session and it’s just keeping track of not detailed specific information just high-level information about what’s going on. It’s referencing the specs missing infrastructure key risks backend patterns it noticed security stuff testing so on and so forth. And so it’s just maintaining this on its own. I don’t do anything with this. I don’t touch this. And that’s one of the key ideas with the mental model. You can’t have your fingers on everything. And this is, you know, the journey that every engineer needs to make as they

[00:23:01] move toward becoming an agentic engineer. You have to let go. You have to teach your agents to operate as you would. The mental model is one of the greatest examples of this. You can give guidelines on how they should be building great mental models. We have that in our mental model skill, but we’re not going much further than that. Right? This skill is only 77 lines long. And we’re letting them maintain and update this on their own. That’s expertise. That’s our specialized agents. This is the system prompt that’s loading into every single agent. We have skills. We have our configuration file. And we also have session. So, this is a really important idea. Let’s go ahead and pull the session ID from this agent and just pull up the conversation log like we had before. You can see here we’re also storing all of the tool calls for every single team. We’re also storing the starting system prompt just so it’s super super clear. We can jump into our backend dev here and we can see exactly what a system prompt looks like with everything templated in full control. You can see super super simple, super concise. By placing this information in the system prompt, it is

[00:24:01] strongly adhering to this information. We are really detailing and controlling the core 4 down to the system prompt level. This is complete customization. If we hop back to our results here, our backend dev did the work. It communicated back to the lead and here it’s handing the results back to the orchestrator. We can see the results for our V1, our LR, and our new classifier here. All the results were written into the session. This session directory is really important so that there’s just a shared space for agents to operate on whatever work they’re doing. We should be able to find head-to-head. There’s the results and we can see the work that our multi-agent teams put together for us. You can see here we’re running our original LR model against compliment and B and we have our accuracy across. The most important part about this classifier system, you know, jumping into the individual problem itself is that whenever we need a high intelligence model, we want our classifier to be more biased toward that. It’s a much worse problem if you predicted a medium or low intelligence model when you really need it high, right? That’s just a bad user

[00:25:00] experience. It’s a bad customer experience. So, we’ve fine-tuned the experience to make sure that things are pushed to high as a safe default. So, we’re always pushing to our high, more expensive model for the customer experience. Based on the financials of your business, you might change that a little bit to veer toward mid, hopefully not low. That’s kind of the details of how I would build out a prompt routing system and how I have built out some prompt writing systems in the past. As you’ve seen here, there are many things, there are many dimensions you can specialize on. Not only are we specializing individual agents, anyone can do this with cloud code via sub-agents, we’ve actually built specialized teams. Every team has this domain and then we’re having a single interface orchestrator so that our cognitive input into the system right your effort it doesn’t need to increase as you increase the total number of specialized agents there’s a huge problem of customization unless you build it right and I think this chat-like interface is the right way to build it because here we have delegation pattern where it all starts with the orchestrator right you are just

[00:26:00] talking to the orchestrator and then the orchestrator delegates to the teams and the teams delegate to their workers by doing this, every team focuses on their specialty and every team is fine-tunable. Of course, you can see here I have all of my leads as a more powerful opus model and the workers are sonnet models. You could play with this, you could tune this however you like, but you can see the pattern very clearly, right? You want your thinkers, your orchestrator level, and your lead agents to be as smart as they possibly can. And then your workers, you can dial down. They just need to follow instructions and execute. Of course, if you’re going all out, you’ll make everything the top best possible model. I think a really important piece of this is the 1 million context window model. My backend dev’s doing a lot of work did get past 100k tokens spent and this will just continue as knowledge and domain expertise stacks up in the code base. Nice part here is you know whenever you want to you can just prune a team or a member or you can add a team. It really is as simple as updating the configuration file. Let me just quickly showcase that. we can just very quickly

[00:27:01] kill this team, hop back into the terminal, fire up the app teams, and now it’s just planning and engineering. No validation. Super powerful trend here on the channel. I really like to focus on the idea of moving where the ball is going, not where it is. We have a lot of very powerful tailwind wins available, but they’re not accessible if you’re doing what everyone else is doing. Let me share a few next generation ideas and ways that I will be and already am pushing multi-agent teams like this. Once you build an opinionated structure like this, the next thing to do is build a meta agent or meta team that helps you quickly fine-tune and improve the team. With the system, you can build out powerful things like team-based custom/commands aka skills. It’s also called skills. There’s so many names for just prompt, it’s annoying. You can build out these prompts, these workflows with your teams in mind. You know, a classic one is like plan, build, validate. Like one of the oldest workflows known to agentic engineers. You have a plan, you build and you validate just like how we wrote except as a reusable prompt. You can start

[00:28:01] really really stacking this up and getting massive advantages with a system like this. Another idea here is a lot of the value of the system comes from your agents stacking up knowledge over time. So our engineering lead for instance has its own mental model and so really fine-tuning the mental model and the mental model skill to properly track results in your system is going to be really important. You may have noticed here expertise is a list. So we can also do something like this. If you have highly opinionated knowledge you need to add to your database migration agent or your billing agent. It’s a really really important thing to have specialized. There’s specific context. You don’t want anyone to mess it up. So you can build out additional expertise that’s not updatable. Talking about that billing use case, billing workflow blah blah blah. All this context is specializable. This would create a read only memory file of expertise that only your billing agent would have. This is one example. But again, expertise super super

[00:29:00] important. Billing, migrations, DevOps, deploying. These are all great examples of specialized engineering. You don’t want a generic agent doing this stuff. You want an agent that has experience, that has expertise doing this over and over and over, just like you would trust someone, an engineer on your team who you know has done it, right? It’s the same concept. Really locking in the expertise is going to be a massive advantage for engineers that are able to unlock it and for engineers that understand why agent experts will always outperform a generic agent. Skills very very clear why this is important. It’s not a secret to anyone. Specialized tools obviously very important. You can see how much power we’ve added to the system with just one tool. Delegate. And then of course domain locking. You might have a configuration file agent or again probably better example DevOps agent. No one should be touching the DevOps files and folders except the DevOps agent. This is like the holy grail of operating mid to large-size code bases with these powerful agents that are continuing to

[00:30:01] get smarter, more intelligent while the token cost is coming down. I know a lot of engineers are going to watch this and it always annoys me, frankly. They’re going to watch this and they’re going to see, “Oh my god, $8 for this agent team. You’re wasting time in tokens communicating between these agents. You’re thinking about right now. You’re not thinking far enough into the future. Models will improve. Costs are going down. Tokens are going to be available to you at insane volumes. I think about this all the time. I’m not spending enough tokens. You are not spending enough tokens. And you are wasting time doing things the old way. And now the old way is one agent decoding tool in the terminal prompting back and forth. I really really highly recommend you push yourself to spend more tokens to do more work with agents. This is the greatest opportunity that has ever existed for us engineers. And now the only question is can you agentic code? Can you agentic engineer? Can you build systems that

[00:31:00] build systems on your behalf? This is yet another powerful agentic opportunity available to you if you want to tap into it. This is not a public codebase. I am sharing this exclusively with tactical agentic coding and agentic horizon members. This is the second of three next generation multi-agent applications and code bases I’ll be sharing with Agentic Horizon members. So let me just quick break this down as I did last week. Tactical agentic coding is my take on how to scale far beyond AI coding and vibe coding with Agentic Engineering so powerful your codebase runs itself. I don’t receive any sponsorships. I’ve been given plenty of opportunity to do so. All I sell on this channel is handcrafted courses I built, which frankly these take years to build to like have the knowledge and the presentation abilities to even communicate these ideas. I take them all and I give them to you in these individual courses. So that’s all I sell on this channel. If you want to support, if you like the ideas, take this course and get ahead of the pack. A lot of what you’re seeing in the industry, we have

[00:32:00] covered already in this course. And there’s information and patterns in this course that has not been covered in the industry at all yet. This is for engineers that ship. AI coding is not enough. You want to be building systems that build systems for you. This is the true gift of agentic coding. All right? You can compound your advantage by building agentic systems that then build your application, test your application, validate your application, plan on your application, so on and so forth. Right? If you’re using agents on a daily basis, this course was made for you. We talk about templating or engineering and scaling the core four to their absolute maximum. Let me be super clear about this. There are two courses in this if you want to gain access to the multi-team agent coding tool and last week’s CEO agents multi-agent codebase where we use many agents and lots of compute to help us make strategic decisions. These are available for agentic horizon members. So you need both courses to gain access but I guarantee you it’s going to be worth it. There’s a lot of valuable powerful ideas we talk about. We go through eight tactics of agent coding. We build a

[00:33:00] system that builds a system. No AGI, no ASI BS. It’s just boots on the ground engineering with agents. All right. Inside of Agentic Horizon, we push all the ideas further. The most important one is building agentic layers. And as mentioned today, agent experts for anyone who’s hesitant or not sure or maybe this is the first time you’re watching one of my videos. I only want you in this course if it’s for you. For that reason, I have a no questions asked 30-day refund before you start lesson 4. So, hop in here, get your edge. I’m going to have these code bases available for Agentic Horizon members. Phase three is coming soon. So, make sure you understand everything that’s available to you right now in phase 2 in the age of agents. Many, many lessons in here breaking down lots of ideas you’ve already seen in the industry. That’s enough for now on this. Again, link will be in the description for you if you’re interested. The name of the game in 2026 is trust and scale. What can you do with your agentic coding tools and your

[00:34:00] agentic systems to increase the trust you have in your agents to such a high level that you know they will ship the result when you hit enter on the prompt. After you do that, you scale it. How big can you go? How surgical can your agents be? Agents with specialized expertise that builds over time will play a critical role here. This isn’t just Claude capturing memory with everything you’ve ever said to it over years. That’s not going to go well. What is going to go well is specialized agents that have specific purposes and growing context windows and increasing intelligence that you have learned to specialize in your agent harness. More tools like this will emerge but the ideas will be there and then the question becomes do you know how to build agent experts and then can you build teams of experts that operate autonomously on your behalf? No matter what, stay focused and keep building.