You know, Herman, I was looking through some old notes from just a couple of years ago—back in late twenty-twenty-four—and it is absolutely staggering how much our expectations for artificial intelligence have shifted in such a short window. We went from being collectively impressed that a chatbot could write a halfway decent rhyming poem or summarize a meeting transcript to now, here in early twenty-six, expecting these systems to basically run entire departments, manage supply chains, or handle complex, multi-step engineering projects while we go grab a coffee. The goalposts haven't just moved; they’ve been strapped to a rocket and launched into a different orbit.
Herman Poppleberry here, and you are hitting on the fundamental shift of our era, Corn. You are absolutely right. The jump from what we used to call generative AI—where the model just predicts the next token in a vacuum—to what we now call agentic AI is arguably the biggest leap since the original Attention Is All You Need paper back in twenty-seventeen. It is the difference between a library that can answer questions if you walk up to the desk and a staff of elite researchers who can actually go out, find the books, cross-reference the data, interview experts, and write the final report for you while you sleep. We’ve moved from "Chat" to "Do," and that "Do" part is getting incredibly sophisticated.
And that is exactly what Daniel was getting at in his prompt for us today. Daniel is a long-time listener and he is really interested in this specific shift toward agentic AI and, more importantly, the architectural move toward sub-agent delegation. It is a fascinating topic because it addresses one of the biggest bottlenecks we faced in twenty-twenty-five, which was that dreaded context degradation and the loss of coherence when you try to make one single, massive model do everything at once. Daniel wants to know the state of the union for these orchestrators—the CrewAIs, the AutoGens, and this new mainstream darling, Open Claude.
Daniel hit the nail on the head. In the early days, we thought the solution to complexity was just "bigger context windows." We thought if we could just fit a million or two million tokens into the prompt, the AI would magically handle a whole company’s worth of data. But we learned the hard way that a bigger window doesn't equal better focus. If you try to force a single agent to hold every single detail of a complex project in its active memory, things start to get messy. It is like trying to cook a seven-course Michelin-star meal by yourself in a tiny kitchen. Eventually, you are going to forget the salt in the soup because you are too focused on the souffle, and you’ll probably trip over the trash can while you’re at it. But if you have a head chef delegating to specialized sub-agents—a saucier, a pastry chef, a line cook—the whole operation becomes much more stable and, frankly, much more sane.
Right, and Daniel mentioned Open Claude as a major driver for mainstream interest lately. I want to dig into that, but also into the state of these orchestrators like CrewAI and Microsoft’s AutoGen. They have been around for a while in developer circles—I remember playing with the early versions of CrewAI back when it was just a few thousand stars on GitHub—but we are seeing a real evolution in how they are being used and monitored here in early twenty-six. It’s no longer just a toy for people who live in a terminal window.
It is a great time to be looking at this because the landscape has matured so much. We have moved past the "Wild West" phase of twenty-twenty-four where everyone was just hacking together messy Python scripts and hoping the agents did not loop infinitely and burn through a thousand dollars in API credits in twenty minutes. I remember those days; you’d start a script, go to lunch, and come back to a five-hundred-page log of two agents arguing with each other about the definition of a JSON schema while your credit card was screaming for mercy.
I remember those rogue agent loops! That was the big fear, right? The "infinite loop of doom." But let's start with this concept of sub-agent delegation that Daniel mentioned. Why is this specifically the game-changer for coherence? Most people still think bigger context windows solve everything. If I can put a whole book into the prompt, why do I need a sub-agent?
Because of what researchers call "Attention Dilution." Even with a million-token window, the model’s ability to reason across all that data simultaneously is limited. When you delegate to sub-agents, you are essentially creating a modular architecture. Each sub-agent gets a very specific, pruned set of information relevant only to its task. It’s the principle of least privilege, but for data. If I am an agent tasked with just writing the CSS for a specific button on a website, I do not need to know the entire database schema, the CEO’s five-year vision, or the marketing strategy for the next quarter. By shielding the sub-agent from irrelevant data, you prevent that cognitive load from degrading its performance. The orchestrator agent—the "Manager"—keeps the big picture, while the sub-agents handle the minutiae. This keeps the "signal-to-noise" ratio high for every single call to the model.
So it’s about focus. It’s about making sure the AI isn't getting distracted by its own vast knowledge. That makes total sense. It is like the difference between a generalist and a specialist. But let's look at the tools. Daniel mentioned CrewAI and AutoGen. These started as very developer-heavy SDKs, or software development kits. You had to be pretty comfortable with Python and environment variables to get anything meaningful out of them. Where are we now with those in twenty-six?
Well, in early twenty-six, we have seen a massive push toward what I like to call the "Democratization of Orchestration." CrewAI, for example, has really leaned into their enterprise offerings. They realized that for these agents to be useful in a business context, you need more than just a terminal window. You need observability. You need to see the logs, the thought process, and the cost in real-time. They’ve launched these incredibly robust web interfaces where you can visually map out your "crew." You can see the "Manager Agent" assigning tasks to the "Researcher Agent," and you can actually watch the "Editor Agent" reject a draft and send it back for revisions. It looks more like a project management tool like Asana or Jira than a coding environment.
And that is where the web interfaces come in. I have seen some of the newer dashboards for these frameworks, and they are beautiful. You can actually see the agents talking to each other in a side-panel, right? It’s like being in a Slack channel where all your coworkers are bots.
Exactly, and that is crucial for trust. If you are delegating a mission-critical task—say, analyzing a legal contract or optimizing a logistics route—to an agentic swarm, you cannot have it be a black box. You need to see the planning agent create the task list, the execution agents checking off items, and the quality assurance agent sending things back for revision. Most of the major orchestrators now have these robust visualizers. It is not just about writing the code anymore; it is about managing the digital workforce. You’re moving from being a "Coder" to being a "Director of Operations."
It is interesting that you use the word "workforce." It really does feel like we are moving toward a world where a solo founder can essentially manage a digital team of twenty specialized agents. But what about the stability? Daniel asked about where the growth and stability are consolidating. Are we seeing everyone move toward these platform-agnostic tools like CrewAI, or are the big vendors like OpenAI and Microsoft winning the war?
It is a bit of a split, honestly, and it depends on your scale. On one hand, you have the vendor-specific projects. OpenAI has their "Assistants API," which they have refined significantly over the last year. Google has their "Vertex AI Agent Builder." These are very stable because they are optimized for their own specific models. If you are a hundred percent in the Google ecosystem, their agent tools are incredibly performant because they can do things at the infrastructure level—like pre-caching certain context or using specialized hardware—that a third-party tool cannot. They offer a "one-throat-to-choke" solution for enterprises.
But then you lose that flexibility. If a new model comes out from Anthropic or an open-source model from Meta that is suddenly ten times better at a specific type of reasoning, you are stuck if you are tied to a vendor-specific orchestrator.
That is the classic "Vendor Lock-in" trade-off. And that is why frameworks like CrewAI and Microsoft’s AutoGen are still so popular and where I see the most long-term growth. They are model-agnostic. In early twenty-six, the most sophisticated users are running "Hybrid Swarms." You might have a GPT-five model acting as the high-level manager because of its incredible planning capabilities, but you have a bunch of smaller, faster, and much cheaper Llama-four models doing the grunt work of data entry or code formatting. You might even have a Claude model doing the creative writing because of its unique prose style. Frameworks like CrewAI allow you to mix and match the "brains" of your agents based on the specific task. That flexibility is a huge draw for developers and businesses who do not want to be beholden to one single AI provider’s pricing or downtime.
Let's talk about Open Claude for a second. Daniel mentioned it as something capturing mainstream interest. For our listeners who might not be following every single GitHub repository or AI newsletter, what exactly is the significance of Open Claude in this agentic ecosystem?
Open Claude is a bit of a phenomenon. It is essentially an open-source framework and UI that leverages Anthropic's Claude models, but it is built to be much more agentic than the standard web interface you get at Claude dot ai. It really leans into the Model Context Protocol, or MCP, which we have talked about before but has really become the industry standard in the last twelve months.
Right, MCP was a huge deal when it launched. It basically gave agents a standardized way to talk to local files, databases, and APIs without having to write custom "glue code" for every single tool.
Exactly. Open Claude takes that and makes it accessible to people who are not necessarily hardcore engineers. It has this very slick interface where you can see the agent's plan, you can interrupt it, you can steer it, and most importantly, you can see it spinning up sub-agents for specific tasks. It has become the poster child for what a user-friendly agentic interface should look like. It’s the "Pro" version of the chatbot experience. It’s where you go when you realize that a single chat window isn't enough for the project you’re trying to build.
I think that steering part is key. One of the frustrations with early agents in twenty-twenty-four was that they would just go off on a tangent, and you could not really stop them until they were done and you’d already spent five dollars in tokens. Being able to see the plan and say, "Wait, do not use that API, use this one instead," or "Actually, skip step three and go straight to step four," before it even starts the task—that is a huge leap forward in usability. It’s "Human-in-the-loop" but in a way that feels natural, not like you’re debugging code.
It really is. And it ties back to Daniel's point about context degradation. In Open Claude, when a sub-agent is created, it is given a fresh, clean context window. It only knows what it needs to know. When it finishes its task, it passes a concise summary back to the main agent. This summary-based communication is much more efficient than just dumping the entire conversation history into one big bucket. It’s like a manager getting a one-page briefing instead of being forced to read every single email the employee sent that day. It keeps the "Manager" agent’s head clear for high-level decision making.
It sounds like we are finally applying classic software engineering principles to AI. Modularization, encapsulation, clear interfaces between components. We are moving away from the idea of the AI as a single, monolithic "God Brain" and toward the idea of the AI as an organizational structure.
That is a perfect way to put it, Corn. We are building digital bureaucracies, in the best sense of the word. Efficient, specialized, and scalable. But here is where it gets really interesting in twenty-six. We are starting to see the consolidation of these SDKs. A year or two ago, there were hundreds of agent frameworks—everyone and their cousin had a GitHub repo with a new way to "chain" prompts. Now, the market is shaking out. The winners are the ones that prioritize "State Management"—the ability for an agent to remember where it is in a long-running process even if the power goes out or the API hiccups.
So who is winning that battle? If I am a business owner or a developer looking to build something today that will still be relevant in two years, where should I be putting my energy? Is it CrewAI? Is it AutoGen? Or is it something else?
If you want maximum control and you are comfortable with code, the combination of CrewAI for orchestration and something like LangGraph for complex logic is still the gold standard. LangGraph, which comes out of the LangChain ecosystem, allows you to create these very intricate, stateful flows. It is great for when you need your agents to follow a very specific, non-linear process with loops, conditional logic, and "human-approval" nodes. It’s less about "let's see what the agents do" and more about "I am designing a sophisticated machine that uses agents as components."
And for someone who wants more of a plug-and-play experience? The person who doesn't want to manage a Python environment?
That is where the web-based orchestrators are dominating. We are seeing companies like MindOS, Skyvern, and even the newer versions of CrewAI’s enterprise platform providing these high-level platforms where you can just drag and drop agents into a workflow. They handle all the back-end orchestration, the sub-agent delegation, and the memory management. You just provide the goals and the tools. It’s the "No-Code" revolution for agentic AI.
It feels like we are reaching a point where the barrier to entry for building a complex AI system is dropping to the level of building a website in the early two-thousands. You still need to understand the logic, you still need to understand your business process, but you do not necessarily need to be a systems architect or a senior software engineer to deploy a team of agents.
Precisely. But there is a hidden challenge here that I think we need to address, and it is something Daniel touched on implicitly. As we move to these sub-agent architectures, latency starts to become a real issue. If you have a manager agent that has to think for ten seconds, then delegate to a planner who thinks for ten seconds, who then delegates to three sub-agents who each have to call an API and think for twenty seconds... you are looking at a lot of waiting time.
That is a great point. If I ask a question and it takes three to five minutes for the "swarm" to coordinate and get back to me, is that still a good user experience? Or have we just traded one problem for another?
It depends entirely on the task. If it is a task that would have taken a human four hours—like doing a deep-dive competitive analysis of twenty different companies—then three minutes is a miracle. It’s a productivity explosion. But if it is something I want an immediate answer to, it is frustrating. This is why we are seeing a lot of work on "Small Specialized Models" that can run locally or very cheaply on the edge. These sub-agents do not always need to be a massive, trillion-parameter model like GPT-five. A tiny, fine-tuned model that only knows how to format SQL queries can do its specific task in milliseconds and for a fraction of the cost.
So the orchestrator might be the big, expensive, "smart" model, and the sub-agents are the quick, agile, "specialized" ones. It is like having a genius professor directing a team of very fast, very efficient grad students. The professor doesn't need to do the data entry; they just need to make sure the data entry is being done correctly.
Exactly. And the professor only steps in when something goes wrong or when a high-level decision needs to be made. This tiered approach—using "Large Reasoning Models" for planning and "Small Language Models" for execution—is really where the stability is coming from in early twenty-six. It is a much more sustainable model, both in terms of cost and performance. It prevents the "Token Burn" that killed so many early agent projects.
I want to go back to the idea of the web interface for a moment. Daniel mentioned that many of these frameworks were previously limited to developer SDKs. Why was that such a massive hurdle? I mean, developers are usually the ones building this stuff anyway. Why does a pretty UI matter so much for the growth of the whole ecosystem?
Because the people who actually know the business processes that need to be automated are rarely the ones who can write complex Python code. Think about a legal department, a medical billing office, or a supply chain management team. They know exactly how their workflow should look. They know the edge cases, the regulations, and the "gotchas." But they cannot build a CrewAI script from scratch. When you give them a web UI where they can see the agents, tweak their instructions in plain English, and monitor their performance, you unlock a massive amount of domain expertise that was previously locked out of the AI revolution. It’s about moving the "Agent Creation" closer to the "Problem Owner."
That is the real game-changer. It is the democratization of agency. If a paralegal can build an agentic swarm to handle document discovery without calling the IT department, that is a huge shift in how work gets done. It’s the end of the "IT Bottleneck" for AI deployment.
And it also helps with the monitoring and scaling. If you are running a hundred agents across a large company, you need to see which ones are failing, which ones are costing too much, and where the bottlenecks are. You cannot do that effectively by just looking at a terminal output or a log file. You need a dashboard. You need analytics. You need to see "Success Rates per Agent" and "Average Cost per Task." The frameworks that are winning are the ones that provide these "Business Intelligence" tools for AI.
So, looking at the current state, would you say we are in a period of consolidation or still in a period of rapid expansion? It feels like every week there’s a new "Agentic Platform" launching on Product Hunt.
I would say it is both. We are consolidating around a few key architectures—like the sub-agent delegation model Daniel mentioned and the Model Context Protocol—but we are expanding rapidly in terms of the vertical applications. We’re seeing "Agentic Platforms for Architects," "Agentic Platforms for Bio-tech," and so on. The underlying frameworks like CrewAI and AutoGen have survived the initial hype cycle and are now becoming the "Infrastructure" of the agentic web. They are the "Operating Systems" that these specialized applications run on.
And what about the big players like Microsoft and OpenAI? Are they going to eventually just absorb all of this? I mean, if Microsoft builds a perfect orchestrator into Windows or Office thirty-six-five, does a tool like CrewAI even need to exist for the average person?
Microsoft is definitely trying. AutoGen is their project, after all, and they are integrating those agentic capabilities into their "Copilot Studio." But there will always be a place for the independent, model-agnostic tools. Think about it like the cloud market. You have AWS and Azure, but you also have tools like Terraform or Kubernetes that let you manage across all of them. Frameworks like CrewAI are the "Terraform of the AI world." They give you that layer of abstraction that prevents total vendor lock-in. If you’re a big enterprise, you don't want to be one-hundred-percent dependent on one company for your entire digital workforce. You want the ability to move your agents to a different provider if the price goes up or the quality goes down.
That is a great analogy. It is about having a common language for agents to talk to each other, regardless of who built them or where they are running. It’s about interoperability.
And that brings us to the next big growth area: Agent-to-Agent communication protocols. It is not just about one person managing a swarm; it is about swarms from different companies talking to each other. Imagine your personal travel agent talking to an airline's booking agent, which then talks to a hotel's concierge agent. They are negotiating, exchanging data, and confirming details in the background while you’re just sitting there.
That sounds like the original promise of the "Semantic Web" from twenty years ago, finally being realized through LLMs. We don't need the whole web to be formatted in a specific way if the agents can just "understand" the unstructured data.
It really is. But instead of needing perfectly formatted XML or RDF data, these agents can just talk to each other in natural language or through these emerging standards like MCP. This is where the real growth is going to be over the next year. We are moving from isolated agentic systems—what I call "Agentic Silos"—to an interconnected agentic ecosystem.
It is fascinating, but it also sounds a bit chaotic. How do we manage the security and the permissions in a world where agents are talking to other agents? If my agent is talking to a third-party agent from a company I’ve never heard of, how do I know it is not leaking my private data or my credit card info?
That is the million-dollar question for twenty-six. We are seeing a lot of work on what we call "Guardrail Agents" or "Inspector Agents." These are specialized sub-agents whose only job is to monitor the communication of other agents. They act like a real-time filter. If an agent tries to send a social security number or a private internal document to an external entity, the guardrail agent blocks the message and flags it for human review. It’s a "Zero Trust" architecture for AI.
So it is agents all the way down. Agents to do the work, agents to plan the work, and agents to police the work. It’s a whole ecosystem of specialized digital entities.
Exactly. It sounds redundant, but when you consider the speed and scale at which these systems operate, it is the only way to manage them. You cannot have a human reviewing every single message between two agents that are communicating at a rate of a hundred messages per second. You need an automated, agentic solution to keep the other agents in check.
Let's talk about the practical side for our listeners. If someone is listening to this and they want to start experimenting with agentic AI today—not just chatting with a bot, but actually building a system that delegates to sub-agents—where should they start? Given the current landscape we have discussed.
If you want the easiest entry point to see what the future feels like, honestly, check out Open Claude. It is a fantastic way to see what an agentic workflow feels like without needing to write a single line of code. You can see the planning, you can see the tool use, and you can get a feel for how to "steer" an agent. It’s a great "sandbox" for understanding the logic of delegation.
And if they want to build something more robust? Maybe a system for their small business or a side project?
Then I would look at CrewAI. Their documentation has become excellent over the last year, and their enterprise platform—which includes that web UI Daniel was asking about—is really the gold standard for managing agents at scale. It allows you to start small with a few Python scripts and then scale up to a full managed environment as your needs grow. It’s very "developer-friendly" but also "business-ready."
What about AutoGen? Microsoft seems to be putting a lot of weight behind it, especially for enterprise users.
AutoGen is incredibly powerful, especially if you are doing things that require a lot of back-and-forth conversation or "group chat" dynamics between agents. It is very good at simulating complex multi-agent dialogues. However, it still feels a bit more like a "research project" compared to the more "product-focused" approach of CrewAI. If you are a developer who loves to tinker and wants the absolute latest in multi-agent research—things like "joint probability optimization" for agent swarms—then AutoGen is your playground.
It is interesting to see how these different philosophies are playing out. You have the research-heavy approach of AutoGen, the product-heavy approach of CrewAI, and the open-source community approach of Open Claude. It is a very healthy, vibrant ecosystem. It doesn't feel like one player has "won" yet, which is good for innovation.
It really is. And I think the most important takeaway from Daniel's prompt is that the era of the "single-prompt chatbot" is effectively over for any complex task. We are now in the era of the "Agentic Loop." You give a goal, the system creates a plan, delegates to sub-agents, executes, reviews the results, and iterates until the goal is met. That loop is the fundamental unit of work in the AI world now.
And the fact that we can now see and manage that loop through these new web interfaces is what is going to make it mainstream. It moves it out of the realm of "magic" and into the realm of a manageable, repeatable business process. It’s the industrialization of intelligence.
Exactly. It is about turning AI from a novelty into a tool. And tools need handles. They need buttons. They need gauges. That is what these orchestrator UIs are providing. They are the dashboard for the AI engine. We’re finally getting the "Control Room" for our AI assistants.
I'm curious about the long-term stability. Do you think we will ever see a single, unified standard for agent communication, or will it always be a bit fragmented? Will we eventually have an "ISO Standard" for how a sub-agent should report back to its manager?
I think MCP, the Model Context Protocol, is our best shot at that. It is being backed by some big names—Anthropic, obviously, but also many of the major tool providers. It solves the most fundamental problem, which is how an agent accesses the world outside of its own weights. If we can standardize that "interface," the rest of the orchestration becomes much easier. It’s like the USB port for AI. No matter what device you have, you know it can plug in and work. We are not quite there yet, but in early twenty-six, the momentum behind MCP is incredibly strong. It’s becoming the "lingua franca" of the agentic world.
This has been a really deep dive, Herman. I think we have covered a lot of ground here, from the technical reasons why sub-agents are necessary to prevent context rot, to the current state of the major frameworks like CrewAI and AutoGen, and the shift toward these user-friendly interfaces like Open Claude.
It is a fast-moving field, but I think the core principles are starting to stabilize. Delegation, modularity, and observability. Those are the three pillars of modern agentic AI. If you understand those three things, you can navigate almost any tool that comes out in the next few years.
Well, I think that is a great place to start wrapping things up. Daniel, thanks for the prompt. It really forced us to look at the state of play in early twenty-six, and it is clear that we are in a much more mature, much more "useful" place than we were even a year ago. The "Agentic Revolution" isn't coming; it’s already here, and it’s finally got a user interface.
Absolutely. The tools are better, the architectures are smarter, and the interfaces are finally becoming accessible to everyone, not just the code wizards. It’s an exciting time to be building.
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You can find all our past episodes, including our earlier discussions on MCP, the rise of Llama-four, and agent frameworks, at myweirdprompts dot com. We have got a full archive there, plus a contact form if you want to reach out with your own questions.
And if you prefer the direct route, you can always email us at show at myweirdprompts dot com. We love getting your feedback and hearing your own "weird prompts" that you’ve been experimenting with. Maybe your prompt will be the star of our next episode!
This has been My Weird Prompts. Thanks for joining us for this look at the state of agentic AI in twenty-six. I'm Corn.
And I'm Herman Poppleberry. We will see you in the next episode.
Goodbye, everyone!
Bye!
