Transcription
Hello community unity. So great that you are back. Yeah, now the latest in multi-agent system called AIM. We have a brand new configuration that is simpler, better, faster, and cheaper. So let's have a look.
You know, if we go with a multi-agent system, you have to define those. You have a rigid plan and execute. You have predefined agents for planning and execution. Everything is inefficient. And even if you just copy here the plan, and you have here the next step of the chain of thought, it is not the best model possible. So let's identify our three problems and let's solve them today. Yes, absolutely.
The first one is a rigid plan. Imagine a plan is offline during the execution. It can't react to unexpected failure. So a new opportunity discovered while running here the execution agents, or think about your agent who has only static capabilities. You have predefined roles: coder, researcher. What about you need complete new capabilities? And lately, yeah, the communication, absolutely inefficient, whatever system you look at. So let's switch on the solution, and it is called AIM: Autonomous Intelligent Multi-agent Ecosystems. Couldn't be easier here. What you want is simply a dynamic, self-organizing team. You know, you don't have to code agents, you don't have to define the role of agents. Let AI do this. Hey, just define a self-evolving, adaptive system, and you're done. Beautiful.
Now, understanding that AIM is crucial because it provides you a blueprint for building more robust, more general-purpose, and truly autonomous systems. So it moves away from specialized agents towards a much more flexible system that can also tackle complex and unpredictable problems. And this is what we need in the real world because sometimes, I don't know, you are a specific agent that searches here the web, encounters a problem, and it is not able to solve it. You have to be flexible. Your multi-AI system has to be able to solve it.
So here we have the standard conversion of AIM. We have four elements. We have here the mission commander, and you think, "Ah, the orchestrator?" No, not at all. It is a dynamic planner this time. Second element is here an factory element where actors and, of course, AI agents are generated given the current need. Then we have here the interaction between our dynamic actors, and finally, the complete communication protocol. We have multiple protocols. Everything goes here to a live mission dashboard. Dynamic, adaptive goals and sub-goals are available for the operating team and for the dynamic planner. Let's start.
Yeah, you can have, of course, your visualization here by the official publication team of today. But hey, don't you prefer my own visualization? So let's go, and let's take here a very simple example. You have in the paper that I show you in a second here also a lot of formulas, but I want to give you a real-world example so that you get a feeling of this. So what do we do? We ask AIM to plan a sweet, budget-friendly trip to Tokyo. So let's do this.
First step, as I told you, the dynamic planner, the mission commander, the central intelligence in planning and adaptation. Not the hero that goes out on the battlefield with weapons. No, stays behind and is just thinking. So what is the task? The mission commander here, the dynamic planner, has here two strategic outputs to generate. At first, a strategic output updating here the mission board. If an agent reports back from the battlefield, let's say, "No, it says," or from the planning here of the mission, "All direct flights to Tokyo on the target date are booked." The commander says, "No panic. I have an idea. I just update here our, if you want, main mission board." And now we say, "Okay, we're looking for alternatives. Add a new high-priority objective like investigate the Shinkansen, the bullet trains, from Osaka to Tokyo." Yes, you guessed it. You were thinking from your continent? No, but we are here in Japan, traveling from one Japanese city to another Japanese city. So you see what it is doing. It is dynamically adapting here the targets. So we have a high-level strategic shift given we have an intel report back from the battlefield, from from the travel reservation. And then we have tactical output, much closer time, closer to what to do next. So this is the immediate command that the commander gives here over the radio, I mean, over the communication signal. And at the same moment, they update here the board. The commander issues here a direct order: "Dispatch now a specialist immediately to research now the Shinkansen, the bullet train, timetable, costs, conditions, whatever." You see, easy. We have a dual output model that is, if you want, at the heart of its adaptability. It focuses on strategic replanning, dynamic adaptation with immediate tactical actions that follow immediately afterwards, completely eliminating, if you want here, the planner being idle bottleneck of the old system. No static documents, everything is real-time.
The second step is now the actor factory where we generate our agents. Now, where does the commander, the command to get us Shinkansen specialist here? Okay, but who is reacting? No, no, this system has a mission specialist generator. So, this is a software system that can create you the perfect, bespoke expert for any task instantly. If you call it here, or the authors call it here, an actor factory. And this is now the element that receives here the subtask from the commander, from the central planning intelligence, "Research a Shinkansen timetable and everything." And now they start to build an agent from scratch, specifically for this single subtask. In reading this paper, I thought this is interesting. Why we do this? Normally, we have a complete web search agent or complete reasoning agent. But you remember, with chain of thought, we reduce the complexity. We break down a complexity into multiple sub-complexities with a less complexity level. So we make it easier. And this is exactly what we are doing now with agents. And the author of today's new publication said, "This is the idea we follow. We build now a one-use agent here from scratch, particularly for one single job only." But therefore, we have to adapt the communication protocols. This is what's so nice on this paper. So, process is like loading here custom software packages into a blank drone, into a blank communication expert here. No.
So what's happening in parallel is now one, the toolkit selection, and the dynamic prompt generation that this agent is generated here with. At the core, an LLM, and memory, and connectivity. Let's talk about the toolkit selection first. It doesn't give the agent every tool imaginable. Nonsense. So it says, "Okay, you have an absolute specific, needle-down-to-the-point here task, and you only get what you absolutely need." So what you get is your web search facility and your data extraction tool bundles. And you see, it's not anymore single tools, but they already found clusters which they said, "What do we need for web search?" And we put everything here in a tool basket, in a tool bundle. And not anymore single tools, but tools that go together, they need each other. Web search, data extraction, nothing else. It's absolutely tailor-generated for a single subtask. And then we have an operational prompt that gives here the new actor its entire identity. And the first is the persona. Then we have to add the knowledge and the format of the output. And if you have seen my last video, you know persona. Oh, persona can be so difficult. This can be dangerous if you get it wrong. Eh, so just a general system prompt. Huh, you're a travel logic expert specializing in Japanese rail systems. You see, absolute focus. Your goal is to find the next, the most efficient and cost-effective Shinkansen route from Osaka to Tokyo at a specific date for a specific price, for a specific amount of people, regarding further boundary conditions of the system. So absolutely specific agents. Here is now the link to the official JR rail pass website. And as a foreigner, you have discount passes, must be booked in advance, and maybe outside of Japan, if I remember correctly. And and and have here a JSON format output. Simple.
This actor factory, this agent now, and here the static agent capability problem. The system power is no longer limited by its predefined role of its agent, or does it have access to 10 agents, 20 agents, 50 agents? This system now can generate hyper-specialized experts on demand, ensuring that every subtask is handled by an agent with the exact persona, with the exact tool set or tool bundle, and the knowledge required for peak performance. But please note, this is only the absolute minimum set of it. You don't want a higher complexity. You don't want a general agent that hallucinates into the blue. You want to keep it as small as a minimum as possible. Cheap, fast, efficient, minimum. Remember, it's more or less the same that we did when we reduce the complexity by introducing a linear chain of thought, the definition here of subtasks for the agent, when we had here the planner agent breaking down the complexity levels. It is just a continuation now, more or less on the operative side. And now we have them. Now we have for specific tasks, we have created dynamic actors, hyper-specialist agents, and they now interact. They now perform their job. So if you want, this newly created Shinkansen specialist is now an autonomous field drone, uh, I mean, an autonomous field operative in our network, deployed to execute a specific mission-critical target. It's not a mindless drone. It's really with the right amount of intelligence for the job to be defined. It is already defined. Yeah.
So let's have a look. You know what it acts on the react loop. This is we know for, I would say centuries now, but at least for 10 years, we have this typical react loop. This is was invented before we had agents. So it is all the same stuff. You see, we just applied here on different levels, on different complexity, on different configuration, but AI, there's nothing new right now. Okay, React tool. So, React, reasoning, and action, but its toolkit includes here also. And this is now an innovation, if you want, an update progress status tool, a communication tool. And you remember the fourth element was this huge whiteboard. This is now here a tool that communicates. This is essential for the communication to the planner and to all the other agents. So crucially, this actor LLM decides for itself when to use it, when to send a status here with this particular message on its task. So this dynamic actor, or dynamic field operative, is now, if you want, this is kind of a monologue I imagine during the execution. The reason is, okay, I've checked here the main booking site of JR Japan, of here the railway connectivity, and there's a direct, you know, the Shinkansen have names. There's a direct Nozomi train that is the fastest, and it's a key finding. And it acts now, and the action that it takes here as an agent is it sends out here an update progress report. It phases this out, says, "Okay, my message is, I have found here the perfect connectivity, the perfect the Shinkansen here. This is the optimal route." Great. Now, reason, act, next loop. Now checking the seat availability. Oh, interesting. The site says here, "Major track maintenance work on the day causing delays." This is a critical obstacle. You see what happens? It sends another update progress report. So whenever it would hit a roadblock or a success flag, it reports immediately back. You don't wait now till the complete task is finished. And the task consists of 10 subtasks. No, you have immediate communication from the battlefield to the, from the, you got it. You know what I mean? So it says, "Okay, alert: found track maintenance on this particular date. Investigate impact here on the travel time." And this means that the planner now has, if you want, on the real second, an update exactly what is happening, what is working with the agent, and what is not working. Because this little agent, remember, it's a highly dedicated agent, but it is not that intelligent. It's just given a little bit of knowledge, a little bit of information, and an absolute focused task. So now the planner in the central command center has now to update the strategy and the mission targets, and maybe generate a new agent to solve this problem. Or, if you want, in general, you have here the reasoning and action, here another abstraction. The reason is, "Hey, I found three potential hotels to fit here the budget in Tokyo." And this is a significant milestone. I should report is here, we have a successful flag that we encountered. And the action is now here the update progress. So we have status in progress message is now, and we broadcast this here, shortlisted three hotels ABC. Now checking the reviews. So you have exactly on all subtasks, on all substeps, you know exactly whenever there is a roadblock encountered, and there's immediately an action. If you want, the key innovation here is really here the actor's toolkit that you augment here with a special system tool, and this is this update progress tool with the status and the message that is broadcasted out. So you have, if you want here, this communication line between the mission commander and here the autonomous field operative, here real-time stream of the complete intelligence from the planning field. So the commander isn't waiting here for a final success report, isn't sitting around here in the office and waiting for the first reports coming in. They know exactly every second what is happening in the multi-agent system.
And now let's come to the main information hub, to the live mission dashboard, to the progress management module, where everything is dynamic, adaptive goals, sub-goals, everything is visible for everybody, for each and every agent, for the planning central intelligence. This is here a simple memory that is available on a very fast interface for everybody. So if you want, this live mission dashboard, or whatever you want to call it, is here at the heart of the command center. It is the single source of truth that every component here, that every field operative, every strategic analyst can see and interact with. No, it's not just a to-do list. It has a rich, structured, and synchronized representation of the entire mission. Let me explain this further. So at the upper of the surface level, you have your objectives, you have your sub-objectives, and multiple objectives, and you have here X. Yes, but if imagine you could click on this, and you would get here detailed reports to the second tier report, what is happening, what is the current state. So we have two protocol layers. We have a real-time synchronization. This is exactly when the dynamic actor in the field pushes here its incremental updates using the update progress tool, which ticks off boxes or adds notes to the progress list directly into the dashboard. Or, and this is now a second one, we have a structured task conclusion. So whenever an actor now finishes here, an agent finishes here a subtask it was generated for, it can generate here a final report, formally updating here the global state on the dashboard. But now the interesting or the new idea is, it's not simply a report says binary yes no success failure. This gives you now a status update, a conclusion summary, gives you a lot of reference pointers to a file path, to URLs, to different hotel booking pages, or to the internet search results that were further analyzed and a saved file, let's say a Shinkansen option JSON with the detailed pricing and schedule. Everything that was found out with the real URLs. So you have absolute transparency. You can now say, "Okay, I immediately see which resources were activated, where the search was performed, on what social platforms I had was looking for feedback, where did I let's say book or had a pricing or schedules." You have all of this information available in this module. If you want, is really here the counterattack to the inefficient communication you have to the classical multi-agent configuration. There's no context lost between the agents because all agents read from and write to the same whiteboard. And you might say, "Well, there's a pro and cons." No, if you do not have a swarm intelligence communication, but you have here a central communication whiteboard. Yeah, the positive is that the dynamic plan always has a complete and up-to-date view on the project, eliminating any redundant work and enabling intelligent, context-aware decision-making. And you can respond within milliseconds. So whatever your data streams that come in for particular objects, you always have a live graph with all the complexities, with all the search results, with all the pointers to the URLs, with all the optional, with all the other graphs and subgraph routines that were executed. You have all the information available, and every other agent and the or tell us now that this they hope that this destroys now the information silos. No, there's no handoff between the agents working here from the same live data or whatever. Whenever the command or the central intelligence agent assigns it the next task, a new specialist agent instantly is maybe generated, or at least has access to the previous agent summaries, to the previous agent files, and the previous agent generated links, ensuring here perfect continuity and zero redundant work. So the idea is nice and absolutely simple to implement. And if you want, this is a very elegant, robust, and explicit memory for the entire system.
Now, of course, you understand if you have multiple parallel operations, sometimes you have to think about how you plan it that you don't have to wait for one operation to finish before you can kick off the next one. But especially if you have a more complex task structure, the current planning LLMs, they have some trick up their sleeves how they can kind of circumvent here any redundancy. Yeah.
The main paper of today is ByteDance. What a coincidence. July 16th published July 17th, 2025. AIM: Towards Fully Autonomous Multi-agent Framework. Fully autonomous, self-learning, self-deploying, no human in the loop. Do you have an idea on the application domains? Maybe it's not only an application domain in booking here travels in performance. Does it work? Three benchmarks: GIA, SVBench, Verified Web Voyager. Have a look. Yeah, better than everything else that we currently have.
Okay, now I mentioned already, there's this idea with the communication protocol. We have also the swarm intelligence, no, and end-to-end communication protocols. If you want to see here the difference, I have a particular video for you where we have a real swarm AI implementation also in a multi-agent AI configuration, and we will look here to theoretical physics to implement here even an optimization. So you see, you have all possibilities. It's really in your, not in the battlefield configuration, in your AI task, is you have now to ask, is there any possible of interference? Is how robust, how backup I have to generate here some communication protocols that the unit in the field would continue to operate with maximum performance given here particular task assigned to particular agents. So you absolutely understand. Yeah. Really interesting.
Just removed from two videos ago where we're talking what Google is doing here with Titan AI, and Google is extracting here the planning here from the Titanis and trying to use this as a template, as a reasoning template for the very small language model, you know, the 1 billion language model. Now, within two days of research, we moved now that the plan that looked like here four lines of textual, of verbal description, what to do, is suddenly now in today's video when we analyze in real-time problems and immediately alternatives within seconds executed. This is now also, if you want, a plan you could save, and this could be a solution template with a little bit higher complexity, as you see, if you compare it to these four lines here of text that you could also transfer now to operational units in the field, and you say, "Hey, if you encounter something, and maybe we lost the communication to the main central command, this is here a plan how you can continue on your particular mission." So you have kind of a, it's not a fractal copy, but you know, you have a guidance here also for other units that you might employ, and they understand here the operation, the logistic operation that they are integrating themselves into.
Summary: I think real nice architecture, real simple, elegant. You don't have to be extremely complicated. I haven't shown you a single formula because it was so simple to explain. But we solved, or the authors solved, some critical failures of our traditional multi-agent systems where we have rigid plans, static rules, and kind of a non-secure or a broken communication. If one element has a problem, or we have a timeout here in the communication line, line of sight, no, this falls apart, and this is not acceptable for us. So we re-evaluate our core assumptions. We need to stop obsessing here, creating here, as a human, the perfect, infallible initial plan. We don't need this. If we say AI has an intelligence, then put this intelligence to work. No. The most important question we should ask ourselves is not anymore, "Is our plan that we, as humans, think before everything starts, is this the perfect plan? Is this the correct plan?" Nonsense. Just give it a self-loading algorithm and say, "Hey, here, this is a quick and dirty solution. You're resilient. You're brilliant. You have all the capabilities you need. Self-evolve."
So, how resilient is our multi-agent system when the plan that we humans sort of had is wrong? If the conditions in the battlefield, and if if the conditions in the booking conditions moved on, if you want to go with the mission control analogy, and no, we don't want to do this. So here we have it now, very easy, just four elements with a multi-agent system. So we have here the commander, not the chief sergeant going out on the battlefield. A dynamic player, a mastermind that has access to the factory where new actors, that are AI agents, can be created for every single simple subtask. I don't know how many you need. There is minimum risk of hallucination, minimum risk that something can go wrong because the complexity is ultra low. Even today's LLM should be able to perform the task without any major complications. Then you have the dynamic actor acting on their specific, not target, on their specific task. Yeah, task. I have today, I have some problems. Okay. And then everything is communicated here to the main progress management model, to the huge whiteboard, everybody can see. So great immediate response, very low redundancy. If something is happening, immediate solution found. What a beautiful idea, simple, elegant. Have a look at the paper. I highly recommend it. And if you like these kind of videos, hey, why not subscribe? And I see you in my next.