Imagine a team of experts. Each one is brilliant at just one thing, right? And they're all tackling a complex problem together. Now, what if that whole team was made up entirely of AI? Welcome back to the Deep Dive, everyone. Today, we're digging into something pretty fascinating. building these AI dream teams. They're called multi -agent systems. Exactly. And for ages, this stuff felt like it was only for, you know, hardcore developers,
deep tech. Right. What was really cool is that new Denoku tools are making it super accessible. So that's what we're exploring today? Yep. We'll look at what these agent swarms are, how they work, the specific NAN updates that kind of lock this, and even some common traps to watch out for. Our mission today. give you a shortcut to understanding this really powerful new way to
automate things. Let's get into it. Okay, so for a long time, this idea of a multi -agent system, you know, a team of specialized AIs working together, it really felt like the holy grail. Totally. Super complex, very technical, felt out of reach for most people, honestly. Unless you were deep in the code. But, and this is the exciting part, recent updates in NAA and the automation platform, they've completely changed the game. How so? It's just intuitive now. Visual.
Yeah. Accessible. You can actually build these really sophisticated AI systems without writing code, like zero code. Wow. It's like getting a new superpower for your work. Seriously. Orchestrating complex stuff that was just impossible before without a dev team. That does sound like a big shift. So is this really truly a game changer for making AI more accessible, or are there still big hurdles if you don't code? Oh, it's absolutely
a game changer. Think about it. Before... You needed serious coding skills, messing with APIs. Right. Complex integrations. Exactly. Now it's drag and drop, visual. NAN basically democratized it. So these advanced AI capabilities. Are now open to everyone. Builders, creators, entrepreneurs, no code needed. You're essentially building a whole brain trust, not just using one single AI brain. Okay. Let's unpack that core idea then. An agent swarm. It's not just one big AI. No,
definitely not. It's more like a coordinated team. You use the Ocean's Eleven analogy. Huh. Yeah, I kind of like that. Everyone has a specific role. So who's who in the AI version? Well, at the heart of it, you've got the orchestrator. That's your Danny Ocean, the leader. Okay. Its main job isn't doing the work itself. It analyzes the big problem, figures out the mission, and then delegates tasks to the right specialists. So it's the manager deciding who does what. Precisely.
And then you have the sub -agents. The crew members. Exactly. Your specialists. You might have an email agent just for writing and sending emails. A calendar agent for scheduling. A web agent for doing internet research. Each one's a master of just one thing. And breaking it down like that, it offers big advantages. Massive advantages. First, you get higher quality outputs. Because they're focused. Right. Each agent gets really good at its one job. Much better results than
one general AI trying to juggle everything. It helps get around some of those LLM limitations when they spread too thin. And I imagine that leads to less prompt complexity, reduced prompt bloat. Spot on. Instead of this giant, like, 500 -line prompt for one AI. Trying to guide it through everything. Yeah. You have smaller, cleaner, much more focused prompts for each specialist. Easier to write. Easier to manage. Easier to tweak. Okay, that makes sense. And debugging
must be way simpler, too. Well, it's for sure. If emails are failing. You check the email agent. Exactly. You know exactly where the problem likely is. Narrows it right down. Nice. And finally,
there's more flexibility. you can actually use different ai models for different jobs inside the same swarm oh interesting yeah maybe a cheap fast model for like quick web searches but then a really high quality premium model for writing perfect emails or reports so you optimize for performance and cost you got it okay so if you had to pick what's the single biggest advantage of using these specialized ai agent swarms the single biggest I'd say it's about reliability
and predictability for complex tasks. Well, you're overcoming that generalist problem with large language models. By giving an AI one narrow focus, it performs that task more reliably. So you can chain these specialists together for complex, multi -step things that a single AI might just fumble, you know, or cost a fortune to run reliably. Focus boosts reliability. Right. Focus leads to higher quality, easier management, and flexibility.
Perfectly put. And this whole shift, making this accessible, you said it was really unlocked by N8n version 1 .103 and onwards. Yeah, those specific updates were key. Before that, trying to build an agent swarm was... Well, clunky is a nice word for it. Hi. Subagents lived in separate places, these subworkflows. Like managing a team where everyone's in a different building. Exactly. And you're running between them. Debugging meant juggling loads of browser tabs, trying to follow
the logic across disconnected bits. It was a headache. But now. Now you build the entire swarm orchestrator, all the subagents. It's all in one single unified workflow. All on the same screen, visually. Yes. You can literally see the data flowing. Watch the orchestrator hand off a task, see the subagent pick it up, see the result come back. That transparency must be huge. It's what finally made no -code agent swarms practical. Two -sec silence. No! I mean,
imagine scaling that. A thousand queries, a million, all orchestrated visually. That's the potential here. That unified view definitely sounds powerful for troubleshooting. But does building these still need a different way of thinking compared to, say, standard no -code stuff? How does seeing it all together change how you build and refine it? Oh, it completely transforms it. You go from this disconnected kind of guessing game. Trying to figure out where things broke. Right, to this
holistic, intuitive process. You see the whole conversation, the delegation, what each agent did, all in one place. It makes building, testing, and fixing these complex systems so much faster. And frankly... way less frustrating. Okay, let's make this concrete. Talk us through an example like that executive assistant agent swarm, something handling complex requests. Yeah, the article had a great one. A user asks, find four top YouTube videos about NAN, send the list to AI Fire via
email. Oh, and also tell me the weather in Paris today. Okay, classic multi -part request, different domains. Exactly. And what happened next was pretty slick. The orchestrator AI kicks in. It understands the whole request and breaks it down. Okay. Need weather for Paris. Need YouTube videos. Need to send an email. Three distinct tasks. Then it delegates. Immediately. Calls the web agent for the weather. Calls the contact agent to look up AI Fires email. Calls the YouTube
agent for the video search. And they work in parallel? Yep. Concurrently. Each specialist just handles its piece simultaneously. Super efficient. Okay, then they report back. Each one feeds its findings back to the orchestrator. Now it has the weather, the video list, the email address, all the pieces. Got it. Final step. The final act. The orchestrator calls the email agent, gives it all the info, and tells it to
compose and send the formatted email. Nice. And, this is important, there's a logging agent working silently in the background the whole time. Ah, the logbook. Recording every action, who did what, even the token usage. It's kind of the AI's processing cost. Right. and saving it all to a Google Sheet. Then finally, a confirmation message goes back to the user. Done. And just to clarify, each agent here is basically an NAN AI agent node, like a building block in the workflow.
Correct. Paired up with a specific tool, like the Gmail node or a web search node. Exactly. It's the AI brain connected to its specific tool or hand. Looking at that whole process, what's the most impressive part of that multi -step operation to you? Honestly. It's a seamless automation of multi -domain problem solving. Going from natural language to... To a team of specialists coordinating to get it done automatically. That
kind of adaptive, complex automation. Really hard to pull off before this multi -agent approach became accessible. Seamless, automated problem solving by specialists. So every good swarm has these core building blocks. You mentioned four key components. That's right. First, the main orchestrator agent. The boss. The Danny Ocean. Yeah. Its job is only thinking and delegating. You have to explicitly tell it. Don't do the work yourself. Critical instruction. Okay, second.
Your specialized sub -agents. Yeah. The crew. Each one of them contains narrow expertise, limited tools. Email agent only gets Gmail tool access. Web agent only gets web search. That focus is vital. Makes sense. Third. Shared memory. Think of it as the mission briefing everyone can see. How does that work technically? Usually it's something like N8N's simple chat memory database. Use the same session ID for a user's requests so all agents can see the conversation history.
So they have context. Exactly. They can collaborate effectively, remembering what happened earlier in the interaction. Super important for follow -up questions. And the fourth component, the one you said is often overlooked. The logbook. Your black box. Absolutely critical. Why so critical? Because you need to track everything. Every action by every agent, who did what, when, how many tokens it used. Logging into something like Google
Sheets gives you transparency. It's essential for debugging, auditing, just understanding what actually happened. Right. You mentioned that. So why is logging so crucial for these complex systems specifically? It seems like extra work. It might seem like it, but it's your black box, your flight recorder. When things go wrong and they... Sometimes you need that detailed log. To trace the problem. Exactly. You can see the
exact sequence. Orchestrator thought this, delegated to that agent, the agent tried this, got this error. Without the log, you're just guessing. It lets you understand the AI's reasoning and pinpoint failures. You're flying blind without it. Okay. That makes a lot of sense. So for someone listening who's thinking, all right, I want to try this, how do they start? Step one. Step one. Set up your orchestrator in NAN. You literally just add an AI agent node connected to your chat
model, like OpenAI or Anthropic. And the crucial part. The system prompt for that orchestrator. This is probably the most important piece of text you'll write because it must tell the orchestrator to delegate, not to do the tasks itself. The article is a good example. Something like, you are an advanced assistant. Your job is to route queries to the right tool. Do not write emails or summaries. Your sole job is calling the correct
tool. very explicit you have to be and another pro tip yeah always stick the current date and time in there dynamically use that eval why is that important again because ais don't know what time it is otherwise they're stateless they live in a weird timeless void unless you ground them got it crucial detail okay step two build the first specialist yep your first sub -agent Add another AI agent node, but this time you set it up as a tool that the orchestrator can use.
Okay. Give it a really clear name, like email agent, and a clear description for the orchestrator, like use this agent to send emails. Needs recipient, subject, and body. So the orchestrator knows exactly what it does and needs. Precisely. Then connect a chat model to this subagent too, and give it its own specific tool, like the Gmail node. And configure that tool. Critically, yes. Configure the Gmail tool so the AI can fill in the recipient, subject, and body based on the
task it got. That's where the action happens. Okay. Step three is just repeating that. Basically, yeah. Assemble the rest of your team. Repeat for a calendar agent, web agent, contact agent, whatever you need. Each one is just that simple unit. AI brain plus one tool, one hand. Brain connected to a single hand. I like that. Step four, testing and debugging. And this is where that unified NAA and Enview is amazing. Your superpower? Right. You send a test request, and
you can literally watch on one screen. Orchestrator gets it, passes it to the email agent. Email agent uses the Gmail tool. And you can see the logs. Click right into the logs, see the AI's thoughts, why it chose that tool, what info it used. Makes fixing things so much easier. And it's iterative, right? You don't build the whole thing at once. Definitely not. Start simple, test, add another agent, test again. I mean, I still wrestle with prompt drift myself sometimes
when I build these. Getting the prompts just right takes iteration. It's part of the process. Okay. And step five was adding memory. Yeah. For a real system, you connect all the agents, orchestrator, and sub -agents to a shared simple chat memory. Using the same session ID. Exactly. So they all share that short -term memory for that user's conversation. Essential for handling follow -up questions and maintaining context. Makes sense. So going through all that, what
do you find is usually the hardest part? for someone building their very first swarm. Honestly, getting that orchestrator delegation prompt absolutely right. Really? Why that specifically? It's just surprisingly hard sometimes to stop a powerful AI from trying to be helpful and just do the task itself. You have to be so clear, so directive, sometimes using strong words, to force it to only delegate. Getting that balance right takes
practice. It's the linchpin, though. Right. Getting the orchestrator's delegation punch just right. Okay. And it's normal for things to break at work? It's totally normal. Expect it. It's less like snapping Legos together. Oh. And more like training really smart but super literal employees. They will misunderstand things initially. So troubleshooting is key. The article mentioned common pitfalls. Yeah. Three main ones that pop up all the time. First, the time traveling agent.
We touched on this. They don't know the date. Exactly. They're stateless, so they might pull old info or schedule things wrong. The fix is simple but critical. Put in your orchestrator system prompt. Always. That lack of date awareness, it still seems so counterintuitive for something so smart. It does. It catches everyone off guard at first. But, yeah, got to tell it to time. Okay, second pitfall. The micromanaging orchestrator.
This is when it ignores your instructions and tries to do everything itself instead of delegating. The fix is a stronger prompt. Yep. More direct, use caps, like sole responsibility, or should never write the email, be forceful, leave no ambiguity. Got it. And the third one? The clumsy specialist. This is when the sub -agent messes up its actual task. Like, it tries to email Bob instead of Bob at example .com. Ah, so it doesn't
understand the required format. Right. The solution is to add descriptive parameters in the tool's configuration. Tell the Gmail note itself. The to field must be a valid email address format. So you add validation or instructions at the tool level. Exactly. It gives the AI tighter guardrails for its specific action. Okay. This brings up a really important point. Knowing when to actually use a swarm versus a more traditional automation workflow. Yes. This is key for being
a smart automation architect. An AI agent swarm is powerful, but it's like you said, tactical nuke level. You don't use it to hang a picture frame. So when do you use it? You use a swarm when the task is a mission. A mission. Meaning? Meaning it's complex, has unpredictable parts, needs reasoning, involves different kinds of knowledge or tools, like onboard this new client. That process changes depending on the client, right? Sure. Or plan my business trip to London.
Flights, hotels, meetings, maybe local info. Lots of variables. Needs coordination. That's a mission. Okay, so missions get swarms. When do you use a traditional workflow? Use a traditional workflow when the task is just a process. Like an assembly line. Exactly. Deterministic, predictable steps. Simple inputs, simple outputs. No complex decision -making needed. The example was type form submitted, add data to Google Sheet, send
confirmation email. Straightforward sequence. Yep. If A happens, do B, then C. No real AI reasoning needed. And using the wrong tool is bad. Yeah, using a complex swarm for a simple process is overkill. Wastes resources, makes it fragile. And trying to use a simple workflow for a complex dynamic mission, it'll just break or fail. Always choose the right level of tool for the job. That feels like maybe the biggest mistake people make with powerful new AI tools generally. What do
you see as the main pitfall there? Trying to use a cool new hammer for every single problem, even screws. People get excited about a powerful tool like agent swarms and want to apply it everywhere. But the real skill is knowing its strengths and weaknesses. When is it the perfect solution? And when is something simpler actually better? Using a powerful tool for every single task is usually the wrong approach. Matching tool to task complexity is crucial. Okay, so let's zoom
out. What does all this really mean for people building things right now? I genuinely think AI agent swarms are the next big leap in no -code automation. For real. And it's accessible now. Yeah. Thanks to platforms like NA and making it visual and intuitive, this power isn't locked away anymore. It's there for builders, creators, entrepreneurs, anyone. So the future isn't one giant all -knowing AI. No. That's kind of sci -fi thinking. The reality, at least for now,
is more interesting. Which is? It's about us becoming better managers, better orchestrators of these specialized AI teams. Like being the conductor of the orchestra. Exactly. You're not playing every instrument. You're leading the specialist to create something complex and powerful together. So the call to action here is really to think differently. Yeah. Think about those complex missions in your work or life. What could a custom AI team do for you? We really encourage
you to explore these no -code tools. See what you can build. The tools are there now. You really can build and lead your own AI team, no coding required. That's pretty amazing. Well, that's all the time we have for this deep dive. Thank you so much for joining us. Yeah, hope you found that insightful. Until next time, keep learning, keep building. And keep asking the big questions. Stay curious.