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.