Avoiding the n8n AI Agent Trap
Key Points
- The speaker addresses a common frustration: non‑technical users want to build custom AI agents without deep coding, finding tools like LangChain too complex and out‑of‑the‑box platforms too limiting.
- While visual workflow tools such as N8N (referred to as “NAD”) empower creators by democratizing automation, that same flexibility often becomes a “complexity trap” that leads to tangled, hard‑to‑maintain agent implementations.
- Real‑world examples show organizations ending up with hundreds of poorly maintained agents, high costs, and dependence on the original builder, highlighting the need for scalable best‑practice patterns.
- The video promises a “Goldilocks” approach—providing a middle ground of customizable yet manageable agents that non‑programmers can build reliably without sacrificing maintainability.
Sections
- The Hidden Cost of DIY AI Agents - The speaker warns that beginners who gravitate toward highly composable low‑code platforms (like N8N) can quickly amass dozens of fragile, costly agents that break, become unmaintained, and drain resources.
- Simplifying Enterprise Workflows with JSON - The speaker argues that adopting JSON‑based workflow representations and treating the platform as a collaborative hub can make automation reliable, simple, and scalable, citing StepStone’s 25× speedup in API integration as proof.
- Simplicity, Refactoring, and JSON Workflows - The speaker emphasizes that engineers refactor code to keep systems simple, maintainable, and scalable, and recommends defining workflows in JSON and using LLM‑generated documentation to preserve clarity and ease of future maintenance.
- Microservices: Simplicity Through Separation - The speaker stresses that true simplicity relies on composable, separated concerns, recounting Amazon’s transition from a massive monolithic codebase to microservices as a practical illustration of why modular architecture is essential for maintainable, scalable software.
- Siloed Automation Undermines Teamwork - While personal automation can save massive time and streamline predictable workflows, treating it as an individual, undocumented tool creates maintenance, debugging, and scalability issues that jeopardize team productivity and continuity.
- High‑Bar Automation with LLM‑Powered NAD - Leaders must demand solid engineering fundamentals for marketer‑built automations, while LLMs serve as accelerants that let non‑developers embed AI‑driven workflows into real business tasks through NAD.
- Balancing Accessibility and Discipline in AI Agent Development - The speaker warns that while tools like Naden make building AI agents easy, developers must still apply solid engineering principles—simplicity, separation of concerns, maintainability, and proper documentation—to achieve real ROI and avoid fragile, burnout‑inducing solutions.
Full Transcript
# Avoiding the n8n AI Agent Trap **Source:** [https://www.youtube.com/watch?v=zRr24Mku3r4](https://www.youtube.com/watch?v=zRr24Mku3r4) **Duration:** 00:24:12 ## Summary - The speaker addresses a common frustration: non‑technical users want to build custom AI agents without deep coding, finding tools like LangChain too complex and out‑of‑the‑box platforms too limiting. - While visual workflow tools such as N8N (referred to as “NAD”) empower creators by democratizing automation, that same flexibility often becomes a “complexity trap” that leads to tangled, hard‑to‑maintain agent implementations. - Real‑world examples show organizations ending up with hundreds of poorly maintained agents, high costs, and dependence on the original builder, highlighting the need for scalable best‑practice patterns. - The video promises a “Goldilocks” approach—providing a middle ground of customizable yet manageable agents that non‑programmers can build reliably without sacrificing maintainability. ## Sections - [00:00:00](https://www.youtube.com/watch?v=zRr24Mku3r4&t=0s) **The Hidden Cost of DIY AI Agents** - The speaker warns that beginners who gravitate toward highly composable low‑code platforms (like N8N) can quickly amass dozens of fragile, costly agents that break, become unmaintained, and drain resources. - [00:03:58](https://www.youtube.com/watch?v=zRr24Mku3r4&t=238s) **Simplifying Enterprise Workflows with JSON** - The speaker argues that adopting JSON‑based workflow representations and treating the platform as a collaborative hub can make automation reliable, simple, and scalable, citing StepStone’s 25× speedup in API integration as proof. - [00:07:31](https://www.youtube.com/watch?v=zRr24Mku3r4&t=451s) **Simplicity, Refactoring, and JSON Workflows** - The speaker emphasizes that engineers refactor code to keep systems simple, maintainable, and scalable, and recommends defining workflows in JSON and using LLM‑generated documentation to preserve clarity and ease of future maintenance. - [00:10:47](https://www.youtube.com/watch?v=zRr24Mku3r4&t=647s) **Microservices: Simplicity Through Separation** - The speaker stresses that true simplicity relies on composable, separated concerns, recounting Amazon’s transition from a massive monolithic codebase to microservices as a practical illustration of why modular architecture is essential for maintainable, scalable software. - [00:15:01](https://www.youtube.com/watch?v=zRr24Mku3r4&t=901s) **Siloed Automation Undermines Teamwork** - While personal automation can save massive time and streamline predictable workflows, treating it as an individual, undocumented tool creates maintenance, debugging, and scalability issues that jeopardize team productivity and continuity. - [00:18:14](https://www.youtube.com/watch?v=zRr24Mku3r4&t=1094s) **High‑Bar Automation with LLM‑Powered NAD** - Leaders must demand solid engineering fundamentals for marketer‑built automations, while LLMs serve as accelerants that let non‑developers embed AI‑driven workflows into real business tasks through NAD. - [00:21:25](https://www.youtube.com/watch?v=zRr24Mku3r4&t=1285s) **Balancing Accessibility and Discipline in AI Agent Development** - The speaker warns that while tools like Naden make building AI agents easy, developers must still apply solid engineering principles—simplicity, separation of concerns, maintainability, and proper documentation—to achieve real ROI and avoid fragile, burnout‑inducing solutions. ## Full Transcript
Today I want to give you a key to one of
the most persistent questions that I get
in the AI space. It's about AI agents
and it's specifically I need to get
started building AI agents. I'm not
sophisticated enough. I don't have
enough code to go and do it with just
code. So they're not going to use
Langchain. They're not going to use Lang
Smith. They want simple agents, agents
they can use, and they want custom
agents. And so a tool like Lindy.ai AI
that has a lot of outof-box agents and
some composability feels limiting to a
lot of the people I talk to in this
situation and a tool like feels just
about right because it's more
composable, more customizable. You can
bring some code and so they feel like
they're in a playground. But if you are
getting started with agents, you need to
recognize that that composability, that
configurability, the power you feel with
N8N is the trap. That is the trap. And
that is why this video exists because
people get started with something like
NAND and they get so excited and the
stars come out in their eyes and they
feel the AGI, they feel the AI agents.
They know they can do all this cool
stuff and then they find out that the AI
agent breaks. They find out they have
556 of them across the business and God
knows what's happened to 332 of them and
only 50 of them are being used regularly
and it's all adding up to a big bill.
They find out the person that built the
agent went on vacation and now they're
they don't know how to fix it because
this this like drag and drop thing has
created a tangled mess and it's
impossible to see. That is all real life
examples. I have seen it over and over
again. This video is going to help you
get from I want to do custom agents to I
can do custom agents. I can use Naden.
It's a great tool, but I know how to I
know how to do it well. I know how to
follow best practices when building
agents and I don't have to be a custom
coder to do it. I have written
comprehensive guides to agents. This is
more narrowly focused. It's very
comprehensive, but it's more narrowly
focused on what I call the Goldilocks
use case. It's not I am so out of box. I
just want a pre-built agent for my
calendar. It's also not I am so
freestyle I'm a developer and I can do
anything. It's in between. It's the
people who want customizability without
committing to just a code lifestyle. So
the first truth to understand if you're
in this bucket is that NADN has a visual
workflow builder that genuinely
democratizes automation and that's part
of why people gravitate to it. For the
first time it is really genuinely
possible and this does work for
non-programmers to build sophisticated
AI agents just by dragging and dropping
little nodes into a little tree. The
second truth, which I've already hinted
at, is that the same visual builder is a
complexity trap. It has killed so many
NAD implementations. So, ironically, the
very feature that makes you want to use
it is the feature that becomes
unmaintainable at scale. But, but worry
not, I'm going to give you a way
through. I want to map out how to get
from that honeymoon phase where you
install an you you you start to build
your first node, you start to see data
flowing across the screen. It feels so
cool. It works. You feel like you've
unlocked superpowers. You want to build
10 more of them. I want to get you
accelerated through the trough of
disillusionment where you have to add
error handling. You have to add
conditional logic. That's even more
nodes. The edge cases pile up. You tell
yourself it's manageable, but the clean
workflow looks like, I don't know, the
subway map of Manhattan. It looks like
like ridiculous, like a pile of
spaghetti. At a certain point, if you
are serious and you are only using the
nodes and dragging and dropping, you are
going to get to a point where you have
12 workflows and six 633 nodes and it
fails at 2 a.m. and you're spending 3
hours debugging it and you feel like you
are in hell. It is an incredible pain.
You can't simulate inputs correctly.
There's nodes that have failed. There's
an LLM call that's different because
LLMs have updated. You have error
objects when they fail and you don't
know what that means. This is where you
can actually shift your approach. You
don't have to go down that path. You can
actually build agents that are useful.
As an example, a real company,
StepStone, it runs 200 mission critical
workflows in NADN. They achieved
approximately a 25x speed up in API
integration time. The 200 workflows that
takes focus. If you are going to do it
at the scale of a company, you almost
need to treat NN like a hub of
excellence where your product and your
marketing and your CS teams meet your
technical teams and everybody can focus
on creating reliable, simple, clear,
followable workflows. And that's
something I'm going to emphasize again
and again and again. When you are
building, make sure they're reliable,
simple, and clear. And you might say,
Nate, is this whole video about you
telling me that my workflows in NAD are
complicated? I already knew that. No,
this is about suggesting to you that you
can get farther with your workflows if
you put them into JSON representations
because JSON representations tend to
force simplicity because you typically
build them in LLMs that tend to obsess
over making workflows as simple as
possible. JSON representations of
workflows are like kitchen instructions.
They tell the chef what to make. And one
of the things that makes them powerful
is that yes, you can drag and drop a
workflow, but you can also do this
programmatic kitchen instruction thing
where you just hand the chef the JSON
workflow and say do this and it will
work. And I want to suggest that JSON
representations are useful here. Not not
because LLMs have some magic power with
JSON. and I saw that circulating around
the web and I kind of rolled my eyes.
That's not what I'm trying to say here.
It's just that JSON is a good ingest
method that this particular tool NAND
happens to understand that LLMs can
write well and that you can use as a
carrier or a vessel for the clarity of
vision that you have. JSON acts as a
simplifier for you. If you work with
claude, if you work with chat GPT to
build a JSON workflow for your N8
automation, you are so much more likely
to have a workflow that works,
especially if you are invoking the
documentation that NAD publishes about
their workflows and their tool calls
because then the LLM will know that when
it's writing the workflow and it will be
less likely to have errors. I also want
to suggest to you that if you are in the
business of building agents, it is
probably better to understand that you
are in the business of building software
even if you're not a developer. I don't
want that to scare you, but I try and
convey it honestly because I don't want
people to be surprised. Effectively,
instead of programming, you're
configuring and so it feels easier, but
it's actually going to be more
strategic. It's going to require more
thinking and intent on your part. And
it's going to require you to understand
some things that most software engineers
have built into their DNA that doesn't
come built into other job families. I am
here to bridge that gap for you so your
agents work better. Let's start with a
simplicity principle. You need to build
workflows that are as ruthlessly simple
as they can possibly be in order to
maintain them. Simple, simple, simple.
Drill it into your head. The reason why
is that simple is maintainable. This is
why engineers will tell you they need to
refactor the back end of the codebase.
And you wonder what they're doing. Well,
let me tell you, engineers out here are
nodding their heads. They know what
they're doing. They have to refactor the
back end of the codebase because they
have to make it simple and maintainable
and scalable. Simple is scalable. Simple
is maintainable. Simple is readable.
Effectively what you have with NAND and
agents is you have a combination of
function and documentation in one
format. That spaghetti code that looks
like the subway map of Manhattan that is
both the actual diagram of the workflow
and also your only documentation. That
is why it hurts so bad. The advantage of
composing these workflows in JSON is you
can not only be more accurate and be
more likely to be simple. If you're
working with an LLM that has a bias to
simplicity, you can tell it to be
simple, right? It will help you get
there. You can also use that exact same
LLM to write the documentation for the
JSON that you're giving. Hey, this is
the documentation so I can save it and I
can maintain it and I can know why I
made the decisions I did. I would
encourage you to do that because you
want to be in a place where you're not
the only one maintaining it unless
you're a solo builder, which by the way,
it is possible to have a solo business
that works this way. As an example of a
small business that did this, uh, a
company called Border, B O R DR, they
dropped the E, built a real business
helping people navigate Portuguese
bureaucracy. This is for part of the
sort of expatriate workers movement. You
can work around the world, the nomad
movement, etc. They used NAND to do it.
The entire operation runs on NADN
workflows. I want you to guess how many
of those little boxes on NADN they have
for their core workflows. Oh, go ahead.
Guess. All right, you guessed. Great.
They have 18. Not 180, not 18,000. 18.
They understand that complexity
compounds risk. Complexity also
compounds exponentially in automation.
This is just basic graph theory. If
you're a developer, every node you add
does not just add one more thing to
maintain. It adds interactions with so
many other nodes. A 10 node workflow has
something like 45 possible interaction
points. A 20 node workflow, just 10
more, would have 190. A 50 node workflow
would have over 1,200. Now, Portuguese
bureaucracy is legendarily complex,
which is why the business exists. Their
workflows are simple not because the
problem is simple but because they
understood how to decompose complicated
problems into composable parts. Every
workflow does one thing. It does it well
and it can be understood quickly. That
is a principle of software engineering
that I don't think is widely enough
understood by people getting started
with NAND and building agents. people in
that Goldilocks bucket, they almost all
overwhelmingly tend to come from the
non-technical side. And by the way, if
you're an engineer listening to this,
this video is kind of your get out of
jail free card when the director of
marketing draws this ridiculously
complex like whiteboard full of nodes
and says, "This is what we want for our
agent." And you're just kind of
screaming on the inside. Show them this.
The simplicity principle matters. It
really does. And by the way, I said
composable parts on purpose. One of the
fundamental aspects of software
engineering is the idea of separation of
concerns. Another way to put it is
things go in their proper place. You
keep your concerns neat and tidy so that
you can manage them well later. This is
where at a larger scale we get the idea
of microervices.
We're gonna take a tiny diversion here
because I worked at Amazon and Amazon is
where the idea of microservices and APIs
originated. Yes, we invented them along
with lots of other things. I don't know
if you consider that a curse or a
blessing, but you've got it anyway.
Microservices are the idea that the
separation of concerns is so important
in software that you cannot scale at a
certain point unless you have it. We
started off in coding by building
monoliths. What what we would call the
subway map of Manhattan for NADN. That
was software. It was software. Even at
Amazon 15, 20 years ago, everything was
this one giant codebase. Well, that
makes it really hard to maintain as you
get bigger and bigger and bigger code
bases. You don't know what a piece of
code touches. Only the senior engineer
who never takes a vacation knows. And
God help him. I hope he doesn't get hit
by a bus. Well, microservices existed
because we had to have them exist to
actually build. We had to separate
concerns. And now every engineer knows
about it because it's just such an
obviously good idea. You separate out
the components of the software. You
standardize how those components
exchange data and business rules. That's
what we call APIs. And then you have
separate concerns you can manage
cleanly. Apply that principle when you
are building agents. Don't build a
monolith agent that does everything.
Separate out your concerns cleanly.
Separate it out cleanly so that you only
have to do one or two things in a
particular agent. It's really, really
critical to focus. I want to give you
another example. This is also a real
example. Delivery Hero save hundreds of
hours a month with their NAD automation.
They handle hundreds of requests
automatically. And you notice what they
automated? IT account recovery. Not the
whole IT department, not employee
requests, just one well-defined process.
This is reinforcing what I'm saying. If
you want to be successful in an agent
implementation, you must focus
radically. Identify one process. It
needs to be painful. It needs to be
frequent. It needs to be really
well-defined with good edges. Automate
it all the way. Run it. Obsess over it.
Learn what breaks. Fix the breaks. Only
when it's mature, sustainable, well
doumented, do you move to the next
process. The typical failure pattern I
see is the opposite. Teams don't clearly
define the pain. They don't know where
the edges are. They had a seminar and
the seminar said use AI agents and they
got all excited and they try to automate
everything at once. They build giant
sprawling workflows and it touches
multiple systems and it looks incredible
and on day one it all works and they
have a CEO announcement and they're
creating dependencies they don't
understand. And when something breaks
and it always breaks, plan for it to
break. They can't isolate the problem
because nobody can read the map anymore
because everyone's on to the next
project because the CEO declared victory
on AI agents. AI agents are not a
tickbox. AI agents if you want to
implement them implement them this way
and so many teams do. AI agents are just
a new way of doing software for
everybody. And it's possible to do
software for everybody. I firmly believe
that if you work with your favorite LLM,
whether that's Claude or Chad GPT or
Gemini or even Grock, you can get to a
point where you build this yourself and
it builds in line with what I'm
suggesting. It's simple. It's
composable. It fixes real pain points,
etc. You don't have to have an engineer
to do that, but let's recognize that you
are doing real engineering work. you are
actually building software. And this is
part of the magic of the AI era. If
you're willing to think this way with
this kind of clear intent, you too can
do the work of building software and you
can generate the magic, the real ROI
that comes from true automation.
Remember when I said delivery hero saves
200 hours a month? They do. It's real.
Because it turns out at scale a lot of
people need their IT accounts recovered
and that's a very predictable workflow
and you can recover it. Border can
maintain their scale and navigate
Portuguese bureaucracy because they
built these agents effectively and it's
not the tool that's the difference.
These guys are using N82. They just know
how to use it. So if you're in this
Goldilocks use case, you should be
encouraged that yes, this is a real use
case. It really matters. You really can
automate it. You don't need to wait for
the developers, but you got to take it
seriously. One thing that I want to
emphasize at this point, we've talked
about best practices for coding, best
practices for software, the team
problem. Your private automation is not
a team level product. Nobody talks about
this. Nad likes to market this as
individual productivity because frankly
it gets them more seats and customers.
You can build an agent that works
perfectly for you. You might understand
its quirks. You know how to restart it
when it hangs. You remember to clear the
memory cache weekly. You know how it
fails on PDFs over 10 megabytes in size.
And then you go on vacation. And it
turns out this was producing a
deliverable your team cared about. Your
team can't debug your workflows when
they break. They don't know why certain
design decisions were made. They're
afraid to modify anything because they
might break something else. It looks
like this scared spaghetti on the wall.
This is how automation projects die.
They die not really from technical
failure. They die from knowledge
isolation, from silos. Successful
transitions require documentation that's
useful. Remember when I said document, I
said document because your automation is
your team's problem. Don't create huge
manuals that nobody reads. Just create
very simple, very short runbooks. When
this error appears, check this. This
workflow depends on this web hook. Clear
this cache every Monday or response
times degrade. Patterns, patterns,
patterns. You don't need to be creative.
You need to have clean patterns. Every
workflow ideally would follow the same
error handling pattern. Every agent, if
possible, should use the same memory
config. Is it boring? Yes. Is it
maintainable? Exactly. That is the
point. The more we make NADN and agent
automations a teamle product, the better
off we are going to be. And this by the
way is one of the hidden lynch pins in
AI strategy and AI work. We typically
talk to the seauite and we talk to
individual contributors and senior
managers and directors people who run
teams are left out of most of the
conversations. We either when we're
talking to IC's implicitly assume
they're the leadership with the seuite
or if we're talking to leadership we
implicitly assume they're with the IC's.
They're neither and they are critical to
the AI revolution and this kind of
example shows why. You cannot make an N8
automation a seuite problem. It's way
too high level. But you also cannot make
it an individual contributor problem
because that leads to all kinds of
downstream breakages and workflows as
I'm hoping I am calling out here. It's a
team problem which means it's a director
problem. It's a senior manager problem.
You guys in those positions need to be
the ones that are insisting on a high
bar here. insisting that your team, even
if they're marketers, learns enough of
the basics of good engineering
principles that when you touch
automations and build agents because the
seauite said you needed to do it, you do
it well and you don't give yourself pain
down the road because you can make cheap
automations that tick that box and you
will give yourself a world of suffering
down the So, let's get to one last piece
here. LLMs are an accelerant in multiple
directions for NAD. One, LLM enable you
to do more. They tie intelligence
directly into workflows. For a lot of
people who are not developers, NADN
represents the first and most accessible
front in the integration pathway. You
can actually tie your chatbot into real
work. You don't have to use the API
necessarily because effectively you're
proxying for that through NAD. It's you
can just pretend it's not there, right?
You can just go on with your business.
And that is huge because people want to
get real work done. They want to monitor
Slack channels for customer complaints.
They want to categorize sentiment
analysis by urgency. They want to create
tickets in Jira. They want to process
records in a certain way. They want to
send a daily summary to a support lead.
This is all real work you can't get done
in the chatbot. Claude or Chat GPT or
other LLMs can generate not just the
JSON config for those workflows but also
the design decisions and why they were
made and the documentation. In other
words, you access the intelligence
inside the workflow and that's an
accelerator and working with the chatbot
is an accelerator as well. It's a second
way LLM accelerate you. What I am
talking about, the reason I am making
this video, this would not have been
possible even 8 months ago. NAND wasn't
mature enough. Chat bots weren't mature
enough. They weren't good enough at
checking documentation reliably. We are
at a point now where it's mature. Nad is
ready. You can get LLMs that reliably
pull up the correct documentation.
They're going to give you reliable
workflows. They're smart enough to give
you good documentation and really help
with constructing agent ecosystems that
work at the team level. Th this is
actually not as hard as you think. You
really can build a couple of simple
workflows, monitor the heck out of them,
check for errors, and go on and expand
from there. You can even get to the
point if you've done this for a couple
of months and you know what you're doing
because you've been slow, focused. I
know slow. I said slow. In this case,
slow is smooth and smooth is fast.
Because you've focused on implementing
smoothly and only doing one edge case,
you will quickly get to the point where
you can do stuff that's more
interesting, where it may make sense
given the problem because it's well-
definfined and you understand it well to
use a complex memory system like
retrieval log metageneration. You can do
that in N82. People jump right to it. I
wouldn't recommend jumping right to it.
You can use multi- aent orchestration.
You can use complicated tool chains.
That's all there, but I wouldn't start
there. Start with something simple and
get to it over time. There are real
savings here. Vodafone saved 2.2 million
pounds with NADN workflow, right?
They're big, right? That's part of how
they got there. But recognize that the
real ROI comes from the principles I've
talked about. Naden is simultaneously
one of the best, most accessible, and
most dangerous tools I have seen for
building AI agents. It's accessible in
the sense that you can just start right
now. You don't even have to listen to
this video. It's dangerous in the sense
that I see time after time after time in
real world situations where I talk with
people passionate about agents that NAND
is the thing that burned them out
because it was so accessible they felt
like they could be non-programmers and
not pay attention to good engineering
principles. Please recognize that you
are building real software and follow
good principles. Make sure you emphasize
simplicity. Separate concerns. Focus on
maintainability. Focus on readability.
Get your documentation done, work with
an LLM, solve a problem that's well
bounded. Your choice is whether to
understand
when each approach serves you and to
have the discipline to switch between
approaches when you need them. So
whether you need to take a a focused
approach on this particular agent or
whether it makes sense to do two agents
for a particular problem that requires
high intention, high thought, careful
architecture or you can just follow the
marketing and you can just believe that
you can just throw something up on a
canvas and end it in and it will just
work. That works great in marketing. It
it demos okay. It's not going to sustain
well over time. And so your choice is
between building maintainable software
with an approach that serves you. An
approach that you have the discipline to
scale over time. You remember when I
said you could have multiple agents.
Maybe don't start with that. But you can
get there if you have the discipline to
scale over time. Or you can start with
what the marketing tells you and do the
multi- aent and the rag and everything
else right now. I understand the appeal.
Agents are really cool. I like building
with them to just take the time to get
into agent building in a way that serves
the long-term value of the business and
frankly helps you sleep well at night.
No one wants to get interrupted on
vacation because their agent broke. If
this works, it's not only going to help
you work, it's going to help your team
actually use agents and it's going to do
something more than that. It's going to
help your business understand what this
agent thing is all about. And that's why
I call NADN somewhat dangerous because a
lot of the time what I see is that as
these agents break, what businesses
understand is that AI agents are fake
and AI agents aren't real and AI agents
aren't delivering the value that they
were promised. That's not actually true,
but it is true if you use them badly.
NAD is like a knife. You can use a knife
badly or you can use a knife well. I'm
trying to give you the principles to use
it well. Good luck with agent building.
I know you got this.