When a big player like OpenAI drops a new tool,
especially one they call Canva for Agents. No.
Well, the internet tends to jump straight to
destruction mode. Oh, yeah. It's always, what's
it going to kill? As soon as AgentKit landed,
October 6, 2025, you saw it everywhere. RIP and
8M. Just instant hyperbole. But that whole Tool
Wars thing. It often misses the point, doesn't
it? Especially when you're talking about serious
business automation. Does the hype around easy
visual building actually hold up for complex
needs? Exactly. And that's what this deep dive
is all about. We're doing a really rigorous head
-to -head comparison. AgentKit versus NAN. We
want to cut through that noise and figure out
for you when you'd use which one. We'll look
at six key areas. Okay, so let's set the stage.
AgentKit. That's the new one. Low -code, drag
-and -drop, visual. Built specifically for conversational
agents. And importantly, it lives entirely within
the open AI world. It's about accessibility.
Right. And then there's NAN. Been around six
years. The established powerhouse. Open source.
Huge number of integrations. Over 500. It really
targets developers, power users, people building
custom, truly autonomous systems. That one's
built for total control. So our starting hypothesis,
just going into the testing, was pretty straightforward.
AgentKit isn't going to kill an AM. They seem
built for fundamentally different users, different
jobs. Let's see if that holds up. Let's do it.
First up, ease of use, or what we're calling
the startup experience. Basically, how much friction
is there just getting your first agent up and
running? AgentKit. It felt incredibly beginner
-friendly. We gave it an 8 out of 10. The interface
is just clean, minimal, basic logic like IFL,
transforming data. Pretty straightforward. Yeah,
the key is speed. You can actually build your
first agent, maybe doing a simple web search
in like less than two minutes. Seriously, no
API keys needed right away, no complex setup.
You just start building. Okay, contrast that
with N8n. We scored it a 6 out of 10 here. It
is definitely powerful, a power user's dream
maybe, but wow, there's a learning curve. You
open it up and it's just hundreds of objects.
That same... Simple web search. In A &A, you've
got to select specific models that have custom
HTTP requests. Definitely need your API keys
handy right from the start. It asks for commitment
up front, you know, but then it gives you enormous
power back later. So for the learner just starting
out, what's the main takeaway on that startup
experience? Is it speed versus depth? Agent Kit
gives you that fast access, that quick win. Ain't
Ain't Ain't makes you invest time before unlocking
its real power. Okay, makes sense. So if getting
started is one thing, how does Agent actually
start? Let's talk triggers. Yeah, and this is
where you see a real philosophical split. Agent
Kit scored a 5 out of 10 from us. Why? It's basically
limited to just the start node. Meaning? Meaning
interactions are almost always chat -based. You,
a human, have to talk to it to kick things off.
Right. It forces that human intervention for
pretty much everything. No scheduled triggers,
no reacting to things happening in other apps.
Nope. No timed triggers, no listening for events
in Slack or Gmail, no real background capability.
It needs you to chat with it. That feels like
a pretty big limitation for genuine automation,
doesn't it, for systems that should just run
themselves? Totally. If a business needs something
to, say, check inventory levels every night at
3 a .m. and send an alert if stock is low, AgentKit
just can't do that silently. It needs a human
prompt. So if a business needs automation that
runs without someone typing into a chat, what's
AgentKit's strategic problem there? Its design
inherently forces human interaction, limiting
its use for those silent background processes.
Okay. And A &AN on triggers. Oh, A &AN is trigger
paradise. Solid 10 out of 10. Hundreds of native
triggers built in. Gmail, Slack, webhooks from
anywhere, databases. Time -based stuff too, like
run every hour. Yep. Time -based external events.
It lets you build genuinely autonomous, scalable
workflows that just hum along quietly in the
background. So it's the difference between like
a chatbot assistant you have to poke versus a
dedicated background system doing work silently.
Exactly that. A virtual assistant versus a silent
engineer. All right. Moving on from starting
the workflow, let's get into the core stuff.
What tools can these agents actually use? What
can they connect to? Okay. AgentKit scored a
5 out of 10 on tools. The built -in set is, well,
limited. You get web search. something called
a client tool to send data back to the chat interface.
And then these things called MCP servers. Hang
on. MCP servers. Can you break that down? What
is that in plain English? Sure. Think of MCP
servers as AgentKit's pre -built connectors for
a few common services, like basic hooks into
Gmail or Google Drive. They're easy to set up,
which is nice, but the selection is really narrow
compared to what else is out there. Gotcha. And
AN. AN is the integration universe here. Easy
10 out of 10. Yes, it has over 500 native integrations
built right in, but the real superpower, it's
the HTTP request tool. Which means? Which means
if any service out there has an API, Any service
at all. Any ant can talk to it. Period. Full
stop. Wow. Okay. And what's really cool, architecture
-wise, is modularity. NEN lets you build and
reuse sub -workflows. Imagine building little
specialized microagents, one for analyzing sentiment,
one for parsing invoices, one for summarizing
emails, and then having your main agent call
these as needed. Yeah. I still wrestle with prompt
drift and tool selection myself sometimes when
I'm trying to chain multiple complex AI tasks
together. So the idea of building one really
solid, reliable sub -workflow for, say, data
extraction and then just reusing that everywhere,
that's... incredibly appealing, reduces so much
variability. That modular approach using sub
-workflows is NEN's killer advantage for complex
specialized business processes, consistency and
reliability. So beyond just the sheer number
of tools, what's NEN the key advantage for those
really specific business needs? Its architecture
lets you build modular, reusable agent systems
using those sub -workflows. Okay, let's shift
to the brain behind the operation. Model support.
Which AI engines can you actually use? With AgentKin,
it feels like the OpenAI Exclusive Club. We gave
it a 6 out of 10. Yeah. The integration with
OpenAI models is excellent. Really deep. Easy
toggles for GPT -4 versus others. Reasoning effort.
How chatty it is. But, and it's a big but, you
are completely locked into the OpenAI ecosystem.
No Anthropic Quad. No Google Gemini. No open
source models running locally. None of that.
No Azure. No AWS Bedrock. No Cohere. Definitely
no local models on your own hardware. It's OpenAI
or nothing. And NAN. NAN is the model democracy.
A clear 10 out of 10. You get genuine flexibility.
Anthropic, Azure, OpenAI, AWS Bedrock, Cohere,
anything via OpenRouter, plus local models using
things like Alama. And you get control over the
settings too, like temperature, top brand. Full
control, which is crucial not just for performance,
but for cost optimization. Right. So if cost
saving is a major strategic goal, why does that
model democracy matter so much? It allows you
to route different tasks to the most cost effective
and task appropriate model provider dynamically.
Save money where you can. mid -roll placeholder
sponsor content goes here. All right. So far,
it feels like NAN is dominating on the pure back
-end logic, the integration power, the model
choice. But let's switch gears a bit. What good
is all that complex logic if the user experience,
the interaction is clunky? Let's talk UI and
chat components. Ah, and this is where AgentKit
really makes its case. A strong 9 out of 10 here.
It's the front -end champion. Its integration
with ChatKit means you can create really slick,
professional -looking, branded website chat widgets
and embeddable components right out of the box.
We saw HubSpot talking about this, actually.
They said ChatKit saved them literally weeks
of custom front -end development work trying
to build a similar interface themselves. That's
huge. Weeks of dev time saved. Absolutely. So
where's the clearest, most immediate time -saving
advantage with AgentKit? Building polished, customer
-facing chat interfaces takes basically zero
extra development time. Compare that to ANAN,
which we called the back -end beast. It scored
a 5 out of 10 on UI. Its focus is overwhelmingly
on the workflow logic. Yes, you can build basic
chat interfaces, but they're hard to customize,
hard to brand. For anything polished, you're
looking at building a completely custom front
-end using other tools and connecting it via
API. So it's like Agent Kit gives you the finished,
polished car. Well, NAN gives you a powerful
engine and says, okay, now build the car body
around it. That's a great analogy. Yeah. Okay.
Final category, deployment and control. This
gets into bigger enterprise concerns. Where does
this thing run? Who controls the data? Who manages
the infrastructure? Right. AgentKit gets a 7
out of 10. It's a managed service. It runs only
in OpenAI's cloud, the upside. Zero infrastructure
for you to manage. Super convenient. The downside.
OpenAI controls everything. Your data lives there.
The uptime depends on them. The pricing is set
by them. You have to fundamentally trust OpenAI
with your critical operational data. Okay. And
NAN. NANN is the sovereignty option, a full 10
out of 10. It offers just unparalleled flexibility.
Use their cloud service, sure, or self -host
it on your own servers, or even run it locally
on your laptop. And because it's open source?
You get complete data control, zero vendor lock
-in. You own the whole stack if you want to.
Whoa. Imagine scaling that level of control,
running maybe a billion queries a month, all
on your own private, completely sovereign infrastructure.
That's a game changer for highly regulated industries
or just for companies wanting total control.
Exactly. For compliance, data revidency. It's
huge. So for a large enterprise, maybe in finance
or health care, what's the single biggest factor
tipping the scales toward A &N in this category?
Complete sovereignty and control over data and
infrastructure, especially for compliance needs.
OK, so the final scores came out, AgentKit 40
and A &N 51. But like we said up front, the raw
numbers don't really capture the whole picture,
do they? Not at all. The qualitative stuff matters
just as much, maybe more. Take debugging, for
example. Right. AgentKit, even though it's visual,
sometimes when a workflow breaks. figuring out
where the data went wrong between nodes can be
opaque. You end up digging through logs. Whereas
NAN, because it's built with developers in mind,
gives you crystal clear visualization. You click
on any node, you see the exact data that came
in, how it was configured, and the exact data
that went out. Troubleshooting is usually way
faster. Plus, there's the community aspect. NAN
has six years of history. Forums, templates,
tutorials, community support. Yeah, a massive
head start there. AgentKit is brand new, still
building that ecosystem. Which brings us back
to that strategic mindset we talked about. AgentKit
isn't killing NEN. They're aimed differently.
We need to shift from thinking which tool wins
to which tool solves this specific problem best.
That's the key. your clients or your business
they choose you for results for solving their
problems they don't really care which tool you
used under the hood as long as it works reliably
and efficiently so let's make it practical when
should someone choose agent kit choose agent
kit if you need something fast rapid deployment
simple conversational agents quick prototypes
or crucially if you want that polished chat widget
on your website without spending weeks on front
-end development and when is n8n the right call
Choose N8n when you need flexibility with AI
providers, when cost optimization across different
models is critical, when you need complex background
automations running silently without human intervention,
when data sovereignty and control are non -negotiable,
or frankly, when you need to connect to almost
anything out there using its powerful API capabilities.
It really sounds like the future is just coexistence.
There's plenty of room. Absolutely. The market
for automation is massive. AgentKit is going
to be huge for quick internal. tools, simple
customer -facing bots, people who value speed
and ease above all. N8 will continue to dominate
the complex enterprise -grade custom control
scenarios. They serve different needs. So the
big idea to wrap up. These platforms serve different
masters. AgentKit is about making agent building
accessible, fast, easy. NEN is about giving you
unlimited control and integration power. The
most important thing for you, the listener, the
learner, is to become tool agnostic. Focus on
the problem first. Yeah, and if you're totally
new to this whole automation game, maybe start
with AgentKit. Get your feet wet, understand
the concepts, get some quick wins. Then when
you inevitably hit its limits for more complex
tasks, graduate to NAN for that deeper power
and control. Because the tools themselves, AgentKit
today, something else tomorrow, they'll keep
changing. But the underlying skill. The ability
to logically structure a problem, break it down,
and design an automated solution. That skill
is permanent. Focus on building that core problem
-solving framework. That's the real investment.
Focus on the framework. Focus on the outcomes.
Excellent point. Until next time.