Okay, so I want you to just imagine something
for a second. Right. Imagine your automation
workflows weren't just these silent scripts running
in the background. Right, not just invisible
code doing its thing. Exactly. What if they were
conversational teammates? Teammates you could
actually ask questions to in real time. Like
a coworker who, you know, already knows all your
data and can get things done instantly. This
is really the fundamental shift that arrived
in late 2025 with Ann Ann's chat hub. It moves
automation from that old set and forget model
to live steering. Okay, let's unpack this. We're
diving into Max Ann's breakdown. of the NN and
chat hub. Yeah. And it's not just about a chat
interface. It's really about how this tool becomes
a unified command center for delegating tasks
and managing a bunch of different AI models.
All in one place. Our mission today is to understand
the three core features, see why this really
changes complex automation, and then get into
the practical steps for setting up agents that
can analyze data and support customers. So what
does this all mean for you? It means your system's
just got a voice and we're going to learn how
to talk back. And when the chat hop was released
on December 15th, 2025, it was it was way more
than just a chat window. It really became this
centralized AI command center. Right. For talking
to your entire infrastructure, not just one AI.
Exactly. So the simplest way to think about it
is this. It's like having GPT -5, Claude, and
your own custom AI agents all in one spot. And
crucially, they're already connected to all your
automation tools, your data. That's the key part.
That connectivity is the technical difference,
isn't it? It is. It's what separates it from
just, you know, using ChatGPT directly. A direct
chat model is great for thinking, for brainstorming.
But it's sealed off from the world. Totally.
It can write an email, but it can't send the
email from your account. It can't query your
live database. It definitely can't run a multi
-step workflow. Before this, automation always
felt like a black box. You build something, hit
run, and just hope for the best. And if it failed
three steps in, you wouldn't know until later
when you're digging through logs. It was so passive.
And that's the critical insight here, the paradigm
shift. It's a move to what they call collaborative
automation. You're not just sending a request
into the void anymore. You're treating the workflow
like an active teammate. You're asking it in
plain English. Hey, what's happening right now?
Why did that last step fail? What data do I need
next? It's that live steering capability. Think
about it. If your daily sales report workflow
fails on step four. The CRM credential node,
always. Right. Instead of digging through logs,
you just ask the hub, why did step four fail?
And it replies instantly, credential expired.
Pause the process. Please update the key. It's
the difference between sending an email and getting
an instant reply from someone sitting right next
to you. It just removes all the guesswork. So
connecting this to the bigger picture then, why
is collaborative automation so much better than
the old set and forget way, especially with complex
data? It allows for live steering and debugging
of systems, turning passive scripts into queried
resources. Okay, so if the concept of talking
to your systems is clear, what did they actually
build to make this happen? Let's get into the
three core features. The first one is the multi
-model chat interface. This is a huge efficiency
boost. I mean, it solves that problem of trying
to force one AI model to do everything badly.
This interface lets you switch between large
language models right in the middle of a thread.
You don't lose your context. You don't have to
start over. It's like having a small specialized
team on call. You can start with GPT -5 for reasoning.
Yeah, for the big picture stuff. But then you
realize you need to analyze a huge document,
like 50 ,000 words, so you just... switch over
to Claude, which is known for handling that huge
context. Or you need to check a stock price right
now, so you flip to Gemini or a model with a
live web search tool, you get real -time info.
No more knowledge cut -offs. What's fascinating
here is the ability to use these specialized
models, but how does ensuring consistency across
those multi -model interactions affect the outcome?
You use each model where it shines, leading to
higher quality and more reliable analysis. Okay,
so the second feature is custom personal agents.
Think of these as the lightweight players on
the team. They're simple agents with custom instructions
and very specific limited tool access. And you
can create them without building a whole complex
workflow. So for someone just starting, this
is probably the easiest way in. Absolutely. You
could make a content editor agent. The instruction
is just always use cloud for consistency. And
that's it. Simple. But the real power, the heavy
machinery, that's the third feature. workflow
agents. This is the game changer. This is where
you connect your big, complex NN workflows directly
to the chat using the new chat trigger node.
So you're merging conversation with actual execution.
That's it. Instead of manually triggering a data
analysis workflow, you just chat with it. You
say, analyze last month's sales data. And the
workflow just runs in the background, it connects
to the database, does the analysis. And then
replies to you right there in the chat window,
right away your next question. Whoa. Imagine
scaling this capability to manage a billion queries
across diverse model architectures. That's a
serious leap forward. Sponsor. So moving into
implementation, there are a few really crucial
details you have to get right for this to actually
work. You must use the newest version of the
chat trigger node. That is the number one pitfall
we see. People are using the old legacy node.
It looks similar, right? Very similar. But the
new one is what handles that back and forth conversational
flow. If you're stuck, check that node version
first. Okay. And the other key thing is enabling
streaming on the AI agent node. Jargon alert.
What's streaming? Streaming just means the AI's
answer appears in real time, word by word, like
someone typing. Not a big block of text after
a long wait. And that's important because it
feels more like a conversation. It feels collaborative.
Exactly. It's about that feeling of interaction.
And what about knowledge cutoffs? I mean, most
models don't know what happened last week. Right.
So you have to connect external tools. You can
plug in something like Jaina AI for real -time
web search or connect to your own knowledge bases.
It keeps the agent current. So now for the power
move. Turning an existing complex workflow into
an agent your team can talk to. It's surprisingly
simple. You just go into the chat trigger and
you turn on one setting. Make available an 8
a .m. chat. And you have to give it a clear descriptive
name, not workflow three. Please don't. Something
like sales data analyzer. So your team knows
exactly what it does. You know, I have to admit,
I still wrestle with prompt drift myself sometimes.
Oh, everyone does. Especially when you're dealing
with these little setup details. Like just remembering
to replace that old chat trigger node. It sounds
so obvious. But it's an easy mistake to make
when you're moving fast and just trying to get
it working. It really happens to everyone. But
when you get those details right, the use cases
are just, they're transformative. Like an interactive
customer support bot. Perfect example. Your support
team can just ask, check status for order, hashtag
12345. And they don't have to log into three
different systems to get that one answer. Right.
It pulls from the CRM, inventory, shipping, all
at once. The psychological win there is huge.
Or the data analysis assistant. Instead of pulling
raw data, you just query the database in chat.
What were our top 10 products last quarter? And
you get the answer already summarized. So given
that the workflow agent is the heavy machinery
here, what's the fastest win a learner can get
when they're starting out with a custom agent?
Focus on building one workflow agent that reliably
answers a single painful question your team asks
every day. Solve one problem perfectly, then
you can scale. Now, when we talk about rolling
this out to a bigger team, especially in a company,
control and security are everything. And this
is where the chat user role comes in. This is
a vital guardrail. It's designed for non -technical
people, your sales team, your marketers, so they
can use this power safely. So what do they see?
They only see the chat screen and the agents
you've chosen to expose to them. They can't see
the workflows. They can't see the credentials.
And can't break the engine. Exactly. It's a perfect
separation of duties. They get the power without
the risk. We should probably touch on limitations,
too. Yes. Remember that personal agents are the
lightweight ones. They're great for quick text
tasks, but they can't do the heavy lifting like
reading through a huge knowledge base. For that,
you need a RAG, right? Retrieval Augmented Generation.
Correct. And for proper RAG, great, you must
use a workflow agent. Why is that? Because RGRI
needs a vector store node to handle all the indexing
and retrieval of that big document. A personal
agent is just too lightweight to manage that
complexity. So to make sure these are reliable,
you really have to treat the rollout like a product
launch. It helps to think of it like you're hiring
a new team. You need to give them clear names,
not Agent 1. Call it Customer Order Lookup. And
write a good description that answers, what does
this do and when should I use it? And then the
system prompts. That's like the employee handbook.
That's where you set the rules, the tone, the
limits. Things like, always verify customer identity
before sharing order info. It makes the agent
dependable. And finally, you have to test it.
You have to try and break it before you share
it. Absolutely. Run edge cases. Ask confusing
questions. If it can handle the stress test,
it's ready for the team. Okay, so a quick recap
of the comparison. Direct chat tools like GPT
are great for thinking. Brainstorming, drafting.
And fully custom chatbots are incredibly powerful.
A massive engineering project to build and maintain.
So ChatHub sits right in that sweet spot in the
middle. It's where your thinking turns into direct
automated action. It gives you that custom behavior
without the insane cost of building a whole system
from scratch. So if security and cost control
are paramount, which administrative control should
an admin prioritize when setting up ChatHub for
a big team? they should focus on the chat user
role restrict credential management and enable
or disable specific potentially expensive ai
models the essential insight we found here is
that chat hub is really more than just a feature
it's a a fundamental paradigm shift toward conversational
automation It just removes the guesswork. You
can debug in plain language. You can guide complex
processes in real time. You stop waiting for
an output and start engaging with the pipeline.
The teams that are going to move fastest are
the ones that upgrade from sending tickets to
having a live analyst, a system they can talk
to and make decisions with in the moment. And
here's where it gets really interesting for me.
This shift. means automation is no longer this
invisible engine in the dark. It's an active
teammate. It's an active teammate ready for you
to delegate to. For anyone focused on fast, high
-quality knowledge, this tool means you can now
interrogate your data pipelines. You can question
your workflows. You're not just waiting for the
final report anymore. You're an active participant.
An active participant in your own business logic.
So if we accept that the future of automation
is conversational, the final thought for you
to consider is this. If your workflows could
actually talk to each other, if the data analyst
agent and the content generator agent could hold
a meeting, what's the first task they would solve
together? Go build something amazing.