Welcome to the Deep Dive. Imagine for a moment
having an entire AI content team, you know, right
there working for you. A researcher, a planner,
writers, even an editor. Wow. All creating your
weekly newsletter completely on autopilot. That's
quite the picture. That's what we're diving into
today. Okay, so today we're unpacking this really
comprehensive guide to building a multi -agent
automation workflow. Yeah, a whole system. Right.
And our mission really is to understand how you
transform that sometimes painful process of content
creation. Oh, yeah. Tedious sometimes. Into something
seamless, automated. Exactly. So we'll walk through
the blueprint, the tools you need, and the step
-by -step build, piece by piece. We'll see how
these different AI agents actually collaborate
from like the first idea, the research. All the
way to the end. All the way to final delivery.
And then we'll look at the time savings, the
strategic advantages this brings. So, yeah, let's
unpack this. This isn't just, you know, one simple
prompt we're talking about. It's really a full
digital assembly line. An assembly line. Okay.
Think of it like a factory. Takes raw info, turns
it into something valuable, like your newsletter.
Right. It kicks off with what the guide calls
an initial research phase. It's like scouting,
finding the latest news for your topic from,
say, the past week. So gathering the raw materials
first. Yeah. Making sure you've got the freshest
stuff before you even start cooking. Foundational.
Precisely. Then, from that initial scoop, a specialized
planning agent steps in. Okay. It analyzes that
research. comes up with a creative title and
picks out the maybe three most important topics
for the newsletter. Got it. After that, there's
a deep research phase. This goes much deeper
on each of those topics, really comprehensive
info gathering. Right. So moving from the kind
of broad overview into the specifics for each
point. Exactly. Then individual section writing
agents get to work. They craft each section.
And the key part here. The critical part. They're
told to cite everything. Every fact, every piece
of data needs a proper citation, even a clickable
URL. A verification. Nice. Yeah. Then an editorial
review agent takes over. It grabs those finished
sections, adds an intro, adds a conclusion, wraps
it all up in clean HTML. the whole pack the whole
package and finally automated delivery sends
a draft straight to your gmail wow and the beautiful
part it's clean linear totally automated no hands
needed start to finish that's a lot of coordination
happening behind the scenes and the guide really
emphasizes um the secret here is planning You've
got to plan before you build. Absolutely. Like
any complex project. Yeah. Think like an architect
sketching out the factory blueprint. They suggest
using a whiteboard tool like Excalibur or Miro.
Yeah. Visual tools help a lot. Map it all out.
So this blueprint includes what? The weekly trigger.
Kicks it off. The raw materials intake. That's
the initial research. Strategic planning. The
AI planning agent. The architect. The deep research
division. Digging into topics. Specialized writer's
room. Final assembly and editing. And then shipping.
A whole flow. And this planning, it's critical,
right? It stops that, what's it called? Scope
creep. Yeah, scope creep, where things just get
bigger and bigger unplanned. Exactly. Yeah. And
make sure every single component has a clear
job. A defined purpose. So you build with intention,
lay that groundwork, avoid problems later. So
why is this upfront planning so crucial before
you even touch any code or any tools? Well, that
clear blueprint. It just prevents that scope
creep we mentioned. And it really defines what
each part is supposed to do precisely. Keeps
it focused. Got it. Keeps it lean and focused.
Absolutely. That blueprint's like your insurance
policy. Make sure every bit of code, every agent
configuration has a purpose. Okay. Let's talk
tools then. the builder's toolkit right and the
cool thing is this whole system uses a pretty
minimal but really effective tech stack lean
but powerful exactly yeah at the core you've
got n8 that's n8n it's this open source platform
lets you connect different apps build automated
workflows it's like the central nervous system
for our factory orchestrating everything yep
then for research both the initial scan and the
deep dives we use tavly ai Gmail is just the
simple, reliable shipping department for the
drafts. Makes sense. OpenRouter acts as this
versatile AI switchboard, which is great because
you get access to lots of different AI models.
From different providers. Yeah, all through one
account. Really handy. Oh, interesting. And the
main brains for writing and analyzing, that's
GPT -5 and GPT -5 Mini. The big guns and the
slightly smaller, maybe more efficient gun. Kind
of, yeah. And this lean stack keeps costs down,
but you still get, like, enterprise -level automation
power. Okay, so every automated system, it needs
that pulse, right? A heartbeat to start things
off? Absolutely, the starting gun. And for this
system, that's the scheduled trigger node in
ANAN. Correct. You set it up for, say, once a
week, maybe Sunday at midnight. Yeah, something
like that. Give it plenty of time to run. So
a draft is waiting Monday morning, ready for
review. Exactly, like setting your watch. Predictable
rhythm. But there's a key rule here. The safety
switch rule. Oh yeah, super important. Always
keep the workflow set to inactive while you're
building it. Don't turn it on too early. No way.
Only flip it to active when you are absolutely
sure it's built, tested, and ready to go live.
What's the main reason for that safety switch
rule during development? Why is that so critical?
Well, it just stops accidental triggers while
you're still building. You know, ensures you
launch a tested, production -ready system, not
something half -baked. Right. Prevents those
oops moments. We've all been there, haven't we?
Yeah. Testing something and suddenly it sends
a half -done email. Exactly. Build in that safety
net, even during development. Okay. So the heartbeat's
set. Now we bring in the scout. That's a Tavoli
search note. Yep. The first research step. And
its job isn't to dig super deep at first, right?
It's just finding the top, say. Three high -level
news stories from the past week. That's it. Quick
scan. Find the main relevant headlines. So you'd
tell it your broad topic, like AI adoption for
small businesses. Set it to look for news, past
week, maybe max three results. Exactly. And you
need your Tavoli API key in an ANAN, of course,
securely stored. Right, the credentials. So this
initial search gives you that foundation, points
you in the right direction. Yeah. And for a pro
upgrade, you could even make that search query
dynamic. Have another AI figure out the best
thing to search for each week. Ooh, okay. Hyper
-focused newsletters. Interesting. Now, the scout
brought the raw intel. Time for the architect.
Another AI agent node. This one creates the actual
plan for the newsletter. Correct. The master
blueprint. We use something cost -effective but
smart, like GPT -5 Mini, maybe via open router
again. Okay. For the user message, we package
the research cleanly, use a formatting function.
It's like a little reusable program. To present
the info consistently to the AI. Exactly. Much
more scalable than doing it manually. And the
system prompt, that's the architect's job description.
Something like, you're an expert newsletter planner.
Make a creative title. Find the main topics.
Keep topics short, three to five words. Clear
instructions. But here's the really cool part.
We use Anaheim's structured output with a JSON
schema. Okay, JSON schema. Sounds technical.
What does that mean in practice? Think of it
like giving the AI a very specific form to fill
out. It forces the AI to give you the title and
topics in separate, clean data fields. So no
more trying to pick apart messy paragraphs of
text from the AI. Exactly. No parsing needed.
It's structured from the start. This is a non
-negotiable pro move. Guarantees clean, usable
data downstream. Saves so many headaches. Why
is using that structured output for the architect
considered such a non -negotiable pro move? Because
it guarantees clean, usable data. like getting
a perfect blueprint, not just a messy sketch,
no messy text parsing needed. Takes the guesswork
out, ensures reliability for the next step. Precisely.
You know exactly what format the data will be
in. Okay, architect's done the blueprint. Now
the investigative journalist's time for the deep
dive. Yeah. So first, the assignment desk. That's
a split -out node in NAN. Split -out node. It
takes the single list of three topics from the
architect and splits them into three separate
items. Ah, so each topic can be handled independently.
Critical step, yeah. Then each topic goes to
another Tavli search node. This one's configured
as the investigative journalist. Okay. The search
query is dynamic now, focusing on just one topic.
Topic type is general for broader research. Not
just news this time. Right. And crucially, you
enable... include raw content. To get the full
text. Yeah, the full article text. That's the
raw material the writers need. Got it. So deep
research done. Now bring in the writers, the
writers room. Yep. Another AI agent node acts
as one of these specialist writers. And because
the topics were split. This runs three times.
Three separate times, once for each topic. We
stick with GPT -5 Mini here. For consistency,
cost efficiency. Makes sense. The user message
uses another formatting function, gives the writer
the topic heading and all that raw research text.
Okay, and the prompt, the instructions for this
writer. That's its prime directive, non -negotiable
rules. You're a professional newsletter section
writer. Always include a clear section heading.
Right. Do not write an overall title, intro,
or conclusion. Focus. Total focus. If you reference
facts, you must cite sources with clickable URLs.
Do not invent citations. Don't make stuff up.
Crucial. Yeah, absolutely. And honestly, the
most important part is often telling the AI what
not to do. It forces specialization. Yeah, I
can see that. I still wrestle with prompt drift
myself sometimes, getting the AI to just stick
to the task. Me too. So those precise negative
instructions, vital. Keep it on track. And a
pro tip. You can even tell it to create a separate
JSON array of all the facts and sources. Makes
checking them super easy later. Oh, that's smart.
So what's the biggest benefit of telling the
AI writer what not to do in its prompt? It just
forces the AI to specialize, ensures it really
excels at its one single specific task instead
of trying to do too much. Laser focus. Okay,
writers have done their sections. Time for the
editor -in -chief. Assembly and final polish
time. First, an aggregate node. Aggregate. Yeah,
it just combines those three separate written
sections back into one single item. Puts them
together. Got it. Then the final AI agent, the
editor -in -chief. Right. And for this step,
we upgrade. Use the full GPT -5 model. Why the
upgrade here? Better writing quality, more nuance,
and it's great at advanced HTML formatting. Okay,
so worth the extra cost for the final polish.
Definitely. You brief it with the newsletter
title and those combined sections. Tell it. Add
an engaging intro, a strong conclusion, format,
and clean HTML with working links. Make it look
professional. And structured output again? Yep.
Two fields. Subject for the email subject line
and content for the HTML body. Nice and clean.
And another pro -level upgrade. Give it a brand
voice document. Tell it to read that and make
sure the draft sounds like you. Ensure consistency.
Very cool. So the polished product is ready.
Now the shipping department. Time to send it
off. A Gmail note at the end. Yep. End of the
line. Simple configuration, but one critical
choice. Which is? The action must be set to create
draft, not send email. Oh, okay. Create draft.
You connect the dynamic subject and content from
the editor, set email type to HTML to keep the
formatting, send it to your own email address.
And that create draft setting. Why is that so
vital? It's that non -negotiable safety feature,
the human in the loop. It gives you that final
chance to review everything before it goes out,
check for errors, maybe add a personal touch,
keep control. Why is setting the Gmail action
to create draft so critical for this automated
system? It's that crucial safety net, that non
-negotiable human in the loop allowing for that
vital final review before anything gets published.
Keeps you in the driver's seat for the final
check, amplifies effort, doesn't just replace
it. Exactly. You maintain control. Okay, so we've
built this thing. A complete content factory
delivering professional newsletters. Right, your
inbox. The spec sheet sounds impressive. Professional
structure, good title, intro, conclusion, three
focus sections with citations. Yeah, evidence
-based, clean HTML, ready to go. But the real
game changer, the ROI is the time saved, right?
Massive time savings. That's the core benefit.
It just wipes out, what, 8 to 12 hours of manual
work every week? Wow, 8 to 12 hours every week.
That's incredible. Yeah, think about it. automating
all that tedious research, writing, editing,
formatting. That doesn't just save time. It completely
frees you up instead of being chained to the
keyboard producing content. You could be doing
higher level stuff, strategy, talking to customers,
exploring new ideas. It shifts your role from
producer to... strategist, innovator. Exactly.
And for performance tuning, making it even better.
You can add things like a Google Sheet dashboard,
log everything, track results, use it to tweak
your prompts. A feedback loop. Yep. Add safety
features like retry logic for API calls. If Tavoli
fails, maybe try perplexity automatically as
a backup. Redundancy. Smart. Upgrade the engine.
Use multiple research sources. Add custom HTML
CSS for perfect branding. Take it from good to
great. Now the professional playbook. Running
it like a pro. This means adopting that moneyball
approach to AI models. Moneyball? Like the baseball
thing? Kinda, yeah. Use the most cost -effective
model for each specific job. Don't just throw
the biggest, most expensive model at everything.
Right. Use the right tool for the job. So, GPT
-5, mini for planning and section writing. Keep
cost down there. But then upgrade to the full
GPT -5 for the final editor where quality and
formatting really matter. Smart resource allocation.
And keep experimenting. Try Claude. Try Gemini.
Find the best balance for your needs. It's not
one size fits all. It reminds me of those early
days, just using the biggest model for everything
and then getting the bill. Like using a sledgehammer
to crack a nut. Exactly. This money ball idea
is just smart business. Then the builder's code.
Best practices. Okay, give us the code. Always
build and test with a manual trigger first. Don't
rely on the schedule initially. Makes sense.
Crucially, pin your data during development.
Pin data? What's that? It saves the output of
a node. So if you tweak something later in the
workflow, you don't have to rerun all the expensive
AI calls before it. Saves time and money during
testing. Ah, clever. Like caching results. Kinda.
Use good descriptive names for your nodes. Don't
just leave them as AI Agent 3. So you know it's
what later. Customize your system prompts. Make
them specific to your industry, your audience.
Include your brand voice. And iterate. Your first
prompt is just a draft. Keep refining it. Constant
improvement. Use workflow templates if you can.
Don't reinvent the wheel. And use version control,
like GitHub, for your workflows and prompts.
Treat it like code. Good practice. Finally! The
launchpad. Hosting. For this to run reliably
on schedule, 247, you really need proper hosting,
like a managed N -A -N -A -B -S, something with
backups, scalable power. Right. It needs a solid
foundation to run reliably. So the bottom line,
the business impact. This isn't just saving time,
is it? It's a strategic asset. Absolutely. A
strategic advantage. The immediate benefits,
the time machine, are clear. Eight 12 -hour saved,
consistent content, regular engagement. Yep.
Huge wins right away. But the strategic advantages,
becoming a thought leader, that's profound. It
positions you as an expert, creates those valuable
touch points, and frees you up for high -value
work, the stuff that actually grows the business.
It lets you shift from being a writer to being
a publisher or a strategist, focusing on high
-level growth. Exactly. And long -term, it's
a compounding asset. Builds a knowledge base,
develops brand authority, establishes reliable
communication. Pays off for years. Beyond just
saving hours, what's a significant strategic
shift this system enables for someone? It transforms
you from just a writer grinding out content.
into a publisher or a strategist. You're focused
on high -level growth, not just production. Thinking
bigger picture. Okay, let's recap the big ideas.
So we've seen how this multi -agent system, using
something like NANN, can automate a whole newsletter
workflow, start to finish. It's basically a digital
content factory. You've got AI agents researching,
planning, writing, editing, all specialized.
Working with you. Yeah. And the real power comes
from that upfront planning, the smart, strategic
use of different AI models. The moneyball approach.
And constantly... tweaking those prompts. Iterative
improvement. And the result? It frees you from
the manual grind, lets you step up, become a
true publisher and strategist for your brand.
It really is pretty mind -blowing. The idea that
content gets created while you sleep. It's a
big shift. But it raises that interesting question,
doesn't it? What does this mean for human creativity?
For oversight? Yeah. How do we best leverage
this power? It's something to think about. Definitely
something to mull over. Well, thanks for joining
us for this deep dive into building a multi -agent
content team. We hope you found this exploration
as fascinating as we did. Until next time, keep
exploring, keep questioning. And keep learning.