WEBVTT

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Welcome to the Deep Dive. Imagine for a moment

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having an entire AI content team, you know, right

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there working for you. A researcher, a planner,

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writers, even an editor. Wow. All creating your

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weekly newsletter completely on autopilot. That's

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quite the picture. That's what we're diving into

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today. Okay, so today we're unpacking this really

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comprehensive guide to building a multi -agent

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automation workflow. Yeah, a whole system. Right.

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And our mission really is to understand how you

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transform that sometimes painful process of content

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creation. Oh, yeah. Tedious sometimes. Into something

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seamless, automated. Exactly. So we'll walk through

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the blueprint, the tools you need, and the step

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-by -step build, piece by piece. We'll see how

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these different AI agents actually collaborate

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from like the first idea, the research. All the

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way to the end. All the way to final delivery.

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And then we'll look at the time savings, the

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strategic advantages this brings. So, yeah, let's

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unpack this. This isn't just, you know, one simple

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prompt we're talking about. It's really a full

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digital assembly line. An assembly line. Okay.

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Think of it like a factory. Takes raw info, turns

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it into something valuable, like your newsletter.

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Right. It kicks off with what the guide calls

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an initial research phase. It's like scouting,

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finding the latest news for your topic from,

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say, the past week. So gathering the raw materials

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first. Yeah. Making sure you've got the freshest

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stuff before you even start cooking. Foundational.

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Precisely. Then, from that initial scoop, a specialized

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planning agent steps in. Okay. It analyzes that

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research. comes up with a creative title and

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picks out the maybe three most important topics

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for the newsletter. Got it. After that, there's

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a deep research phase. This goes much deeper

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on each of those topics, really comprehensive

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info gathering. Right. So moving from the kind

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of broad overview into the specifics for each

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point. Exactly. Then individual section writing

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agents get to work. They craft each section.

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And the key part here. The critical part. They're

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told to cite everything. Every fact, every piece

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of data needs a proper citation, even a clickable

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URL. A verification. Nice. Yeah. Then an editorial

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review agent takes over. It grabs those finished

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sections, adds an intro, adds a conclusion, wraps

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it all up in clean HTML. the whole pack the whole

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package and finally automated delivery sends

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a draft straight to your gmail wow and the beautiful

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part it's clean linear totally automated no hands

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needed start to finish that's a lot of coordination

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happening behind the scenes and the guide really

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emphasizes um the secret here is planning You've

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got to plan before you build. Absolutely. Like

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any complex project. Yeah. Think like an architect

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sketching out the factory blueprint. They suggest

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using a whiteboard tool like Excalibur or Miro.

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Yeah. Visual tools help a lot. Map it all out.

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So this blueprint includes what? The weekly trigger.

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Kicks it off. The raw materials intake. That's

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the initial research. Strategic planning. The

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AI planning agent. The architect. The deep research

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division. Digging into topics. Specialized writer's

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room. Final assembly and editing. And then shipping.

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A whole flow. And this planning, it's critical,

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right? It stops that, what's it called? Scope

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creep. Yeah, scope creep, where things just get

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bigger and bigger unplanned. Exactly. Yeah. And

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make sure every single component has a clear

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job. A defined purpose. So you build with intention,

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lay that groundwork, avoid problems later. So

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why is this upfront planning so crucial before

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you even touch any code or any tools? Well, that

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clear blueprint. It just prevents that scope

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creep we mentioned. And it really defines what

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each part is supposed to do precisely. Keeps

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it focused. Got it. Keeps it lean and focused.

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Absolutely. That blueprint's like your insurance

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policy. Make sure every bit of code, every agent

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configuration has a purpose. Okay. Let's talk

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tools then. the builder's toolkit right and the

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cool thing is this whole system uses a pretty

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minimal but really effective tech stack lean

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but powerful exactly yeah at the core you've

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got n8 that's n8n it's this open source platform

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lets you connect different apps build automated

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workflows it's like the central nervous system

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for our factory orchestrating everything yep

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then for research both the initial scan and the

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deep dives we use tavly ai Gmail is just the

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simple, reliable shipping department for the

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drafts. Makes sense. OpenRouter acts as this

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versatile AI switchboard, which is great because

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you get access to lots of different AI models.

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From different providers. Yeah, all through one

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account. Really handy. Oh, interesting. And the

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main brains for writing and analyzing, that's

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GPT -5 and GPT -5 Mini. The big guns and the

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slightly smaller, maybe more efficient gun. Kind

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of, yeah. And this lean stack keeps costs down,

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but you still get, like, enterprise -level automation

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power. Okay, so every automated system, it needs

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that pulse, right? A heartbeat to start things

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off? Absolutely, the starting gun. And for this

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system, that's the scheduled trigger node in

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ANAN. Correct. You set it up for, say, once a

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week, maybe Sunday at midnight. Yeah, something

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like that. Give it plenty of time to run. So

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a draft is waiting Monday morning, ready for

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review. Exactly, like setting your watch. Predictable

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rhythm. But there's a key rule here. The safety

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switch rule. Oh yeah, super important. Always

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keep the workflow set to inactive while you're

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building it. Don't turn it on too early. No way.

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Only flip it to active when you are absolutely

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sure it's built, tested, and ready to go live.

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What's the main reason for that safety switch

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rule during development? Why is that so critical?

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Well, it just stops accidental triggers while

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you're still building. You know, ensures you

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launch a tested, production -ready system, not

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something half -baked. Right. Prevents those

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oops moments. We've all been there, haven't we?

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Yeah. Testing something and suddenly it sends

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a half -done email. Exactly. Build in that safety

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net, even during development. Okay. So the heartbeat's

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set. Now we bring in the scout. That's a Tavoli

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search note. Yep. The first research step. And

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its job isn't to dig super deep at first, right?

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It's just finding the top, say. Three high -level

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news stories from the past week. That's it. Quick

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scan. Find the main relevant headlines. So you'd

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tell it your broad topic, like AI adoption for

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small businesses. Set it to look for news, past

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week, maybe max three results. Exactly. And you

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need your Tavoli API key in an ANAN, of course,

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securely stored. Right, the credentials. So this

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initial search gives you that foundation, points

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you in the right direction. Yeah. And for a pro

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upgrade, you could even make that search query

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dynamic. Have another AI figure out the best

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thing to search for each week. Ooh, okay. Hyper

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-focused newsletters. Interesting. Now, the scout

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brought the raw intel. Time for the architect.

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Another AI agent node. This one creates the actual

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plan for the newsletter. Correct. The master

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blueprint. We use something cost -effective but

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smart, like GPT -5 Mini, maybe via open router

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again. Okay. For the user message, we package

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the research cleanly, use a formatting function.

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It's like a little reusable program. To present

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the info consistently to the AI. Exactly. Much

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more scalable than doing it manually. And the

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system prompt, that's the architect's job description.

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Something like, you're an expert newsletter planner.

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Make a creative title. Find the main topics.

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Keep topics short, three to five words. Clear

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instructions. But here's the really cool part.

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We use Anaheim's structured output with a JSON

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schema. Okay, JSON schema. Sounds technical.

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What does that mean in practice? Think of it

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like giving the AI a very specific form to fill

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out. It forces the AI to give you the title and

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topics in separate, clean data fields. So no

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more trying to pick apart messy paragraphs of

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text from the AI. Exactly. No parsing needed.

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It's structured from the start. This is a non

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-negotiable pro move. Guarantees clean, usable

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data downstream. Saves so many headaches. Why

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is using that structured output for the architect

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considered such a non -negotiable pro move? Because

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it guarantees clean, usable data. like getting

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a perfect blueprint, not just a messy sketch,

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no messy text parsing needed. Takes the guesswork

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out, ensures reliability for the next step. Precisely.

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You know exactly what format the data will be

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in. Okay, architect's done the blueprint. Now

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the investigative journalist's time for the deep

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dive. Yeah. So first, the assignment desk. That's

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a split -out node in NAN. Split -out node. It

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takes the single list of three topics from the

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architect and splits them into three separate

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items. Ah, so each topic can be handled independently.

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Critical step, yeah. Then each topic goes to

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another Tavli search node. This one's configured

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as the investigative journalist. Okay. The search

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query is dynamic now, focusing on just one topic.

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Topic type is general for broader research. Not

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just news this time. Right. And crucially, you

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enable... include raw content. To get the full

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text. Yeah, the full article text. That's the

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raw material the writers need. Got it. So deep

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research done. Now bring in the writers, the

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writers room. Yep. Another AI agent node acts

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as one of these specialist writers. And because

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the topics were split. This runs three times.

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Three separate times, once for each topic. We

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stick with GPT -5 Mini here. For consistency,

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cost efficiency. Makes sense. The user message

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uses another formatting function, gives the writer

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the topic heading and all that raw research text.

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Okay, and the prompt, the instructions for this

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writer. That's its prime directive, non -negotiable

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rules. You're a professional newsletter section

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writer. Always include a clear section heading.

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Right. Do not write an overall title, intro,

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or conclusion. Focus. Total focus. If you reference

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facts, you must cite sources with clickable URLs.

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Do not invent citations. Don't make stuff up.

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Crucial. Yeah, absolutely. And honestly, the

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most important part is often telling the AI what

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not to do. It forces specialization. Yeah, I

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can see that. I still wrestle with prompt drift

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myself sometimes, getting the AI to just stick

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to the task. Me too. So those precise negative

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instructions, vital. Keep it on track. And a

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pro tip. You can even tell it to create a separate

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JSON array of all the facts and sources. Makes

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checking them super easy later. Oh, that's smart.

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So what's the biggest benefit of telling the

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AI writer what not to do in its prompt? It just

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forces the AI to specialize, ensures it really

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excels at its one single specific task instead

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of trying to do too much. Laser focus. Okay,

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writers have done their sections. Time for the

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editor -in -chief. Assembly and final polish

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time. First, an aggregate node. Aggregate. Yeah,

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it just combines those three separate written

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sections back into one single item. Puts them

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together. Got it. Then the final AI agent, the

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editor -in -chief. Right. And for this step,

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we upgrade. Use the full GPT -5 model. Why the

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upgrade here? Better writing quality, more nuance,

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and it's great at advanced HTML formatting. Okay,

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so worth the extra cost for the final polish.

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Definitely. You brief it with the newsletter

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title and those combined sections. Tell it. Add

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an engaging intro, a strong conclusion, format,

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and clean HTML with working links. Make it look

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professional. And structured output again? Yep.

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Two fields. Subject for the email subject line

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and content for the HTML body. Nice and clean.

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And another pro -level upgrade. Give it a brand

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voice document. Tell it to read that and make

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sure the draft sounds like you. Ensure consistency.

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Very cool. So the polished product is ready.

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Now the shipping department. Time to send it

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off. A Gmail note at the end. Yep. End of the

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line. Simple configuration, but one critical

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choice. Which is? The action must be set to create

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draft, not send email. Oh, okay. Create draft.

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You connect the dynamic subject and content from

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the editor, set email type to HTML to keep the

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formatting, send it to your own email address.

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And that create draft setting. Why is that so

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vital? It's that non -negotiable safety feature,

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the human in the loop. It gives you that final

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chance to review everything before it goes out,

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check for errors, maybe add a personal touch,

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keep control. Why is setting the Gmail action

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to create draft so critical for this automated

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system? It's that crucial safety net, that non

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-negotiable human in the loop allowing for that

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vital final review before anything gets published.

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Keeps you in the driver's seat for the final

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check, amplifies effort, doesn't just replace

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it. Exactly. You maintain control. Okay, so we've

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built this thing. A complete content factory

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delivering professional newsletters. Right, your

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inbox. The spec sheet sounds impressive. Professional

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structure, good title, intro, conclusion, three

00:12:37.039 --> 00:12:39.500
focus sections with citations. Yeah, evidence

00:12:39.500 --> 00:12:43.080
-based, clean HTML, ready to go. But the real

00:12:43.080 --> 00:12:46.960
game changer, the ROI is the time saved, right?

00:12:47.039 --> 00:12:49.200
Massive time savings. That's the core benefit.

00:12:49.419 --> 00:12:52.080
It just wipes out, what, 8 to 12 hours of manual

00:12:52.080 --> 00:12:55.220
work every week? Wow, 8 to 12 hours every week.

00:12:55.240 --> 00:12:58.419
That's incredible. Yeah, think about it. automating

00:12:58.419 --> 00:13:00.679
all that tedious research, writing, editing,

00:13:00.860 --> 00:13:03.279
formatting. That doesn't just save time. It completely

00:13:03.279 --> 00:13:05.379
frees you up instead of being chained to the

00:13:05.379 --> 00:13:07.460
keyboard producing content. You could be doing

00:13:07.460 --> 00:13:10.720
higher level stuff, strategy, talking to customers,

00:13:10.899 --> 00:13:13.679
exploring new ideas. It shifts your role from

00:13:13.679 --> 00:13:16.759
producer to... strategist, innovator. Exactly.

00:13:17.039 --> 00:13:19.620
And for performance tuning, making it even better.

00:13:19.820 --> 00:13:21.639
You can add things like a Google Sheet dashboard,

00:13:22.039 --> 00:13:24.360
log everything, track results, use it to tweak

00:13:24.360 --> 00:13:26.679
your prompts. A feedback loop. Yep. Add safety

00:13:26.679 --> 00:13:30.240
features like retry logic for API calls. If Tavoli

00:13:30.240 --> 00:13:32.580
fails, maybe try perplexity automatically as

00:13:32.580 --> 00:13:35.279
a backup. Redundancy. Smart. Upgrade the engine.

00:13:35.460 --> 00:13:38.159
Use multiple research sources. Add custom HTML

00:13:38.159 --> 00:13:40.679
CSS for perfect branding. Take it from good to

00:13:40.679 --> 00:13:43.419
great. Now the professional playbook. Running

00:13:43.419 --> 00:13:46.279
it like a pro. This means adopting that moneyball

00:13:46.279 --> 00:13:49.879
approach to AI models. Moneyball? Like the baseball

00:13:49.879 --> 00:13:52.860
thing? Kinda, yeah. Use the most cost -effective

00:13:52.860 --> 00:13:55.399
model for each specific job. Don't just throw

00:13:55.399 --> 00:13:57.700
the biggest, most expensive model at everything.

00:13:57.919 --> 00:14:01.460
Right. Use the right tool for the job. So, GPT

00:14:01.460 --> 00:14:04.490
-5, mini for planning and section writing. Keep

00:14:04.490 --> 00:14:06.769
cost down there. But then upgrade to the full

00:14:06.769 --> 00:14:09.149
GPT -5 for the final editor where quality and

00:14:09.149 --> 00:14:11.850
formatting really matter. Smart resource allocation.

00:14:12.149 --> 00:14:15.669
And keep experimenting. Try Claude. Try Gemini.

00:14:15.889 --> 00:14:18.470
Find the best balance for your needs. It's not

00:14:18.470 --> 00:14:20.590
one size fits all. It reminds me of those early

00:14:20.590 --> 00:14:23.429
days, just using the biggest model for everything

00:14:23.429 --> 00:14:26.409
and then getting the bill. Like using a sledgehammer

00:14:26.409 --> 00:14:29.370
to crack a nut. Exactly. This money ball idea

00:14:29.370 --> 00:14:32.110
is just smart business. Then the builder's code.

00:14:32.559 --> 00:14:34.940
Best practices. Okay, give us the code. Always

00:14:34.940 --> 00:14:37.960
build and test with a manual trigger first. Don't

00:14:37.960 --> 00:14:39.740
rely on the schedule initially. Makes sense.

00:14:39.980 --> 00:14:42.259
Crucially, pin your data during development.

00:14:42.559 --> 00:14:45.279
Pin data? What's that? It saves the output of

00:14:45.279 --> 00:14:47.139
a node. So if you tweak something later in the

00:14:47.139 --> 00:14:49.059
workflow, you don't have to rerun all the expensive

00:14:49.059 --> 00:14:52.200
AI calls before it. Saves time and money during

00:14:52.200 --> 00:14:55.600
testing. Ah, clever. Like caching results. Kinda.

00:14:55.879 --> 00:14:58.639
Use good descriptive names for your nodes. Don't

00:14:58.639 --> 00:15:01.220
just leave them as AI Agent 3. So you know it's

00:15:01.220 --> 00:15:03.659
what later. Customize your system prompts. Make

00:15:03.659 --> 00:15:05.580
them specific to your industry, your audience.

00:15:05.860 --> 00:15:08.980
Include your brand voice. And iterate. Your first

00:15:08.980 --> 00:15:11.700
prompt is just a draft. Keep refining it. Constant

00:15:11.700 --> 00:15:14.120
improvement. Use workflow templates if you can.

00:15:14.179 --> 00:15:17.039
Don't reinvent the wheel. And use version control,

00:15:17.240 --> 00:15:19.139
like GitHub, for your workflows and prompts.

00:15:19.320 --> 00:15:22.669
Treat it like code. Good practice. Finally! The

00:15:22.669 --> 00:15:26.090
launchpad. Hosting. For this to run reliably

00:15:26.090 --> 00:15:29.129
on schedule, 247, you really need proper hosting,

00:15:29.289 --> 00:15:31.990
like a managed N -A -N -A -B -S, something with

00:15:31.990 --> 00:15:34.950
backups, scalable power. Right. It needs a solid

00:15:34.950 --> 00:15:37.450
foundation to run reliably. So the bottom line,

00:15:37.549 --> 00:15:40.289
the business impact. This isn't just saving time,

00:15:40.330 --> 00:15:42.470
is it? It's a strategic asset. Absolutely. A

00:15:42.470 --> 00:15:44.590
strategic advantage. The immediate benefits,

00:15:44.870 --> 00:15:48.029
the time machine, are clear. Eight 12 -hour saved,

00:15:48.289 --> 00:15:51.009
consistent content, regular engagement. Yep.

00:15:51.230 --> 00:15:54.049
Huge wins right away. But the strategic advantages,

00:15:54.370 --> 00:15:58.850
becoming a thought leader, that's profound. It

00:15:58.850 --> 00:16:01.529
positions you as an expert, creates those valuable

00:16:01.529 --> 00:16:03.750
touch points, and frees you up for high -value

00:16:03.750 --> 00:16:05.990
work, the stuff that actually grows the business.

00:16:06.230 --> 00:16:08.669
It lets you shift from being a writer to being

00:16:08.669 --> 00:16:11.669
a publisher or a strategist, focusing on high

00:16:11.669 --> 00:16:13.970
-level growth. Exactly. And long -term, it's

00:16:13.970 --> 00:16:16.990
a compounding asset. Builds a knowledge base,

00:16:17.110 --> 00:16:19.289
develops brand authority, establishes reliable

00:16:19.289 --> 00:16:22.929
communication. Pays off for years. Beyond just

00:16:22.929 --> 00:16:25.590
saving hours, what's a significant strategic

00:16:25.590 --> 00:16:28.509
shift this system enables for someone? It transforms

00:16:28.509 --> 00:16:31.070
you from just a writer grinding out content.

00:16:31.519 --> 00:16:33.879
into a publisher or a strategist. You're focused

00:16:33.879 --> 00:16:36.480
on high -level growth, not just production. Thinking

00:16:36.480 --> 00:16:38.620
bigger picture. Okay, let's recap the big ideas.

00:16:38.840 --> 00:16:40.960
So we've seen how this multi -agent system, using

00:16:40.960 --> 00:16:44.139
something like NANN, can automate a whole newsletter

00:16:44.139 --> 00:16:46.639
workflow, start to finish. It's basically a digital

00:16:46.639 --> 00:16:48.899
content factory. You've got AI agents researching,

00:16:49.100 --> 00:16:51.299
planning, writing, editing, all specialized.

00:16:51.620 --> 00:16:54.139
Working with you. Yeah. And the real power comes

00:16:54.139 --> 00:16:56.980
from that upfront planning, the smart, strategic

00:16:56.980 --> 00:16:59.860
use of different AI models. The moneyball approach.

00:17:00.059 --> 00:17:03.240
And constantly... tweaking those prompts. Iterative

00:17:03.240 --> 00:17:05.380
improvement. And the result? It frees you from

00:17:05.380 --> 00:17:08.119
the manual grind, lets you step up, become a

00:17:08.119 --> 00:17:11.279
true publisher and strategist for your brand.

00:17:11.619 --> 00:17:14.259
It really is pretty mind -blowing. The idea that

00:17:14.259 --> 00:17:16.259
content gets created while you sleep. It's a

00:17:16.259 --> 00:17:18.420
big shift. But it raises that interesting question,

00:17:18.579 --> 00:17:21.500
doesn't it? What does this mean for human creativity?

00:17:21.900 --> 00:17:28.430
For oversight? Yeah. How do we best leverage

00:17:28.430 --> 00:17:30.950
this power? It's something to think about. Definitely

00:17:30.950 --> 00:17:32.710
something to mull over. Well, thanks for joining

00:17:32.710 --> 00:17:35.430
us for this deep dive into building a multi -agent

00:17:35.430 --> 00:17:38.029
content team. We hope you found this exploration

00:17:38.029 --> 00:17:41.269
as fascinating as we did. Until next time, keep

00:17:41.269 --> 00:17:43.210
exploring, keep questioning. And keep learning.
