Why I Started with the MVP Output Document Before We Wrote a Line of Orchestration Code 

When most people think about building an AI system, they start with the exciting questions. 

Which model are you using? 
Which framework did you choose? 
How many agents are in the system? 
What’s the stack? 
I didn’t start there. 

Before we wrote orchestration code, before we finalized infrastructure, and before we committed to specific models, I started with an MVP Output Document. 

That was intentional. 
Because I’m not building a chatbot. 
I’m not building a campaign assistant. 
And I’m not building AI for the sake of having AI. 
I’m building a Growth Strategy Agentic System. 

And for a system like that, I believe the most important question comes first: 
What does this system need to produce that a CEO, CFO, or Chief Growth Officer would actually trust, use, and act on? 
That is what the MVP Output Document was designed to answer. 

 What is the MVP Output Document? 

It’s not a feature list. 
It’s not a backlog. 
And it’s definitely not an architecture diagram. 
It’s a document that defines the actual business outputs the system must produce. 
Not abstractly. Not eventually. Specifically. 
For me, that meant defining the things the Growth Strategy Agentic System would have to deliver in order to be useful: 

• an executive growth summary  
• the diagnostic artifacts  
• a three-year growth blueprint  
• a capital-allocation view  
• machine-readable execution briefs for AI tools and media platforms 
• and the closed-loop scorecards that tell you whether anything actually worked  

I wanted to know what the system had to produce before I let the architecture start pulling the product definition around. 
Because once a technical build gets momentum, it becomes very easy to confuse activity with clarity. 

Why start there? 

Because a Growth Strategy Agentic System can look smart long before it becomes useful. 
That’s one of the biggest traps in this space. 
If you start with models, agents, prompts, and orchestration, you can build something that looks sophisticated very quickly. It might generate elegant summaries. It might create plausible recommendations. It might even impress people in a demo. 
And it still might not produce anything that a CFO would approve, a CEO would use, or a growth team could actually execute against. 
That’s the danger. 
I didn’t want to build something that looked intelligent. 
I wanted to build something that produced decision-grade outputs. 
So instead of asking: 

• what tools should we use?  
• what agents should we create?  
• what framework should we prototype in?  

I started with: 

• what exactly should the system output?  
• who is the user?  
• what decision does it support?  
• and what would make that output acceptable?  

That changed everything. 

What did that force me to define? 

It forced me to get specific much earlier than I expected. 
Not “the system should provide insights.” 
Not “the system should improve growth planning.” 
Those are vague product ambitions. 
The MVP Output Document forced me to define things like:

• what belongs in an executive summary  
• what makes a diagnostic artifact complete  
• what a finance-grade planning surface actually needs to contain  
• what a downstream execution brief has to look like to be useful  
• and how measurement should show up in the system, not as an afterthought, but as part of the output contract itself 

That last point was especially important. 
Because once you start writing the outputs honestly, you can see very quickly whether the system is actually designed to support them. 
And in my case, that exposed gaps. 

 What did it expose? 

The biggest one was measurement. 
The first version of the output document was strong on diagnosis and planning. 
It was weaker on what happened after the action. 
That’s a common mistake, and I think a lot of systems make it. 
It’s easy to say “we’ll measure results later.” 
It’s much harder to define what the system actually needs to produce to support incrementality, scorecards, writeback, and learning. 
Once I saw that gap in the outputs, I could no longer pretend it was a Phase 3 problem. 
It became a design problem immediately. 
That’s one of the reasons I now think every serious Growth Strategy Agentic System needs a formal measurement and learning layer. 
Not because measurement sounds sophisticated, but because the outputs force it to exist. 

 Why does this matter so much for this kind of system? 

Because this isn’t just software. 
A Growth Strategy Agentic System sits in a much more sensitive place than a lot of traditional marketing tools. 
It is trying to answer questions like: 

• where profitable growth actually lives  
• what should be fixed before scaling acquisition  
• which opportunities deserve capital  
• what is real lift versus noise  
• and how strategy should be translated into action across the business  

That means the outputs have to be credible across functions. 
A system of strategy cannot get away with sounding intelligent. It has to produce outputs that are structurally sound. 
That is why I started with the document. 
Because if I could not define the outputs clearly enough that a leadership team would know how to evaluate them, then I had no business choosing infrastructure or coding the reasoning layer yet.  

How did it shape the architecture? 

More than I expected. 
Once I wrote that the capital-allocation output had to be finance-grade, it became obvious that the underlying calculations could not live inside an LLM’s narrative reasoning. They had to come from explicit, deterministic logic. 
Once I wrote that execution briefs had to be safe to dispatch to Salesforce/Google/Meta/TikTok, it became obvious that sequencing gates, guardrails, validation logic, and measurement-plan checks were not optional. 
Once I wrote that the system had to eventually reason about market opportunity, it became obvious that connector and API strategy had to be considered much earlier — because the outputs would eventually need to reflect TAM, SAM, competitive pressure, and external market context, not just internal performance. 
In other words, the output document didn’t just describe the product. 
It forced the architecture to grow up. 
That’s why I think starting with outputs is so powerful. It turns abstract product conversations into real system design decisions. 

 How did this change the way I worked with Velvetech? 

It made the collaboration much better. 
There’s a huge difference between handing an engineering team a feature list and handing them an output contract. 
A feature list says: 
build something impressive. 
An output contract says: 
build the smallest, most reliable system that can produce this artifact at this standard. 
That distinction matters. 
Once the outputs are clear, architecture becomes less ideological. It becomes practical. 

 What would I tell another founder or CMO building something like this? 

Start with the outputs. 
Not because it’s neat. 
Not because it’s efficient. 
Because it’s clarifying. 
Before you choose: 

• models  
• frameworks  
• cloud services  
• agents  
• retrieval layers  
• measurement methods  
• or connector strategy  

define what the system must actually produce. 
If you cannot describe the outputs clearly enough that: 

• a CFO would review them  
• a CEO would act on them  
• a Chief Growth Officer would use them  
• or a downstream marketing system or AI tool could consume them  

then you are not ready to pick architecture yet. 
That might feel slower in the first few weeks. 
In reality, I think it saves time everywhere after. 

Final thought 

One of the biggest lessons I’ve learned so far is that the hardest part of building a Growth Strategy Agentic System is not the AI itself. 
It is the discipline of deciding, early and clearly, what the system must actually produce. 
Most teams skip that step. 
They move straight into tooling, prompting, agents, architecture, and demos. 
I understand why. Those things feel like progress. 
But without an output contract, the system has: 

• no fixed definition of done  
• no real acceptance bar  
• and no reliable way to tell whether it is creating something a leadership team would actually use  

That is why the MVP Output Document came first. 
Not because it was administrative. 
Because it was the first real product decision.