How to Write Your First AI Agent Solution Design Document
A Step-by-Step Guide
You've just walked out of the meeting where your AI agent project got the green light.
The executives nodded, the budget was approved, and someone mentioned a "solution design document" as the next deliverable.
Now you're staring at a blank document, wondering how to turn "build an AI agent for logistics tracking" into something that actually guides development and deployment.
Here's what most people get wrong: they think solution design is about the clever AI bits—the model selection, the prompt engineering, the reasoning framework.
It’s not.
Enterprise solution design is about everything else: how it integrates, how it scales, how it fails, how it gets maintained, and how it behaves ethically under pressure.
The difference between a solution design that works and one that creates chaos isn't the sophistication of the AI—it's the thoroughness of the enterprise thinking.
Today I’ll show you some of the aspects I add to my documentation and how you can apply them for your projects.
Let’s get into it.
Why Most First AI Agent Designs Fail
There might be some of you out there that think design documents are a bit 90’s. The truth is, in the enterprise, they’re alive and well. The main reason being (other than the obvious of it being good practice to design things properly and document) that organisational governance demands it. Enterprise IT always has working groups and review boards for projects and without attention to this due diligence, your project, no matter what it is, is just a toy or a PoC.
When it comes to what works and what doesn’t, the failure patterns are predictable. Again, it doesn’t matter if it’s a web app or an agent, if you cannot document and present your solution in a cohesive manner to the stakeholders in your review cycle, you’ll not get the support you need from the technical community to implement.
There’s a few common points where agent designs run aground.
The Academic Paper Style
Detailed explanations of transformer architectures and attention mechanisms. No mention of how it connects to the CRM or what happens when the API goes down.
The Happy Path Design
Everything works perfectly, users ask reasonable questions, and the agent always has the right context. No consideration of edge cases, error handling, or system failures.
The Technology Stack
Lists of frameworks, libraries, and deployment tools. No explanation of why these choices make sense for this specific business context.
The Black Box
"The agent will use advanced reasoning to solve customer problems." No detail about what that actually means in practice or how it will be implemented.
These designs fail because they treat the AI agent as an isolated system rather than a component in a complex enterprise ecosystem. They focus on what the agent will do, not how it will do it reliably, safely, and ethically at scale. They’re also too light on the explaining of the solution - too much technical definition, and not enough “why” added.
The Enterprise Agent Reference Architecture Reality
Before you write a single line of your solution design, you need to understand what you're actually building.
An enterprise AI agent isn't just a chatbot with access to APIs. It's a distributed system with multiple layers of orchestration, integration, operational oversight, and governance.
Below is my Enterprise Agent Reference Architecture diagram. I use this as my mental model of agent design to keep me on track.
Notice how the "Agent Core" is just one component among many. The entry points, orchestration layer, guardrails, enterprise integrations, and operational services are what make it work in practice.
