This repository is a spec-driven, AI-native product development operating system where structured Markdown (knowledge layer) defines the system, AI agents execute against it to generate and run the product (execution layer), and data feeds back to continuously improve it—making the repository itself an executable blueprint for both digital and physical product creation.
It represents a new methodology for creating products and services as AI becomes the primary execution and production layer. AI is no longer limited to writing code; it is increasingly capable of conducting research, running tests, generating new materials, prototyping products, and operating automated production systems. As a result, new paradigms are emerging for how we communicate intent to AI. This product development OS is my approach to structuring that communication—translating strategy into executable specifications that AI can reliably interpret and act upon.
This repository serves as a reusable template that is copied each time a new product is created. It provides the structured, executable framework required for AI to understand, build, and manage the product and its production lifecycle. As the system evolves, I continuously refine and expand this repository to improve how effectively AI can operate within it.
But it is not just for products. My thoughts are to have a template that is applicable for anything, from starting a business, designing new products, launnching them, and creating AI agents and workflows.
In this model, changes to something (company, product, service, workflow, prcess) are made by updating the executable structure—not manually modifying code or production systems directly. The knowledge layer defines the desired state, and AI acts as the development and production engine that implements those changes.
This framework guides the full lifecycle: how to define a product, how to build it, how to produce it, and how to continuously improve it through feedback from real-world data. While immediately applicable to software, it is equally designed for physical products, as AI increasingly integrates with automated testing, prototyping, and manufacturing systems.
I organize the layers according to the 8 core functional areas of a business that I created, [as defined here](https://eddiesoehnel.com/startup-roadmap/).
* If humans need to think through it → Google Docs to allow for shared working
* If AI needs to read it to build something → Markdown in a repo
* You want to map strategy >>> structured specs >>> agent execution. Strategy is business realm (problem you solve, differertiators, moats, growth strategy), which gets turned into structures specs that AI agents use to build the product to satisfy the strategy.
1. Knowledge layer (specs/) → what the system is supposed to do and how to do it. The truth via highly structured executable knowledge that AI can understand. Structure leads to intelligence. Smarter AI models help, but if poorly structured, then failure. Well structure knowledger layers can succeed even with average models.
2. Harness layer (harness/) → the program that runs the AI. It does four things: runs the model in a loop, reads (specs/, tasks/, data-ops/), writes files (srv/), manages context (specs/), and enforces safety. That's it. It is "thin", very tightly scoped to a specific task.
2. Product layer (tasks/ and srv/) → takes the knowledge layer and executes it: develop code, develop the product, produce the product.
3. Data layer (data/) → what the system observes, stores, and learns from based on production. The data validates reality, that leads to iterate above to improve.
Intelligence is in specs/ (skills docs). Execution is in the srv/, and execution is deterministic. Deterministic is code calls, code execution, cron jobs. It just does, but does not think.
- Branch strategy: main (production), dev (active development), feature branches (experimental). Flow: feature>>dev>>main
### Structure For Files
- Use consistent headings
- Keep sections predictable. If every file has a predictable structure, AI navigation improves dramatically.
#### repo/
- README.md \ high level overview of project; but copy out the contents in this file to the overview file to allow an AI to understand the basis for this repo and its structure for product development.
- CHANGELOG.md \
- VERSION.md \
- PROJECTRULESPROCESSES.md \ agent and human rules/processes
##### specs/
*The executable structure layer, organized by main function and in order*
- agents.md \ Functionas more like a router, telling AI where to find stuff. General rules. Set up clear path where AI agents orchestrate other AI agents. When typx X task appears, load document y first. You are pointing to documents that get loaded at the right time. You do not put all of this into one document. Keep context tight.
- skill.md \ teaches model how to do something; supplies the process for doing something;
- decisions.md \ why we did what we did, why we chose A over B, etc. Architecture Decision Records (ADR). provide context, decision, risks, consequences. Document using the [decision layer and biases layer](https://docs.google.com/document/d/1VjtP-jPn-wOpE8z5QSVOdk-pPxneyuDDM6z_a9OIpYY/edit?tab=t.0)
- vision/concept validation/strategy/mission/goals/marketing/sales/distribution/goals/target customer/constraints/success metrics/.md \ use the **B2C framework**.
*The production/execution layer for AI to execute on. Code is created in files. Code is well documented and explains why the code exists, what it does, maintenance tips, gotchas if any.*
###### digital
*digital/software-based products/services*
- app/
- models/
- scripts/
- services/
- ui/
- utils/
- routes/
###### physical
*physical-based products/services*
##### data-ops/
*Data that is already a part of the system (ie.: CRM customer data) or which gets produced from the system. repo/ topline and docs/ are strategy, systems and product development are src/, data is all other functions, pulled from **B2C Framework**
