The Rules and Agents System: Making the Product Creation Process Repeatable

Published: [CURRENT_DATE] Category: Product Creation, SaaS, Frameworks Tags: product development, SaaS, process, repeatability, AI, agents

What if your product creation process was as systematic as your code?

As a founder, I've experienced the frustration of an ad-hoc, unpredictable product creation process. It feels like we're constantly reinventing the wheel, making decisions based on gut feel rather than data, and hoping our next idea will magically take off.

But what if there was a way to make product creation as systematic and repeatable as our engineering processes? That's the core idea behind the Rules and Agents system we've developed for our SaaS Starter product creation engine.

In this article, I'll take you behind the scenes and show you how we've built a comprehensive, machine-readable system of rules, quality gates, and specialized AI agents to transform our product creation process into a well-oiled, evidence-based machine.

By the end, you'll understand how to apply these principles to make your own product creation more systematic, predictable, and defensible.

The Core Concept: Rules, Agents, and Quality Gates

The foundation of our product creation system is a set of over 300 codified rules that define and enforce our entire process. These rules cover everything from idea validation to moat design to user experience to engineering best practices.

Underpinning the rules is a multi-agent system made up of 12 specialized AI agents, each responsible for a specific phase or function in the product creation lifecycle. These agents work in a coordinated way to gather data, make decisions, and produce the key outputs required to move a new product or feature through our pipeline.

At the heart of the system are a series of quality gates that must be passed before a product or feature can progress to the next stage. These gates ensure that critical validation, strategy, and design milestones are met before any engineering work begins.

The result is a fully integrated, machine-readable system that transforms our product creation process into something as systematic and repeatable as our engineering codebase.

The Rules System: Codifying the Process

At the core of our product creation engine is a comprehensive rules system that encodes our entire process into a set of machine-readable, enforceable guidelines.

These rules cover the key phases of our product creation lifecycle:

Portfolio & Insight: Scoring ideas, validating unfair insights
Validation: Proving desirability through lo-fi tests
Moat & Retention: Designing defensibility and durable retention
Monetization: Mapping expansion revenue and pricing strategy
Design: Mapping user flows, interactions, and accessibility
Engineering: Architecture, testing, implementation, and accessibility

Each phase has a dedicated "playbook" - a set of interconnected rules that define the inputs, outputs, quality gates, and workflows for that stage. For example, our "Insight & Validation Playbook" includes rules like:

# Core Rule: Desirability First
No feature or product may enter engineering until desirability is validated via 3+ lo-fi tests

These rules are machine-readable and automatically enforced by our product creation "orchestrator" - a central coordinator that manages the flow of work through the system.

The Agent System: Specialized AI for Each Phase

Powering the rules system is a multi-agent architecture made up of 12 specialized AI agents, each responsible for a specific phase or function in the product creation lifecycle.

These agents work together in a coordinated way, gathering data, making decisions, and producing the key outputs required to move a new product or feature through our pipeline.

Here are a few examples of the agents in our system:

Insight & Narrative Strategist: Transforms raw ideas into validated insights, narratives, and emotional hooks. Outputs an "Unfair Insight" document that defines the target community, their unmet needs, and a compelling value story.

Product Strategist: Creates a desirability-first Product Requirements Document (PRD) that includes a value story, "why now" analysis, lo-fi validation plan, and early adopter profile. Runs a series of demand-validation tests to ensure the product will be desired by real users.

Moat & MRR Strategist: Designs a defensibility strategy that includes a "moat map" (2-3 moat types), a data moat thesis, a retention thesis, and an expansion revenue model. Ensures the product will remain relevant and profitable 12-36 months out.

Retention Architect: Maps the complete activation and retention journey, including habit loops, notification strategy, collaboration triggers, and renewal mechanisms. Ensures the product will keep users engaged and coming back.

Engineering Architect: Creates an Architecture Decision Record (ADR) that defines the technical structure, data model, and key tradeoffs. Ensures the engineering solution aligns with the product strategy.

These agents work in a coordinated way, with each one responsible for a specific aspect of the product creation process. The outputs of one agent feed directly into the inputs of the next, creating a seamless, end-to-end workflow.

Quality Gates: Validation at Every Step

Underpinning the rules and agents system are a series of quality gates that must be passed before a product or feature can progress to the next stage.

These gates ensure that critical validation, strategy, and design milestones are met before any engineering work begins. For example, here are the gates in our "Insight, Narrative & Desirability" phase:

Insight & Narrative Strategist completes an "Unfair Insight" brief
Product Strategist validates desirability via 3+ lo-fi tests
Portfolio Prioritizer approves the idea based on expected value
Moat & MRR Strategist confirms the defensibility strategy

Only once all four of these gates are passed can the product or feature move on to the next phase of our pipeline. This creates a series of "filters" that prevent us from building anything that hasn't been thoroughly validated.

The quality gates also serve as checkpoints where we can make "kill" or "pivot" decisions, cutting our losses on ideas that don't meet our validation criteria. This helps us focus our resources on the most promising opportunities.

Real-World Examples

To bring this framework to life, let's look at a few examples from our actual implementation:

Example 1: Unfair Insight for a Productivity App

Our Insight & Narrative Strategist identified an "unfair insight" for a new productivity app: Remote workers struggle to stay focused and motivated when working from home, but existing productivity apps are too rigid and impersonal.

The strategist produced an "Unfair Insight" document that defined the target community (remote workers), their unmet needs, and a compelling value proposition: a productivity app that adapts to each user's unique work style and psychological needs.

This insight then fed directly into the Product Strategist's work on the narrative PRD and desirability validation.

Example 2: Moat & Retention Strategy for a SaaS Marketplace

For a new SaaS marketplace, our Moat & MRR Strategist designed a multi-pronged defensibility strategy:

Network Effect Moat: By incentivizing both buyers and sellers to participate, the marketplace would create a self-reinforcing network effect.
Data Moat: The marketplace would capture rich data on buyer/seller behavior, allowing us to personalize the experience and develop advanced matching algorithms.
Switching Costs: Integrations with users' existing tools and workflows would create high switching costs, locking in both buyers and sellers.

The Retention Architect then mapped out the complete activation and retention journey, including habit-forming features, collaboration triggers, and automated renewal mechanisms.

This defensibility and retention strategy was a key input to the Engineering Architect, who designed the technical architecture to support the identified moats and retention loops.

Example 3: Accessibility for a SaaS Admin Dashboard

When designing the admin dashboard for our SaaS product, our IA Designer mapped out the complete user flows, edge cases, and emotional journey. This included considerations for keyboard-only navigation, screen readers, and other accessibility requirements.

The Accessibility Agent then performed a comprehensive WCAG 2.2 AA audit on the designs, identifying and documenting all accessibility issues that needed to be addressed.

These findings were fed back to the IA Designer and Implementer, who worked to resolve the accessibility gaps before any code was written. This ensured the final product met strict accessibility standards from the start.

Practical Application: How to Implement a Rules & Agents System

So how can you apply these principles to make your own product creation process more systematic and repeatable? Here are a few key steps:

Define Your Phases & Playbooks: Start by mapping out the key phases of your product creation lifecycle, and define a set of rules and workflows for each one. This could include phases like Ideation, Validation, Design, Engineering, and so on.
Identify Your Agents: Determine the specialized roles and functions required to execute each phase, and assign those to individual "agents" (which could be human experts, AI systems, or a combination).
Establish Quality Gates: Build a series of validation checkpoints that must be passed before moving to the next stage. These gates should ensure key milestones are met around validation, strategy, and design.
Codify Everything: Translate your rules, workflows, and quality gates into a machine-readable format that can be automatically enforced. This could be as simple as a set of Markdown documents, or as complex as a custom software system.
Integrate with Engineering: Ensure your product creation system is tightly integrated with your engineering processes. This could involve feeding technical requirements directly into your engineering backlog, or automating the handoff of designs and specifications.
Measure & Improve: Continuously monitor the performance of your system, identify bottlenecks and failures, and make iterative improvements to your rules, workflows, and agent responsibilities.

The key is to approach product creation with the same rigor, discipline, and systematic thinking that you apply to your engineering processes. By making the process repeatable and enforceable, you can dramatically improve your odds of success.

Takeaways

Here are the key takeaways from our experience building a Rules and Agents system for product creation:

Codify your process: Transform your product creation workflow into a set of machine-readable rules and quality gates.
Specialize your agents: Assign each phase or function to a dedicated AI or human agent with the right expertise.
Validate at every step: Implement a series of quality gates that must be passed before moving to the next stage.
Integrate with engineering: Ensure your product creation system is tightly coupled with your engineering processes.
Measure and improve: Continuously monitor performance and make iterative updates to your rules, workflows, and agent responsibilities.

The end result is a systematic, evidence-based approach to product creation that can dramatically improve your odds of building something people truly want - and keeping them engaged for the long haul.

What if your product creation process was as systematic and repeatable as your code? It's time to find out.

The Rules and Agents System: Making the Process Repeatable