📦 Agentic SaaS Product Blueprint¶

🧭 Introduction to Agentic SaaS Product Blueprints¶

The Agentic SaaS Product Blueprint is a high-level, AI-interpretable specification that defines the entire structure, logic, and behavior of a multi-edition SaaS platform. It captures the product’s editions, features, shared services, and tenant-aware customizations, serving as the semantic contract for how the platform is generated and evolved across agents.

This blueprint type is not a code template — it is the source-of-truth orchestration document that enables agents to:

Understand the product’s business model
Generate the .NET Core platform scaffold
Create edition-specific service blueprints
Produce test cases, documentation, pipelines, and observability configurations
Support multi-tenant runtime configuration and edition inheritance

It serves as the semantic root node from which all downstream blueprints (services, pipelines, docs, etc.) branch.

🧠 Why It Exists¶

Challenge in Traditional SaaS	Solution via Product Blueprint
SaaS editions are scattered in code/config	Centralized YAML edition model
Features are hardcoded or flagged inconsistently	Declarative feature matrix with DSL links
Shared services are duplicated	Generated platform template with common modules
Tenant customization is manual	Declarative, pluggable tenant overrides
No end-to-end traceability	Blueprint IDs, traceIds, and studio graph mapping
CI/CD doesn’t reflect edition logic	Pipelines generated per edition with blueprint awareness

🔗 Role in the Factory¶

graph TD
    Vision --> SaaSProductBlueprint
    SaaSProductBlueprint --> PlatformTemplate
    SaaSProductBlueprint --> EditionBlueprints
    EditionBlueprints --> ServiceBlueprints
    SaaSProductBlueprint --> TestBlueprints
    SaaSProductBlueprint --> ObservabilityBlueprints

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This blueprint is the root-level semantic object from which an entire agentic SaaS ecosystem is generated — including services, docs, APIs, pipelines, and observability dashboards.

📎 Example Artifact Families Triggered¶

Artifact Type	Derived From
`ConnectSoft.Platform.sln`	Generated from product blueprint metadata
`BookingEdition.yaml`	Generated edition blueprint
`src/Platform.Shared/Edition.cs`	Shared model class tied to edition DSL
`tests/edition-tests.feature`	Generated per feature matrix
`pipelines/ci-booking.yml`	Edition-aware pipeline

🧠 Key Agents Involved¶

Agent Name	Role in Blueprint Realization
Product Manager Agent	Defines editions, features, and platform modules
Platform Engineer Agent	Generates the initial .NET Core solution scaffold
QA Engineer Agent	Scopes tests per edition/features
Knowledge Engineer Agent	Indexes blueprint into memory + trace network
DevOps Agent	Configures edition-aware CI/CD per blueprint

✅ Summary¶

The Agentic SaaS Product Blueprint allows the AI Software Factory to:

Define a multi-edition SaaS platform semantically
Generate consistent platform scaffolds with shared and scoped logic
Coordinate the actions of multiple specialized agents
Serve as the single source of truth for editions, tenants, and services
Maintain full traceability, memory awareness, and regeneration capability

🧭 Blueprint Role in Platform Generation¶

The Agentic SaaS Product Blueprint plays the central role in generating the core platform structure for the SaaS solution — as a semantic, edition-aware orchestration blueprint.

Rather than generating a full mono-repo of source code, the blueprint instructs agents to:

Scaffold a central .NET Core platform solution
Reference external services as NuGet packages, container images, or remote modules
Include configuration artifacts, edition wiring, and shared utilities
Lay out the project for multi-edition orchestration, not tightly-coupled microservices

This pattern mirrors .NET Aspire-style project orchestration and supports polyrepo SaaS architectures.

🏗️ What the Blueprint Generates¶

Artifact	Description
`ConnectSoft.Platform.sln`	The orchestrating solution that links to internal and external components
`src/Platform.Orchestration`	Configures external microservices (via image/package refs)
`src/Platform.Shared/`	Shared libraries (auth, observability, edition model, etc.)
`src/Editions/{Edition}`	Placeholder folders for edition-specific config or logic
`infra/docker-compose.yml`	References to microservices via image registry
`.env`, `launchSettings.json`	Wiring for local debugging or tenant emulation
`feature-matrix.yaml`	Declares which services support which editions/features

🧬 Blueprint-Driven Architecture (Visualized)¶

flowchart TD
    SaaSProductBlueprint["🧠 SaaS Product Blueprint"]
    SaaSProductBlueprint --> PlatformTemplate["🛠️ .NET Platform Solution"]
    PlatformTemplate --> SharedLibs["📦 Platform.Shared"]
    PlatformTemplate --> Editions["📂 Editions/"]
    PlatformTemplate --> ServiceRefs["🔗 External Microservices"]
    PlatformTemplate --> Observability["📈 Observability Configs"]
    PlatformTemplate --> CI["🔁 CI/CD Pipeline Blueprint"]

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🔗 Referencing External Services¶

Reference Type	Format in Generated Artifacts
NuGet Packages	`ConnectSoft.Microservice.Booking.dll` in `.csproj`
Container Images	`booking-svc:1.4.2` in `docker-compose.yml`
Remote Project Feeds	External git-based microservices via `PackageReference`
Service Registrations	Declared in `ServiceOrchestrator.cs` or DI registries

This enables clean separation of concerns while maintaining composability.

🧠 Agent Collaboration in Generation¶

Agent	Role in Platform Generation
Platform Engineer Agent	Generates the solution scaffold, container orchestrator, edition hooks
Edition Blueprint Agent	Generates YAML + config structure per edition
DevOps Agent	Configures Docker Compose, deployment targets, pipelines
Observability Agent	Inserts spans, metrics, and tracing setup

🔍 Example Project Structure (Generated)¶

/ConnectSoft.Platform/
├── ConnectSoft.Platform.sln
├── src/
│   ├── Platform.Shared/
│   ├── Platform.Orchestration/
│   ├── Editions/
│   │   └── vet-pro/
├── infra/
│   ├── docker-compose.yml
│   ├── monitoring/
├── feature-matrix.yaml
├── tests/
└── .env

✅ Summary¶

The SaaS Product Blueprint drives generation of an orchestration-focused solution, not a monolith.
Services are linked via package references, image registries, and service configuration, enabling polyrepo architectures.
Inspired by .NET Aspire, this model supports both development-time composition and deployment-time integration.
The blueprint ensures that each SaaS platform is modular, edition-aware, and orchestrated from a single semantic source.

🧭 When to Use: Scenarios and Triggers¶

The Agentic SaaS Product Blueprint is not used for every project. It’s invoked by the AI Software Factory when the platform must support product-level orchestration, edition-aware delivery, and multi-tenant configuration across a modular system.

This blueprint is foundational when building real SaaS products, especially those designed to scale across industries, customer types, tiers, or tenants.

✅ When to Use This Blueprint¶

Scenario	Why the Blueprint Is Required
🆕 Launching a new SaaS product	Acts as the product’s orchestration layer, linking all services and editions
🏷️ Creating tiered editions (Basic, Pro, Enterprise)	Enables declarative feature matrices and edition-specific logic
🧩 Verticalizing an existing platform	Split logic by industry verticals while reusing shared services
🏢 White-label or reseller support	Supports branding, config, and pipeline overrides per tenant or partner
🧠 Full AI-led product generation	Agents require this blueprint to reason across service boundaries
🌍 Multi-tenant platform deployment	Wires tenant identity, edition activation, and runtime policy enforcement
🧪 Feature flag lifecycle management	Blueprint defines what features exist, where, and under which edition toggles
🔁 Edition-based CI/CD	Generates pipelines and tests scoped by edition and feature set
🚀 Platform scaffold without tight code coupling	Generate solution with service references, not embedded implementations

🚫 When Not to Use¶

Situation	Alternative Approach
🔧 Building a single, standalone microservice	Use a `service-blueprint.yaml` instead
🛠️ Building a tool or CLI, not a SaaS product	Use a `tool-blueprint.yaml`
🧪 Writing tests for an existing service	Use `test-blueprint.yaml` with scoped DSL input
📄 Generating documentation only	Use `doc-blueprint.yaml`
🧱 Reusing a service in many SaaS products	Define it in a shared registry and reference from product blueprints

📦 Blueprint Promotion Trigger Points¶

Trigger Event	Agent Behavior
✅ New product idea approved by Product Manager Agent	Generates initial `agentic-saas-product-blueprint.yaml`
🔄 Existing product being refactored into editions	Product Owner Agent generates edition list and feature matrix
💡 Enterprise Architect maps new vertical blueprint	Triggers edition-based service refactoring
🧠 Feature Planner Agent emits `FeatureMatrixCreated`	Product Blueprint gets enriched with DSL references

DSL Type	Purpose
`ProductDefinition.yaml`	Holds product ID, name, default behavior
`EditionMatrix.yaml`	Defines which features belong to which editions
`FeatureCatalog.yaml`	Master list of known capabilities
`TenantProfile.yaml`	Config-level overrides, branding, constraints

✅ Summary¶

Use the Agentic SaaS Product Blueprint when:

You're creating or evolving a multi-edition, multi-tenant, or multi-service SaaS product.
You want AI agents to coordinate generation across shared libraries, pipelines, docs, and tests.
You need feature matrix governance, scalable configuration, and platform-level traceability.

🧭 Core Concepts: Product, Edition, Tenant¶

The Agentic SaaS Product Blueprint centers around three semantic pillars:

Product – the root definition of the SaaS platform itself
Edition – a scoped variant of the product (e.g., Basic, Pro, Enterprise)
Tenant – a customer or client organization using the product under a specific edition

Understanding these three concepts is crucial for blueprinting multi-tenant, edition-aware, and AI-orchestrated SaaS solutions.

🔷 1. Product¶

A Product is the global contract of the platform:

Describes what the platform offers
Defines the shared services and feature catalog
Enumerates supported editions
Anchors traceability, observability, and semantic regeneration

productId: connectsoft-appointments
name: ConnectSoft Appointments Platform
description: Multi-tenant SaaS for booking and schedule optimization
defaultEdition: pro
dslRefs:
  - dsls/features/appointments.yaml
  - dsls/entities/tenant.yaml

🟦 2. Edition¶

An Edition is a variant or tier of the product. It can:

Extend or override features
Link to different pipelines, observability hooks, or UI variants
Be reused or inherited by multiple tenants

editions:
  - id: basic
    name: Basic Tier
    features: [BookAppointment, ViewCalendar]

  - id: pro
    name: Professional Tier
    features: [BookAppointment, CancelAppointment, Analytics]
    extends: basic

  - id: enterprise
    name: Enterprise Tier
    features: [Integrations, SLA, CustomBranding]
    extends: pro

✅ Editions form a semantic inheritance tree, enforced and visualized by the AI Factory.

🟨 3. Tenant¶

A Tenant is a runtime or config-level consumer of the platform:

Is assigned to one edition
Can override config, branding, limits, or UI at runtime
May inherit default edition behavior or override it

tenantId: vetclinic-123
name: VetClinic Chicago
edition: pro
overrides:
  theme: green
  maxAppointmentsPerDay: 50
  showIntegrations: false

🔁 Relationship Between Concepts¶

graph TD
    Product --> EditionBasic
    Product --> EditionPro
    EditionPro --> EditionEnterprise
    EditionBasic --> Tenant1
    EditionPro --> Tenant2
    EditionEnterprise --> Tenant3

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Entity	Owns/Defines	Can Extend	Used By
Product	All services, features, DSLs	—	All editions
Edition	A scoped set of product capabilities	Yes	One or more tenants
Tenant	Runtime consumer with config/profile	No	—

🔗 Referenced DSLs¶

DSL	Description
`EditionDSL.yaml`	Edition definitions & metadata
`TenantProfileDSL.yaml`	Tenant-specific config & flags
`FeatureCatalogDSL.yaml`	All available product features

✅ Summary¶

The Product is the semantic root.
Editions define functional and behavioral slices of the product.
Tenants are consumers of editions, with optional overrides.
This structure is what allows the AI Software Factory to generate scalable, modular, and personalized SaaS platforms from a single blueprint.

🧭 Agentic Collaboration Overview¶

The Agentic SaaS Product Blueprint is not produced or consumed by a single AI agent. Instead, it is the collaborative output and shared orchestration input for an entire cluster of agents, each responsible for generating, validating, or extending different parts of the SaaS platform.

This cycle outlines which agents participate, what roles they perform, and how they collaborate in blueprint-driven workflows.

👥 Key Agents and Their Responsibilities¶

Agent Name	Responsibility in Product Blueprint Lifecycle
Product Manager Agent	Defines core product scope, editions, features, and roadmap
Edition Blueprint Agent	Extracts edition-specific logic and triggers sub-blueprints
Platform Engineer Agent	Generates the orchestration solution template (.NET Core)
Solution Architect Agent	Maps high-level edition and feature breakdown to services
Feature Matrix Agent	Produces the edition-feature matrix and DSL references
QA Engineer Agent	Maps features to test blueprints per edition
Observability Agent	Inserts trace points, spans, and logs based on blueprint scope
DevOps Agent	Generates CI/CD pipelines with edition-aware stages
Documentation Agent	Generates markdown docs, changelogs, diagrams
Feedback Agent	Collects feedback signals and routes blueprint updates
Knowledge Management Agent	Indexes blueprint in memory with semantic traceability

🧭 Collaborative Blueprint Flow¶

flowchart TD
    Vision --> ProductManager
    ProductManager --> SaaSProductBlueprint
    SaaSProductBlueprint --> EditionAgent
    SaaSProductBlueprint --> PlatformEngineer
    SaaSProductBlueprint --> FeatureMatrixAgent
    SaaSProductBlueprint --> DevOpsAgent
    SaaSProductBlueprint --> QAAgent
    SaaSProductBlueprint --> ObservabilityAgent
    SaaSProductBlueprint --> DocumentationAgent
    SaaSProductBlueprint --> KnowledgeAgent
    FeedbackLoop --> ProductManager

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🤖 Orchestration Highlights¶

Event-driven coordination: Agents communicate using events like EditionBlueprintRequested, TestMatrixCompleted, PlatformTemplateGenerated
Shared memory: Blueprint, DSLs, and feedback are stored in long-term memory for cross-agent reuse
Traceable spans: Each agent action adds spans tagged with productId, editionId, and traceId

📎 Sample Agent Prompts Involved¶

Agent	System Prompt Snippet
Product Manager Agent	“Create a SaaS product with 3 editions and a calendar feature.”
QA Agent	“For each edition, create SpecFlow scenarios for `BookAppointment`.”
DevOps Agent	“Create pipelines with deployment scopes per edition: basic, pro, enterprise.”
Observability Agent	“Emit spans for each command and edition switch point.”

🧠 Collaboration Trace Example¶

traceId: product::connectsoft-appointments
events:
  - ProductManagerAgent.createdBlueprint
  - PlatformEngineerAgent.generatedSolution
  - EditionBlueprintAgent.createdEditionBlueprint[v1]
  - QAAgent.generatedScenarios[book,cancel]
  - DevOpsAgent.generatedPipeline[ci-deploy-pro]

Each of these is recorded in memory and observable across agents and UI.

✅ Summary¶

The SaaS Product Blueprint is a shared artifact across many agents.
Each agent reads or contributes to its structure, extending the scope semantically.
Coordination is driven by events, traceIds, and shared memory entries.
This enables decentralized, scalable SaaS product engineering, even across thousands of editions and services.

🧭 Generated .NET Core Platform Template¶

One of the most direct outputs of the Agentic SaaS Product Blueprint is the Platform Template — a scaffolded, structured, AI-generated .NET Core solution that acts as the orchestration entry point for the product.

Rather than duplicating microservices or tightly coupling their implementations, the platform template:

References services as NuGet packages, gRPC clients, or container images
Wires multi-edition behavior and runtime policies
Provides a unified solution experience for devs, agents, and tools
Encodes observability, DI, configuration, edition model, and shared logic

📦 What the Platform Template Contains¶

Component	Description
`ConnectSoft.Platform.sln`	Root solution generated for the product blueprint
`src/Platform.Orchestration/`	DI, service references, edition loader, runtime policy config
`src/Platform.Shared/`	Shared libraries: edition model, tenant model, feature flags
`src/Editions/`	Folder for edition-specific bootstrapping, placeholders, overrides
`infra/docker-compose.yml`	Defines runtime services via container images or image tags
`.env`, `launchSettings.json`	Local dev variables for tenants, editions, integrations
`feature-matrix.yaml`	Semantic mapping of features → editions → service responsibility
`Platform.Documentation/`	Markdown docs, architectural diagrams, edition changelogs

🧬 Example Folder Structure¶

/ConnectSoft.Platform/
├── ConnectSoft.Platform.sln
├── src/
│   ├── Platform.Orchestration/
│   ├── Platform.Shared/
│   ├── Editions/
│   │   └── enterprise/
│   │       └── EditionOverrides.cs
├── infra/
│   ├── docker-compose.yml
│   ├── monitoring/
├── tests/
│   └── EditionScenarios/
├── feature-matrix.yaml
└── Platform.Documentation/

🔗 Integration Style (Aspire-Inspired)¶

Integration Method	Description
🧱 Container Image	Referenced in `docker-compose.yml` (e.g., `booking-svc:v1.4.1`)
📦 NuGet Package	Added to `.csproj` via `PackageReference` (e.g., `ConnectSoft.Booking`)
🔗 Remote Service Proxy	Bound via gRPC/HTTP client interfaces

🔧 Services remain loosely coupled to allow modular product scaling and edition reuse.

⚙️ Edition-Aware Configuration in Template¶

The blueprint predefines Edition.cs and Tenant.cs with:

Feature toggles per edition
Runtime behavior switching
Tenant scope, plan ID, limits, SLA metadata
Edition/tenant awareness wired into DI and config

if (tenant.Edition == Edition.Pro)
    services.AddScoped<IBookingValidator, ProBookingValidator>();
else
    services.AddScoped<IBookingValidator, BasicBookingValidator>();

🧠 Agent Responsibility Breakdown¶

Agent	Role in Template Generation
Platform Engineer Agent	Generates solution file, shared libs, DI, editions
Feature Matrix Agent	Injects edition-feature matrix into config logic
Edition Blueprint Agent	Places placeholders or config overrides per edition
DevOps Agent	Initializes local compose stack + CI build context

✅ Summary¶

The platform template is not an application, but a configurable, composable entry point for the SaaS product.
Its .NET Core solution supports edition switching, tenant overrides, and remote service composition.
It enables agents to independently manage and reason about the SaaS product in modular, scalable units.
Generated directly from the product blueprint, it is regenerable, extensible, and traceable.

🧭 Platform Template Structure¶

The .NET Core Platform Template produced by the Agentic SaaS Product Blueprint has a highly structured layout. It reflects Clean Architecture, DDD principles, edition separation, and modular orchestration — enabling multi-agent contribution, traceable regeneration, and tenant-aware customization.

This cycle describes the generated folder and project structure, how it reflects blueprint semantics, and how it connects to downstream microservice artifacts.

📂 Top-Level Directory Layout¶

ConnectSoft.Platform/
├── ConnectSoft.Platform.sln
├── src/
│   ├── Platform.Orchestration/
│   ├── Platform.Shared/
│   ├── Editions/
│   └── ExternalServices/
├── tests/
│   └── EditionScenarios/
├── infra/
│   ├── docker-compose.yml
│   ├── observability/
├── blueprints/
│   └── agentic-saas-product-blueprint.yaml
├── docs/
│   ├── architecture.md
│   ├── editions.md
│   └── tenants.md
├── feature-matrix.yaml
└── .env

📦 Project Breakdown¶

Project / Folder	Description
`Platform.Orchestration/`	Main startup project; wires edition, DI, config
`Platform.Shared/`	Models: `Edition`, `Tenant`, `FeatureFlag`, constants, enums
`Editions/`	Edition-specific configs, overrides, and startup hooks
`ExternalServices/`	Service proxies, gRPC clients, REST wrappers (not service code)
`tests/EditionScenarios/`	SpecFlow or BDD test cases scoped by edition or feature
`infra/docker-compose.yml`	Runtime service stack defined via image versions and dependencies
`blueprints/`	Stores generated blueprint YAMLs for regeneration and traceability
`docs/`	Markdown docs and diagrams generated by Documentation Agent

🧩 Key Blueprint-Aligned Assets¶

Artifact	Source in Blueprint
`feature-matrix.yaml`	From `features` block in product blueprint
`Editions/{id}/config.cs`	From each `edition` block
`Platform.Shared/Edition.cs`	Generated from `dslRefs` + edition definitions
`docs/editions.md`	From rendered blueprint summaries
`Platform.Orchestration/Startup.cs`	Autowires tenant + edition model in DI

🧱 Sample Startup Registration (AI-Generated)¶

services.ConfigureEditionServices(configuration);
services.AddTenantContext();
services.AddObservability("connectsoft-platform");
services.AddServiceReference<IBookingService>("booking-svc");

This logic is generated based on the blueprint traceId, DSLs, and service registry.

🎯 Design Goals of the Structure¶

✅ Isolate edition logic without duplication
🔁 Regenerable by any agent using blueprint traceId
🧩 Composable with external services via images or packages
📚 Documented and introspectable via generated Markdown
🎯 Edition-aware tests and overrides scoped correctly
🤖 AI-agent friendly for future evolution, delta application, and memory linkage

🧠 Memory + Traceability Integration¶

traceId is stored in blueprint metadata and embedded in generated files
Studio UI can reconstruct graph views, trigger regeneration, or suggest edition changes
Blueprint files in blueprints/ folder include embedded lineage for audit

✅ Summary¶

The platform structure is generated to support scale, traceability, and cross-edition control
Each folder, file, and project corresponds to blueprint semantics
Designed to support a distributed, agentic generation workflow with clean separation of concerns

🧭 Platform Services and Shared Libraries¶

The Platform Template produced from the Agentic SaaS Product Blueprint includes core shared libraries and platform services — reusable components required across all editions and tenants.

Rather than duplicating logic in each microservice, these platform-level modules provide cross-cutting functionality, tenant-awareness, and edition orchestration.

These shared components are generated from DSL references in the blueprint and are intended to be reused across the entire SaaS product family.

📦 Shared Libraries in `Platform.Shared/`¶

Library/Module	Purpose
`Edition.cs`	Generated from `EditionDSL`; includes ID, name, features, inheritance
`Tenant.cs`	Generated from `TenantDSL`; includes branding, limits, edition link
`FeatureFlags.cs`	Loads runtime feature flags and toggle maps per edition or tenant
`EditionSelector.cs`	Resolves edition assignment during runtime, optionally from token/context
`ITenantContext`	Injected into services for accessing tenant metadata
`IDistributedEditionProvider`	Allows runtime fetch of edition configs from remote sources (e.g. blob/db)

🧠 Blueprint DSL → Code Generation Mapping¶

# blueprint.yaml
dslRefs:
  - dsls/entities/Edition.yaml
  - dsls/entities/Tenant.yaml
  - dsls/features/FeatureMatrix.yaml

// Edition.cs
public class Edition
{
    public string Id { get; set; }
    public string Name { get; set; }
    public List<string> Features { get; set; }
    public string? Extends { get; set; }
}

This enables regeneration of code from DSL updates, and traceable lineage from service back to source blueprint.

⚙️ Example Usage of Shared Services¶

var tenant = await tenantContext.GetTenantAsync();
var edition = editionSelector.GetEditionForTenant(tenant.Id);

if (edition.Features.Contains("Analytics"))
    services.AddScoped<IAnalyticsModule, EditionAnalytics>();

This logic is automatically scaffolded from feature matrix and injected into platform composition.

📎 Additional Shared Utilities¶

Utility	Description
`EditionRuntimePolicy.cs`	Defines behavior overrides (e.g. throttling, SLA) per edition
`TenantBranding.cs`	Loads visual or naming overrides per tenant
`DiagnosticsEmitter.cs`	Emits observability spans for edition switches or overrides
`EditionTestHelper.cs`	Provides BDD setup for SpecFlow-style tests per edition

🔌 Cross-Service Integration¶

Shared libraries also expose interfaces for cross-cutting services like:

Authentication / Authorization
Notification routing
Retry/timeout/resiliency config
Storage abstractions
Logging standards

These are included in the platform solution but are designed for injection into independently built microservices.

🧠 Agent Contributions to Shared Code¶

Agent	Contribution
Feature Matrix Agent	Populates `FeatureFlags.cs` from matrix definitions
Product Manager Agent	Defines shared modules in blueprint metadata
Edition Blueprint Agent	Extends feature model with inheritance references
Observability Agent	Injects tracing hooks in cross-cutting services
QA Agent	Uses shared test helpers for edition-specific flows

✅ Summary¶

Shared libraries generated from the blueprint provide a strong runtime foundation for edition-aware behavior
Agents generate, extend, and consume these libraries to maintain semantic consistency
By housing these cross-cutting concerns in the platform, the system ensures:
🧩 Modularity
🎯 Traceability
🔁 Regenerability
👥 Agent collaboration across editions and tenants

🧭 Feature Matrix Blueprinting¶

At the heart of any edition-aware SaaS platform lies the Feature Matrix — a declarative mapping that defines:

Which features exist in the product
Which editions expose them
Which services implement them
And optionally, how each feature is tested, monitored, or flagged

The Agentic SaaS Product Blueprint includes or references this matrix to drive agent decisions in code generation, testing, observability, and documentation.

🗂️ What Is the Feature Matrix?¶

A structured table or YAML definition that maps:

Feature ID	Description	Editions	Implemented In
`BookAppointment`	Schedule new appointments	basic, pro, ent	BookingService
`CancelAppointment`	Cancel scheduled visit	pro, ent	BookingService
`Analytics`	View usage data	pro, ent	AnalyticsService
`CustomBranding`	Theme, logo overrides	ent	TenantBrandingService

📘 YAML Feature Matrix Fragment (from DSL or inline)¶

features:
  - id: BookAppointment
    description: Allow users to schedule a visit
    editions: [basic, pro, enterprise]
    implementedBy: booking-service
    testBlueprint: tests/features/book_appointment.feature

  - id: Analytics
    editions: [pro, enterprise]
    implementedBy: analytics-service
    observability:
      spanName: appointment.analytics.viewed

💡 The matrix can be authored by Product Manager or Feature Matrix Agent, or derived from DSLs.

🔁 How It's Used by the AI Factory¶

Usage Area	Agent / Output
Service Blueprinting	Code generation for exposed endpoints only if feature is enabled in edition
Test Generation	Generate `*.feature` files and test cases per edition
Observability	Inject spans and metrics per feature based on edition usage
Documentation	Auto-generate edition comparison tables
Pipeline Scoping	Edition-aware build/deploy/test toggles

🔄 Traceable Matrix Storage¶

Stored as feature-matrix.yaml in the platform template
Indexed in long-term memory for agentic retrieval and diffing
Includes traceable links to:
Feature DSLs
Service responsibilities
Changelog entries
Blueprint traceId

📎 Feature Matrix Evolution Events¶

Event	Trigger
`FeatureMatrixCreated`	First time blueprint emits full edition-feature mapping
`FeatureAddedToEdition`	Edition scope expanded
`FeatureDeprecated`	Agent emits notice + triggers test review
`FeatureImplementationChanged`	Update cross-service reference

Each is observable, traceable, and optionally recorded in changelog DSLs.

🧠 Agent Collaboration with Feature Matrix¶

Agent	Role
Product Manager Agent	Seeds matrix based on product vision
Feature Matrix Agent	Normalizes, validates, and links to DSLs
QA Agent	Reads matrix to determine which features to test per edition
Observability Agent	Adds span definitions per feature
Service Blueprint Agent	Restricts generation based on edition support
Documentation Agent	Publishes edition vs. feature tables

✅ Summary¶

The Feature Matrix is the semantic contract for edition behavior
It is essential for:
Controlling generation scope
Tracing feature coverage
Validating consistency across code, tests, docs
It transforms the blueprint from static metadata into a living capability model for the SaaS product

🧭 Edition Blueprint Generation¶

Each Edition defined in the SaaS Product Blueprint can be promoted into a dedicated Edition Blueprint, allowing downstream agents to:

Scope service generation
Override platform behavior
Generate targeted tests, observability, and pipelines
Evolve editions independently (via inheritance or customization)

This cycle covers how Edition Blueprints are derived, structured, and consumed across the AI Software Factory.

🏷️ What Is an Edition Blueprint?¶

An Edition Blueprint is a focused sub-blueprint derived from the parent product. It includes:

Selected features (inherited or overridden)
Linked services (optional overrides or replacements)
Feature flags and runtime config policies
Test and pipeline scope limitations
Traceability links to product and tenant artifacts

🧠 Think of it as the “activation context” of a product for a particular edition.

🔄 Generation Flow¶

flowchart TD
    SaaSProductBlueprint --> EditionBlueprintAgent
    EditionBlueprintAgent --> EditionBlueprints["🧾 vet-basic.yaml, vet-pro.yaml, ent.yaml"]
    EditionBlueprints --> ServiceBlueprints
    EditionBlueprints --> TestBlueprints
    EditionBlueprints --> Docs
    EditionBlueprints --> CI/CD Pipelines

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🧬 Example YAML: `vet-pro-edition.yaml`¶

editionId: vet-pro
extends: vet-basic
features:
  - BookAppointment
  - CancelAppointment
  - Analytics
observability:
  spanPrefix: pro/
  flags:
    - emitFeatureUsage: true
ciTags:
  - deploy-vet-pro
  - test-pro-only

📁 Where It Lives¶

By convention, generated edition blueprints are placed under:

/ConnectSoft.Platform/blueprints/editions/
├── vet-basic-edition.yaml
├── vet-pro-edition.yaml
└── enterprise-edition.yaml

These are linked from:

feature-matrix.yaml
docker-compose.override.yml
tests/EditionScenarios/
Platform.Orchestration/Startup.cs

🧠 Purpose in Factory Flow¶

Purpose	How Edition Blueprint Enables It
🔧 Service Scope	Only generate endpoints/features for edition
🧪 Test Coverage	Generate scenarios and validations per edition
📈 Observability Injection	Activate edition-specific spans/logs
🔁 Pipeline Routing	Only run CI/CD for relevant editions
📚 Documentation	Auto-create comparison tables, edition changelogs
🧠 Memory Trace	Trace decisions, agent actions back to edition
🧩 Regeneration	Blueprint can regenerate edition scope on change

🧠 Agents Involved in Edition Blueprint Creation¶

Agent	Contribution
Edition Blueprint Agent	Promotes edition metadata to standalone blueprint
Product Manager Agent	Defines edition inheritance and overrides
QA Agent	Links test plans to edition
DevOps Agent	Tags and scopes pipelines
Feedback Agent	Applies user/tenant feedback to edition blueprint

✅ Summary¶

An Edition Blueprint is the executable spec for an edition
It scopes what gets built, tested, deployed, and monitored
Enables isolated agent activity, traceability, and regeneration
Supports advanced features like:
Inheritance (extends)
Dynamic config injection
Edition-aware observability

🧭 Tenant Context and Overrides¶

Tenants are the runtime units of consumption for a SaaS product. In ConnectSoft’s AI Software Factory, each tenant:

Is bound to an edition
May override specific configuration, branding, or limits
Exists as a dynamic runtime entity, enriched by the SaaS Product Blueprint

This cycle details how tenant context is modeled, how it interacts with editions, and how it supports multi-tenant orchestration in agentic SaaS generation.

🟨 What Is a Tenant in This Context?¶

A Tenant represents a customer (company, partner, organization) that consumes the product under a specific edition. It is a runtime entity enriched by both:

The Tenant Profile DSL
The SaaS Product Blueprint’s edition/feature definitions

🧬 Sample Tenant Profile YAML¶

tenantId: vetclinic-chicago
name: VetClinic Chicago
edition: vet-pro
overrides:
  maxAppointmentsPerDay: 50
  showBranding: true
  theme: green
  flags:
    enableNewCalendarUI: true
    useLegacyEmailFlow: false

🔄 Tenant-Edition Linkage¶

graph TD
    SaaSProductBlueprint --> Editions
    Editions --> Tenants
    Tenants --> RuntimeBehavior
    Tenants --> PipelineOverrides

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At runtime, the platform resolves:

Edition-level feature access
Tenant-level config and policies
Scoped observability and metrics tagging

🧠 Tenant-Aware Behaviors Enabled¶

Behavior	How Tenant Context Enables It
🧩 Feature Flags	Load tenant+edition flags to control code paths
🎨 Branding	Inject theme, logo, and names dynamically
💡 AI Prompts	Generate different LLM prompts per tenant (if enabled)
⚙️ Config Overrides	Adjust limits, policies, and service rules by tenant
📈 Metrics & Logging	Emit tenant-scoped metrics, logs, and spans
🧪 Test Parameterization	Run test flows with tenant-specific variables

🔧 Platform-Level Code Usage¶

var tenant = await _tenantContext.GetTenantAsync();
if (tenant.Overrides.Theme == "green")
    ApplyGreenTheme();

All tenant profiles are injected as part of the platform’s runtime DI graph and can be hydrated via:

Blob config stores
API injection
Static YAML loaders
AI-generated test harnesses

📎 Traceability in Blueprint¶

Each tenant is linked to its edition
Each override is mapped to a specific DSL reference
All runtime decisions emit traceIds for observability and backtracking

traceId: tenant::vetclinic-chicago
origin: product::connectsoft-appointments
edition: vet-pro
effectiveFeatures: [BookAppointment, CancelAppointment, Analytics]

🤖 Agents Affected by Tenant Profiles¶

Agent	Behavior
Platform Engineer Agent	Injects tenant loader into DI and config
QA Agent	Generates tenant-specific test flows
Observability Agent	Emits tenant-scoped span metadata
Feedback Agent	Accepts user feedback to refine tenant blueprint
Edition Blueprint Agent	Reads tenant overrides for inheritance or conflict detection

✅ Summary¶

Tenants personalize and contextualize edition behavior
Overrides include config, branding, observability, and feature toggles
Tenant profiles are used during:
🏁 runtime execution
🧪 test generation
🧠 AI planning and prompt scoping
Their structure and linkage are traceable, regenerable, and observable

🧭 Service Blueprint Coordination¶

The Agentic SaaS Product Blueprint does not define microservices directly — instead, it coordinates their generation by delegating to Service Blueprints, based on:

Features assigned to editions
Shared modules declared in the product
External service references
DSL-defined entities, commands, and events

This cycle explains how services are orchestrated from the product blueprint and how service blueprints inherit semantic context.

🧩 Service Blueprint Trigger Points¶

Each feature in the product blueprint may declare:

Its implementing service
Its bound events, commands, queries
DSLs that describe domain behavior

This enables the factory to instantiate a microservice blueprint, scoped precisely to the feature’s role in the product.

📘 Example Product Feature → Service Mapping¶

features:
  - id: BookAppointment
    editions: [basic, pro, enterprise]
    implementedBy: booking-service
    dslRefs:
      - dsls/features/book_appointment.feature.yaml
      - dsls/entities/appointment.yaml

This triggers the Service Blueprint Agent to generate:

A blueprint for booking-service
API surface, entities, event contracts
Integration points with platform (edition hooks, observability, etc.)

🧬 Relationship Flow¶

graph TD
    SaaSProductBlueprint --> FeatureMatrix
    FeatureMatrix --> ServiceBlueprintAgent
    ServiceBlueprintAgent --> ServiceBlueprint["booking-service-blueprint.yaml"]
    ServiceBlueprint --> MicroserviceTemplate

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🛠️ Service Blueprint Injection from Product¶

The generated service blueprint includes:

Field	Injected From
`productId`	From top-level SaaS Product Blueprint
`editionScope`	From Feature Matrix edition assignments
`traceId`	Blueprint lineage reference
`dslRefs[]`	From features[].dslRefs
`tenantContext`	From shared platform services
`observabilityPolicy`	From product-level spans and overrides

📂 Blueprint Structure¶

serviceId: booking-service
productId: connectsoft-appointments
editions: [basic, pro, enterprise]
dslRefs:
  - dsls/entities/appointment.yaml
  - dsls/features/book_appointment.feature.yaml
features:
  - BookAppointment
  - CancelAppointment

🧠 The product-to-service binding ensures clean separation yet full traceability.

🤖 Multi-Agent Coordination Per Service¶

Agent	Role
Service Blueprint Agent	Generates `*.service-blueprint.yaml` files
API Surface Agent	Builds OpenAPI spec and endpoint handlers
Entity Model Agent	Translates DSL entities into domain models
Observability Agent	Wires spans based on feature matrix injection
Test Agent	Generates edition-aware tests per feature-service binding
Documentation Agent	Cross-links service docs with product docs and editions

🔁 Benefits of Coordinated Generation¶

Enables polyrepo microservice management (only blueprint is referenced in platform template)
Allows independent teams to consume blueprints in isolated pipelines
Service teams can be aligned with edition scope and lifecycle via blueprint triggers
Factory ensures semantic compatibility between product, edition, and service outputs

✅ Summary¶

The SaaS Product Blueprint doesn’t contain service logic — it delegates it to service blueprints
This creates a loosely coupled, semantically linked service architecture
Product→Edition→Feature→Service mappings allow:
🔁 traceable regeneration
🧪 test alignment
🧩 consistent observability
🤖 full agentic orchestration

🧭 Pipeline and Deployment Blueprinting¶

The Agentic SaaS Product Blueprint enables automated generation of CI/CD pipelines and edition-aware deployment blueprints. These are critical for managing:

Edition-specific test/build flows
Tenant-scoped deployment overrides
Microservice orchestration across environments
Release governance based on product semantics

This cycle describes how agents derive pipeline structures and deployment targets from the product blueprint.

🚀 What Gets Generated¶

Artifact	Purpose
`ci-connectsoft-platform.yml`	Master CI pipeline for the platform template
`deploy-vet-pro.yml`	Edition-scoped deployment flow
`tests-edition-enterprise.yml`	Conditional test suite for enterprise edition
`service-build-booking.yml`	Scoped CI for service bound to features
`tenant-deploy-vetclinic.yml`	Optional overrides for specific tenants

These pipelines are edition-aware, regenerable, and trace-linked to the product blueprint and matrix.

🧬 Generation Flow (Simplified)¶

graph TD
    SaaSProductBlueprint --> EditionBlueprints
    EditionBlueprints --> DevOpsAgent
    DevOpsAgent --> CIPipelines
    DevOpsAgent --> DeploymentScripts
    DevOpsAgent --> MonitoringHooks

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📘 Pipeline YAML Snippet (CI Scope)¶

trigger:
  branches:
    include:
      - main
stages:
  - stage: Build
    jobs:
      - job: BuildPlatform
        steps:
          - script: dotnet build ConnectSoft.Platform.sln

  - stage: Test
    condition: eq(variables['Edition'], 'pro')
    jobs:
      - job: RunTests
        steps:
          - script: dotnet test tests/EditionScenarios

📦 Edition-Aware Deployment Targets¶

editions:
  - id: enterprise
    pipeline: deploy-enterprise.yml
    targets:
      - cluster: aks-enterprise
        namespace: connectsoft-ent
        observabilityEnabled: true

Each edition defines its own deployment characteristics, which the DevOps Agent turns into:

Helm charts or Bicep/IaC scripts
Kubernetes manifests
Tenant overlays (e.g., per-tenant values.yaml)
Environment variables, secrets, storage configs

🧠 Agent Contributions to Pipeline Generation¶

Agent	Role
DevOps Agent	Generates core CI/CD pipelines and deploy YAMLs
Edition Blueprint Agent	Supplies edition-target mappings
QA Agent	Tags test plans and groups by edition/service
Platform Engineer Agent	Injects DI and runtime config links into deploy artifacts
Observability Agent	Hooks in telemetry/log forwarding to edition clusters

🔁 Feedback + Regeneration¶

Pipelines are not hardcoded — they are regenerated if:
Editions change
Services are reassigned
Feature scopes are updated
Tenant overrides change deployment profile
TraceIds are included in the YAML to support:
📜 Auditable change logs
🔁 Idempotent blueprint re-application
📈 Edition-based success metrics

✅ Summary¶

Pipelines and deployments are semantically bound to the product blueprint
Agents generate them automatically from:
Feature assignments
Edition scopes
Tenant profiles
They enable automated testing, edition-specific rollout, and traceable observability

🧭 Observability and Tracing Hooks¶

Every SaaS platform generated by the AI Software Factory must be observability-first. The Agentic SaaS Product Blueprint includes embedded observability directives that allow agents to inject:

📈 Spans and traces scoped by edition, tenant, and feature
📊 Custom metrics tied to usage, errors, or SLA breaches
🧩 Edition-level diagnostic overlays
🔁 Traceable event hooks across services and pipelines

This cycle details how tracing, logging, and telemetry are structured in the blueprint and implemented in the platform.

🔍 Observability DSL in Product Blueprint¶

observability:
  defaultSpanPrefix: "connectsoft.platform"
  enableFeatureSpans: true
  metrics:
    - name: edition_activated
      type: counter
      labels: [edition, tenantId]
    - name: feature_used
      type: event
      labels: [feature, edition]
  tracing:
    includeEditionInTraceId: true
    spanTags:
      tenantId: context.tenant.id
      edition: context.edition.id

These are transformed into concrete observability code and infrastructure by agents.

🧠 Observability Agent Responsibilities¶

Task	Example
Generate `ActivitySource` spans per service	`ActivitySource("connectsoft.booking-service")`
Add middleware for trace context injection	`AddTraceContextMiddleware()`
Emit metrics for feature usage	`feature_used{feature="BookAppointment", edition="pro"}`
Configure distributed tracing per tenant	via Azure Monitor / OpenTelemetry / Grafana Agent
Inject edition into traceId and logs	`traceId: ed-pro.abc123.tenantX`

📦 Generated Artifacts¶

File	Description
`ObservabilityConfig.cs`	.NET class that loads settings from blueprint
`otel-config.yaml`	OpenTelemetry collector pipeline
`infra/monitoring/metrics.rules`	Grafana or Prometheus alert rules per edition
`logs/enrichment.cs`	Logs enriched with tenant + edition context
`tests/EditionObservabilityTests.cs`	BDD tests for span emission validation

🔁 Trace Scope by Product Hierarchy¶

flowchart LR
    Product --> Edition --> Tenant
    Edition --> Feature
    Feature --> Span
    Span --> Logs
    Span --> Metrics

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Each layer adds semantic metadata to the observability layer:

🧩 Edition tags: edition=pro
🏷️ Feature context: feature=BookAppointment
🟨 Tenant scope: tenantId=vetclinic-chicago
🔁 Span linkage: parent-child relationships by event

📊 Example Span Emitted¶

{
  "spanName": "appointment.booked",
  "traceId": "ed-pro.abc123.vetclinic-chicago",
  "tags": {
    "feature": "BookAppointment",
    "edition": "pro",
    "tenantId": "vetclinic-chicago"
  },
  "duration_ms": 142
}

This format is consistent across services and testable via synthetic trace scenarios.

🧪 Observability Test Generation¶

Generated SpecFlow-style tests ensure:

Spans are emitted with correct tags
Errors are traced with root cause lineage
Metrics are updated on feature usage
Logs are scoped to tenant and edition

✅ Summary¶

Observability is embedded in the product blueprint and transformed by agents
Supports:
🔁 Feature-to-span traceability
📊 Metrics for tenant usage, SLA breaches, and adoption
🧠 Semantic trace IDs for platform-wide correlation
Enables platform-level debugging, AI insights, and feedback loops

🧭 Documentation and Diagram Generation¶

The Agentic SaaS Product Blueprint is not only consumed by agents — it also serves as the foundation for user-facing and developer-facing documentation, as well as autogenerated architecture diagrams.

This cycle explores how agents generate blueprint-aware documentation, edition comparison charts, and system diagrams from the blueprint metadata.

📚 What Documentation Is Generated?¶

Artifact	Purpose
`docs/product-overview.md`	Explains the SaaS product, its editions, and features
`docs/editions.md`	Side-by-side feature matrix table per edition
`docs/tenants.md`	Lists tenants, their edition, and overrides
`docs/architecture.md`	High-level system architecture with diagrams
`docs/observability.md`	Tracing and metrics overview by edition/feature
`docs/blueprint-lineage.md`	History of blueprint changes and regeneration events

🧩 Edition Comparison Table (Markdown Example)¶

| Feature             | Basic | Pro | Enterprise |
|---------------------|:-----:|:---:|:----------:|
| Book Appointment     | ✅    | ✅  | ✅         |
| Cancel Appointment   | ❌    | ✅  | ✅         |
| Analytics Dashboard  | ❌    | ✅  | ✅         |
| Custom Branding      | ❌    | ❌  | ✅         |

Automatically derived from feature-matrix.yaml

📊 Auto-Generated Diagrams¶

🧬 System Architecture (Mermaid)¶

graph TD
    Platform --> BookingService
    Platform --> AnalyticsService
    BookingService --> DB1[(Appointments DB)]
    AnalyticsService --> DB2[(Metrics DB)]
    Platform --> TenantContext

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🧭 Agent Collaboration Flow¶

flowchart LR
    ProductManager --> SaaSProductBlueprint
    SaaSProductBlueprint --> EditionBlueprintAgent
    EditionBlueprintAgent --> QAAgent
    QAAgent --> TestBlueprints
    DevOpsAgent --> DeploymentYAMLs
    ObservabilityAgent --> Metrics

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🧠 Documentation Agent Responsibilities¶

Role	Output
Renders product & edition docs	`docs/editions.md`
Builds mermaid diagrams from blueprint lineage	`docs/architecture.md`
Generates markdown tables for features and tenants	`docs/tenants.md`
Adds change history and trace info to `blueprint-lineage.md`	Shows when/why/which agents modified the blueprint

📄 Traceable Headers in Docs¶

Each generated doc includes metadata:

<!-- generated-by: documentation-agent -->
<!-- blueprint-trace-id: connectsoft-appointments -->
<!-- version: 1.4.2 -->
<!-- date: 2025-06-07 -->

🔁 Documentation Updates Triggered On¶

✏️ Feature matrix change
📦 Edition added or modified
🎨 Tenant profile overrides
🛠️ Regeneration events (e.g., after feedback)
🧠 New DSL linked or removed

Agents auto-update docs as part of each generation cycle.

✅ Summary¶

The blueprint feeds automated documentation — always accurate, always updated
Includes:
Edition comparison tables
Tenant lists
Architecture diagrams
Change trace logs
Agents handle this generation, requiring no manual doc maintenance

🧭 SaaS Product Blueprint (Consolidated Description)¶

The SaaS Product Blueprint is the semantic root artifact that drives the entire AI Software Factory pipeline for building multi-tenant, edition-aware SaaS platforms.

This cycle brings together all prior concepts into a single consolidated blueprint structure — combining metadata, editions, features, observability, agents, and downstream traceability.

📘 Full Blueprint Structure (Simplified YAML Form)¶

productId: connectsoft-appointments
name: ConnectSoft Appointments Platform
description: Scalable SaaS for booking and schedule optimization

dslRefs:
  - dsls/features/appointments.yaml
  - dsls/entities/tenant.yaml

editions:
  - id: basic
    name: Basic
    features: [BookAppointment]

  - id: pro
    name: Professional
    features: [BookAppointment, CancelAppointment, Analytics]
    extends: basic

  - id: enterprise
    name: Enterprise
    features: [BookAppointment, CancelAppointment, Analytics, CustomBranding]
    extends: pro

observability:
  defaultSpanPrefix: "connectsoft"
  emitFeatureSpans: true
  metrics:
    - name: feature_used
      type: event
      labels: [feature, tenantId]

tenants:
  - tenantId: vetclinic-chicago
    edition: pro
    overrides:
      theme: green
      maxAppointmentsPerDay: 50

featureMatrix:
  - id: BookAppointment
    editions: [basic, pro, enterprise]
    implementedBy: booking-service
  - id: Analytics
    editions: [pro, enterprise]
    implementedBy: analytics-service

generatedArtifacts:
  - platformTemplate: /ConnectSoft.Platform/
  - featureMatrixFile: feature-matrix.yaml
  - editionBlueprints:
      - vet-pro-edition.yaml
      - enterprise-edition.yaml
  - serviceBlueprints:
      - booking-service-blueprint.yaml
      - analytics-service-blueprint.yaml
  - pipelineFiles:
      - deploy-vet-pro.yml
      - ci-connectsoft-platform.yml
  - testFiles:
      - BookAppointment.feature
      - Analytics.feature

🔁 Core Properties and What They Control¶

Property	Drives
`productId`	Traceability across services, pipelines, tests
`dslRefs[]`	DSL loading and regeneration of models
`editions[]`	Edition feature scoping, inheritance, testing
`tenants[]`	Runtime overrides, observability tagging
`featureMatrix`	Which services are built and tested
`observability`	Span injection, metrics wiring
`generatedArtifacts`	CI/CD, platform scaffolds, blueprints

🧠 Blueprint as a Graph Root¶

graph LR
    SaaSProductBlueprint --> EditionBlueprints
    SaaSProductBlueprint --> ServiceBlueprints
    SaaSProductBlueprint --> PlatformTemplate
    SaaSProductBlueprint --> Documentation
    SaaSProductBlueprint --> ObservabilitySpanMap
    SaaSProductBlueprint --> QAPlan

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All other artifacts in the system reference or derive from this root blueprint, and agents operate with a consistent traceId.

🧩 Blueprint Roles in the Factory¶

Role	Description
🧠 Knowledge Seed	Feeds vector DB and DSL interpreters for agents
🧱 Generation Source	Triggers service, edition, and test blueprints
🔍 Observability Anchor	Ensures traceability of events, spans, features
🤖 Agent Contract	Defines scope, expectations, and outputs for agents
🧬 Regeneration Handle	Serves as source of truth for product lifecycle

✅ Summary¶

The SaaS Product Blueprint encapsulates:
Product identity
Feature-to-edition mappings
Tenant overrides
DSL references
Service bindings
Observability hooks
All downstream artifact traceability
It is modular, extensible, regenerable, and AI-agent optimized

🧭 Blueprint Regeneration and Drift Detection¶

The Agentic SaaS Product Blueprint is not static — it evolves over time as:

Features change
Editions are redefined
Services are refactored
Tenants require customization
Feedback is received from usage or telemetry

This cycle outlines how the AI Software Factory handles blueprint regeneration, tracks drift, and maintains consistency between the source blueprint and all downstream artifacts.

🔁 What Is Blueprint Regeneration?¶

It’s the re-execution of the blueprint to:

Re-generate platform templates
Re-build service blueprints
Re-sync observability, CI, docs, tests
Align current state with declared semantic contract

Regeneration ensures that artifacts reflect the latest feature matrix, edition scopes, tenant overrides, and DSLs.

📦 What Triggers Regeneration?¶

Trigger Type	Example
🧠 DSL Update	Entity or feature definition changed
🏷️ Edition Feature Change	“Analytics” added to `pro` edition
🧩 Feature-Service Binding	Feature moved to a new service
🔁 Tenant Override Updated	Max appointment count raised
✏️ TraceId Drift Detected	Agent sees out-of-sync runtime artifact
🧪 Feedback from QA Agent	Tests show feature mismatch

🧠 Blueprint Drift Detection Mechanism¶

Each generated artifact (test, service, doc, pipeline) includes a traceId, e.g.:

# vet-pro-edition.yaml
traceId: product::connectsoft-appointments::edition::vet-pro::v3.1.2

Agents verify trace alignment. If they detect:

⚠️ Missing or outdated features
⚠️ Incompatible test plans
⚠️ Broken edition → service → feature mappings

They emit a BlueprintDriftDetected event, prompting regeneration.

🔍 Regeneration Flow (Mermaid)¶

sequenceDiagram
    actor Agent
    Agent->>BlueprintRegistry: Fetch SaaS Product Blueprint
    Agent->>DriftDetector: Compare traceIds
    DriftDetector-->>Agent: DriftDetected
    Agent->>RegenerationEngine: Trigger blueprint regeneration
    RegenerationEngine->>AllAgents: Broadcast updated artifacts

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📁 Drift-Proof Artifact Inclusions¶

Each artifact includes:

✅ traceId
✅ blueprintVersion
✅ blueprintSourceFile (path or URL)
✅ generatedByAgent

Example:

// EditionContext.cs
// traceId: connectsoft.appointments::edition::ent::v2.1
// generatedBy: edition-blueprint-agent

🔁 Partial vs. Full Regeneration¶

Scope	Use Case
🔁 Partial Regeneration	Only `Analytics.feature` test updated
🔁 Edition-Scoped Regeneration	Regenerate `enterprise-edition.yaml` and its tests
🔁 Full Product Regeneration	New DSLs + matrix → all editions, services, docs updated

The regeneration engine uses impact graphs to determine the minimal required changes.

🤖 Agents Participating in Drift Detection¶

Agent	Role
DriftDetector Agent	Monitors artifacts for trace/version mismatch
Regeneration Orchestrator Agent	Reinvokes blueprint generation with deltas
Documentation Agent	Updates diagrams and changelogs
QA Agent	Validates post-regeneration test coverage
DevOps Agent	Refreshes pipeline configs and edition deploys

✅ Summary¶

Blueprint regeneration ensures:
🔁 Alignment between declarations and outputs
🧠 Agent-aware consistency
📜 Traceable artifact evolution
Drift detection is proactive and fully automated
TraceIds, semantic diffs, and blueprint history enable safe, scalable updates

🧭 Feedback-Driven Blueprint Evolution¶

To maintain a continuously improving platform, the Agentic SaaS Product Blueprint is designed to evolve based on runtime signals, user feedback, and agent observations.

This cycle explains how the blueprint incorporates insights from execution, monitoring, QA, and end users to refine editions, features, service bindings, and tenant-specific behavior — making the system self-improving and context-aware.

🔁 What Constitutes Feedback?¶

Source	Type of Feedback Example
🎯 User Input	“CancelAppointment not available in Pro Edition”
🧪 QA Tests	Repeated failure in feature scenario in specific edition
📈 Observability	Span latency spikes tied to specific feature
🤖 Agent Logs	Regeneration triggered due to ambiguous DSL definition
📝 Documentation	Out-of-sync feature matrix vs. service implementation
📬 Support Cases	Requests for new edition configuration or overrides

📡 Feedback Ingestion Flow¶

flowchart TD
    FeedbackCollector --> FeedbackAgent
    FeedbackAgent --> TraceAnalyzer
    FeedbackAgent --> RegenerationOrchestrator
    TraceAnalyzer --> SuggestionDSL
    RegenerationOrchestrator --> BlueprintDelta
    BlueprintDelta --> BlueprintUpdate

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The Feedback Agent orchestrates how signals from runtime, agents, and external sources are synthesized into blueprint updates.

📬 Feedback Types and Target Updates¶

Feedback Type	Target Blueprint Update
“Feature not in edition”	Add to `featureMatrix.yaml` and edition entry
“Test failing”	Validate `dslRefs` + extend coverage definition
“Analytics slow”	Add observability trace rule to feature span
“Need tenant override”	Inject override in `tenant.yaml`
“Confusing naming”	Update description in product metadata

✏️ Example Feedback Update¶

Input:

{
  "source": "QA Agent",
  "message": "Feature CancelAppointment fails in edition=basic",
  "traceId": "product::connectsoft-appointments::feature::cancel"
}

Agent Suggestion:

- action: remove-feature-from-edition
  edition: basic
  feature: CancelAppointment
  reason: feature not supported in service implementation

🧠 Feedback Memory and Lineage¶

Every feedback event is:

🧾 Stored in a feedback registry
🧠 Embedded in memory for future agent inference
🔁 Versioned against the product blueprint
🔍 Used in downstream prompt refinements (e.g. regenerate docs/tests only for affected areas)

🧠 Feedback Agent Responsibilities¶

Role	Output
Collate feedback from multiple sources	Unified feedback report
Map issues to traceable blueprint artifacts	Edition, feature, tenant
Generate blueprint deltas	Minimal YAML patches
Trigger regeneration events	Re-execute edition/service/test pipelines
Collaborate with Drift Detector	Identify overlapping concerns

🧬 Semantic Feedback Integration¶

Each feedback-generated update is treated as a semantic diff, not a raw file change. This allows agents to:

Explain why the change occurred
Replay deltas in test environments
Tag outputs with feedbackId
Reverse if new feedback contradicts it

✅ Summary¶

Feedback is a first-class signal in the blueprint lifecycle
It is processed by the Feedback Agent and used to:
Refine editions
Improve test coverage
Tune observability
Reassign features
The blueprint becomes a living artifact: always adapting to runtime reality

🧭 AI Prompt Enrichment via Blueprint Context¶

The SaaS Product Blueprint is not only a static spec — it becomes a semantic context source for prompt engineering and agentic communication across the factory. Agents use it to:

Enrich their prompt templates
Control generation scope
Align responses with product constraints
Route logic based on edition, tenant, or feature

This cycle explores how blueprint metadata is used to enrich LLM prompts, enabling more accurate, scoped, and semantically aligned outputs.

🧠 Prompt Injection from Blueprint¶

Each agent uses the blueprint context to inject precise metadata into prompts, such as:

Prompt Field	Injected From Blueprint
`productName`	`name`
`editionScope[]`	`editions[].features`
`serviceContext`	`featureMatrix[].implementedBy`
`tenantOverrides`	`tenants[].overrides`
`observabilityPolicy`	`observability` section
`linkedDSLs`	`dslRefs[]`

This allows prompts to be grounded, edition-aware, and regenerable.

💡 Example: Test Agent Prompt Enrichment¶

Before:

“Write a SpecFlow test for the CancelAppointment feature.”

After Blueprint Enrichment:

“Generate a SpecFlow test for the feature CancelAppointment, implemented in the booking-service, available only in editions pro and enterprise. Use tenantId=vetclinic-chicago context and verify observability span appointment.canceled is emitted.”

📘 Enrichment Snippet (Agent Template)¶

{
  "promptContext": {
    "productId": "connectsoft-appointments",
    "edition": "pro",
    "features": ["BookAppointment", "CancelAppointment"],
    "tenant": "vetclinic-chicago",
    "spanPrefix": "appointment.",
    "dslRefs": [
      "dsls/entities/appointment.yaml",
      "dsls/features/cancel_appointment.feature.yaml"
    ]
  }
}

This is passed to the Semantic Kernel planner or agent kernel for templated prompt rendering.

🤖 Prompt-Aware Agents¶

Agent	Enrichment Use Case
QA Agent	Tests per edition/tenant context
Observability Agent	Generate span declarations and test verifications
DevOps Agent	Edition-aware CI/CD pipeline scaffolding
Service Blueprint Agent	Prompt-aware service responsibilities
Documentation Agent	Use product DSL and matrix to generate accurate copy
Feedback Agent	Explain why blueprint changes were applied

📎 Prompt Memory Lineage¶

Prompt templates and rendered outputs include blueprint references:

<!-- injected-by: blueprint-context -->
<!-- edition: pro -->
<!-- feature: CancelAppointment -->
<!-- blueprint-trace-id: connectsoft-appointments::edition::pro -->

This ensures observability, regeneration traceability, and reusability of prompt executions.

🔁 Dynamic Prompt Routing¶

The AI orchestrator may use blueprint conditions to route prompts to the correct agent, e.g.:

if feature in edition.pro and service == 'analytics-service':
  route to Observability Agent

This allows flexible, blueprint-driven orchestration.

✅ Summary¶

Blueprint context is injected into prompts to ground and scope LLM output
Enables agents to produce:
🧪 Edition-aware tests
📈 Scoped observability logic
🧱 Correct service boundaries
🧬 Consistent documentation
Prompts become semantically traceable, versioned, and regenerable

🧭 Cross-Edition Feature Consistency and Validation¶

As a SaaS product evolves across multiple editions (e.g., basic, pro, enterprise), maintaining feature consistency, service alignment, and behavioral correctness becomes critical. This cycle introduces how the blueprint enables cross-edition validation to ensure:

No broken inheritance chains
No undefined features in editions
No misaligned test coverage
No partial observability across tiers

🎯 What Gets Validated¶

Validation Area	Check Description
🧱 Edition Inheritance	Valid `extends` chains, no cycles or undefined base editions
📋 Feature Matrix	Feature must be defined globally if listed in any edition
🔁 Feature Consistency	Feature available in parent must be correctly inherited or overridden
🧪 Test Coverage	Each feature in an edition must have test scaffolding or reason to skip
📈 Observability Span Match	Feature with `observability.spanId` must emit in all editions that support it
📦 Service Assignment	Feature listed must resolve to a valid, generated service blueprint

📘 Example Validation YAML Output¶

crossEditionValidation:
  errors:
    - id: orphan-feature
      message: "Feature Analytics listed in 'pro' but not defined in feature matrix."
      path: editions[pro].features[Analytics]

    - id: test-missing
      message: "No test found for CancelAppointment in edition 'enterprise'."
      feature: CancelAppointment

    - id: span-incomplete
      message: "Span 'appointment.analytics.viewed' not declared in 'basic'."
      recommendation: "Remove feature or disable span logic."

  status: INVALID
  traceId: connectsoft.appointments::v2.5.3

This output is consumable by agents, DevOps pipelines, or regeneration workflows.

🧠 Edition Validation Agent Role¶

Responsibility	Description
Parse edition → feature assignments	Validate they align with matrix and DSLs
Traverse inheritance tree	Detect shadowed, undefined, or duplicate features
Verify linked artifacts	Check that each feature → test → service → span forms a complete graph
Emit `EditionValidationReport`	Traceable to product and edition
Trigger blueprint regeneration if invalid	Partial or full, based on scope

🔍 Sample Inheritance Tree Check¶

graph TD
    basic --> pro
    pro --> enterprise
    enterprise --> X["❌ unknown-edition"]

Hold "Alt" / "Option" to enable pan & zoom

❌ Blueprint validation will fail: enterprise cannot extend from undefined X.

🔁 Feature Overlap Table¶

Feature	basic	pro	enterprise	Tests Present	Service Linked	Span Valid
BookAppointment	✅	✅	✅	✅	✅	✅
CancelAppointment	❌	✅	✅	✅	✅	✅
Analytics	❌	✅	✅	❌	✅	❌
CustomBranding	❌	❌	✅	✅	✅	✅

This kind of table is generated automatically as part of edition-validation-report.md.

🧩 Outputs Produced¶

edition-validation-report.yaml
blueprint-validation-results.md
Optional: trigger regeneration:partial if inconsistencies found
Diagnostic mermaid diagrams for missing linkage

✅ Summary¶

Validates that editions are complete, consistent, and traceable
Detects:
Orphan features
Missing tests
Span mismatches
Inheritance errors
Enables agents to self-heal, reroute, or notify on blueprint issues

🧭 Multi-Tenant Configuration Blueprinting¶

The Agentic SaaS Product Blueprint is designed for multi-tenant SaaS platforms. This cycle focuses on how it supports tenant-specific customization, runtime isolation, and platform consistency — all while maintaining a unified product and edition structure.

🏢 Tenants as First-Class Entities¶

Each tenant:

Belongs to a single edition
Inherits that edition’s features and defaults
Can override UI, limits, flags, observability, or integration behaviors
Is represented in a tenant configuration blueprint

📘 Tenant Profile Example¶

tenantId: vetclinic-chicago
name: VetClinic Chicago
edition: pro
overrides:
  theme: green
  maxAppointmentsPerDay: 50
  enableLegacyUI: false
  observability:
    spanPrefix: vetchi
    logEnrichment: true
  integrations:
    calendar: outlook
    marketing: braze

Each override is traceable to edition defaults, and validated for schema and compatibility.

📦 Blueprint Structure: `/blueprints/tenants/`¶

├── vetclinic-chicago.yaml
├── petcare-nyc.yaml
├── happyvets-toronto.yaml

Each YAML file is self-contained and can be:

Injected into runtime config
Used in deployment overlays
Queried by the Observability Agent
Referenced in test generation

🧠 Agent Usage of Tenant Blueprints¶

Agent	Use
DevOps Agent	Builds tenant overlays and env files for deployments
Observability Agent	Customizes spans/logs per tenant
QA Agent	Generates tests under tenant constraints
Prompt Agents	Inject branding and behavior differences
Documentation Agent	Generates tenant tables and changelogs

🔄 Runtime Resolution Flow¶

flowchart LR
    TenantProfile --> Edition
    Edition --> FeatureMatrix
    TenantProfile --> Overrides
    TenantProfile --> RuntimeConfig
    RuntimeConfig --> DIContainer

Hold "Alt" / "Option" to enable pan & zoom

At runtime:

The platform loads tenantId from context/session/token
Resolves assigned edition
Loads merged config: edition.defaults + tenant.overrides
Applies to service layer, prompt layer, and observability

🧪 Test Scenario Generation Per Tenant¶

Scenario: VetClinic Chicago books appointment
  Given edition is "pro"
  And tenant is "vetclinic-chicago"
  And feature "BookAppointment" is enabled
  When user books an appointment
  Then a green-themed confirmation is shown
  And observability span "vetchi.appointment.booked" is emitted

This structure ensures tenant-specific validation without duplicating global logic.

🧩 Multi-Tenant Validation Checks¶

Validation Area	Rule
Override Type Match	`maxAppointmentsPerDay` must be integer
Feature Flag Consistency	Cannot enable undefined feature
Observability Hook Valid	Prefix must be valid in span registry
Edition Match	Tenant edition must exist in product blueprint

✅ Summary¶

Tenants are independently blueprintable
Their configs are consumable by all agent types
Overrides are validated, scoped, and regenerable
Multi-tenant support becomes first-class, not bolted on
Enables tenant-aware:
Testing
Deployment
Prompt generation
Observability
Feature control

🧭 Final Summary and Blueprint System Diagram¶

This final section includes a structured recap and a full Mermaid diagram showing how all blueprint parts interact across the platform.

🧩 What the Blueprint Enables¶

Capability	Enabled By
Multi-edition product management	`editions[]`, `featureMatrix`, inheritance
Tenant-level customization	`tenants[]`, `overrides`, config layering
Observability-first design	`observability` block, span + metric wiring
Feature-to-service orchestration	`featureMatrix[].implementedBy`
AI-enriched prompt grounding	`promptContext` from blueprint metadata
Regeneration and drift correction	`traceId`, `driftDetection`, regeneration
Documentation and test generation	YAML → markdown → SpecFlow/BDD artifacts
Modular CI/CD and deployment pipelines	Derived from blueprint scope and services

🧠 What the Blueprint Contains¶

✅ Product Identity
✅ DSL References (entities, features, commands, etc.)
✅ Editions (basic, pro, enterprise…)
✅ Feature Matrix
✅ Tenant Definitions and Overrides
✅ Observability Definitions (spans, metrics, logs)
✅ Feature-to-Service Bindings
✅ Traceable Outputs (pipelines, tests, configs, docs)
✅ Agent-aware Contracts

🧠 Platform-Wide Mermaid Diagram¶

graph TD
    SaaSProductBlueprint["📘 SaaS Product Blueprint"]

    SaaSProductBlueprint --> Editions["📚 Editions"]
    SaaSProductBlueprint --> Tenants["🏢 Tenants"]
    SaaSProductBlueprint --> DSLRefs["📖 DSL References"]
    SaaSProductBlueprint --> FeatureMatrix["🧩 Feature Matrix"]
    SaaSProductBlueprint --> Observability["📈 Observability"]
    SaaSProductBlueprint --> TraceId["🔁 Traceability + Drift"]

    Editions --> EditionBlueprints["📜 Edition Blueprints"]
    Tenants --> TenantConfigs["🗂️ Tenant Config Blueprints"]
    FeatureMatrix --> ServiceBlueprints["🛠️ Service Blueprints"]

    DSLRefs --> EntityModels["🏷️ Entity Models"]
    DSLRefs --> PromptContexts["💬 AI Prompt Enrichment"]

    EditionBlueprints --> CI_CD["🚀 CI/CD Pipelines"]
    ServiceBlueprints --> Microservices["🧱 Generated Microservices"]
    TenantConfigs --> DeploymentOverrides["⚙️ Tenant-Specific Deployments"]

    Observability --> SpansMetrics["🔍 Spans + Metrics"]
    TraceId --> DriftDetection["🚨 Drift Detection"]
    TraceId --> FeedbackLoop["📬 Feedback Integration"]

    Microservices --> Observability
    CI_CD --> DriftDetection
    FeedbackLoop --> Regeneration["♻️ Blueprint Regeneration"]

Hold "Alt" / "Option" to enable pan & zoom

✅ Final Summary¶

The Agentic SaaS Product Blueprint:

📘 Serves as the semantic contract between product, architecture, runtime, and AI agents
🔁 Powers regenerable, traceable, and automatable SaaS creation
🧠 Feeds every stage of the ConnectSoft AI Software Factory — from code to observability
🧩 Ensures clean edition separation, tenant contextualization, and modular service generation
📚 Enables platform-wide consistency, introspection, and self-healing

This is not a template. It’s the source of truth for intelligent, scalable, and self-evolving software.

📦 Agentic SaaS Product Blueprint¶

🧭 Introduction to Agentic SaaS Product Blueprints¶

🧠 Why It Exists¶

🔗 Role in the Factory¶

📎 Example Artifact Families Triggered¶

🧠 Key Agents Involved¶

✅ Summary¶

🧭 Blueprint Role in Platform Generation¶

🏗️ What the Blueprint Generates¶

🧬 Blueprint-Driven Architecture (Visualized)¶

🔗 Referencing External Services¶

🧠 Agent Collaboration in Generation¶

🔍 Example Project Structure (Generated)¶

✅ Summary¶

🧭 When to Use: Scenarios and Triggers¶

✅ When to Use This Blueprint¶

🚫 When Not to Use¶

📦 Blueprint Promotion Trigger Points¶

🔁 Related DSLs Used at This Stage¶

✅ Summary¶

🧭 Core Concepts: Product, Edition, Tenant¶

🔷 1. Product¶

🟦 2. Edition¶

🟨 3. Tenant¶

🔁 Relationship Between Concepts¶

🔗 Referenced DSLs¶

✅ Summary¶

🧭 Agentic Collaboration Overview¶

👥 Key Agents and Their Responsibilities¶

🧭 Collaborative Blueprint Flow¶

🤖 Orchestration Highlights¶

📎 Sample Agent Prompts Involved¶

🧠 Collaboration Trace Example¶

✅ Summary¶

🧭 Generated .NET Core Platform Template¶

📦 What the Platform Template Contains¶

🧬 Example Folder Structure¶

🔗 Integration Style (Aspire-Inspired)¶

⚙️ Edition-Aware Configuration in Template¶

🧠 Agent Responsibility Breakdown¶

✅ Summary¶

🧭 Platform Template Structure¶

📂 Top-Level Directory Layout¶

📦 Project Breakdown¶

🧩 Key Blueprint-Aligned Assets¶

🧱 Sample Startup Registration (AI-Generated)¶

🎯 Design Goals of the Structure¶

🧠 Memory + Traceability Integration¶

✅ Summary¶

🧭 Platform Services and Shared Libraries¶

📦 Shared Libraries in Platform.Shared/¶

🧠 Blueprint DSL → Code Generation Mapping¶

⚙️ Example Usage of Shared Services¶

📎 Additional Shared Utilities¶

🔌 Cross-Service Integration¶

🧠 Agent Contributions to Shared Code¶

✅ Summary¶

🧭 Feature Matrix Blueprinting¶

🗂️ What Is the Feature Matrix?¶

📘 YAML Feature Matrix Fragment (from DSL or inline)¶

🔁 How It's Used by the AI Factory¶

🔄 Traceable Matrix Storage¶

📎 Feature Matrix Evolution Events¶

🧠 Agent Collaboration with Feature Matrix¶

✅ Summary¶

🧭 Edition Blueprint Generation¶

🏷️ What Is an Edition Blueprint?¶

🔄 Generation Flow¶

🧬 Example YAML: vet-pro-edition.yaml¶

📁 Where It Lives¶

🧠 Purpose in Factory Flow¶

🧠 Agents Involved in Edition Blueprint Creation¶

✅ Summary¶

🧭 Tenant Context and Overrides¶

🟨 What Is a Tenant in This Context?¶

🧬 Sample Tenant Profile YAML¶

🔄 Tenant-Edition Linkage¶

🧠 Tenant-Aware Behaviors Enabled¶

🔧 Platform-Level Code Usage¶

📎 Traceability in Blueprint¶

📦 Shared Libraries in `Platform.Shared/`¶

🧬 Example YAML: `vet-pro-edition.yaml`¶