🧬 DSL Knowledge Index¶

Purpose and Scope¶

🎯 What Is a DSL in ConnectSoft?¶

In the context of the ConnectSoft AI Software Factory, a Domain-Specific Language (DSL) is a structured, declarative representation that defines:

The expected format, constraints, and relationships of generated artifacts
The intent behind blueprints, spans, test cases, telemetry, UI components, etc.
A language-level contract used by agents to:
Generate reusable code
Validate output
Drive prompt injection
Enforce consistency across domains and tenants

📚 Examples of DSLs¶

DSL Name	Domain	Purpose
`dsl-actor-handler`	Backend	Defines a message-based actor that processes a command and emits a response
`dsl-feature-flow`	Orchestration	Describes the flow of steps for a specific business scenario
`dsl-telemetry-span`	Observability	Defines telemetry span structure for tracing a user or system action
`dsl-ai-skill-call`	AI Kernel	Declares inputs and outputs of a skill or planner action
`dsl-ui-form-section`	Frontend	Declaratively specifies UI input fields, validation, and layout
`dsl-specflow-scenario`	Testing	Represents a BDD scenario outline for use in SpecFlow or Gherkin

🎯 Purpose of the DSL Knowledge Index¶

The DSL Knowledge Index is a first-class knowledge document that:

📦 Stores all DSL definitions, metadata, tags, and versions
📚 Enables semantic search and similarity-based retrieval
🔗 Powers prompt enrichment by injecting DSL examples and schemas
🧠 Drives agent decision-making based on DSL availability and compatibility
🔄 Supports regeneration, validation, and evolution of code artifacts
🤝 Enables multi-agent coordination through shared DSLs

🌐 Scope of This Document¶

This documentation defines:

The structure of the DSL registry (index, graph nodes, vector memory)
How DSLs are linked to generated assets
How DSLs influence prompt construction and validation
Which agents use DSLs and how
How DSLs evolve across versions, forks, and tenants
How DSL coverage, reuse, and quality are measured

It is not a DSL specification guide (that’s per DSL). Instead, this document defines the knowledge and infrastructure around DSLs.

🔖 Tags and Indexes Used¶

Each DSL entry will contain tags like:

domain:backend, layer:frontend, purpose:test, tenant:shared
confidence: 0.93, sourceAgent: DSLDesignerAgent, usedIn: prompt-blueprint-login

This supports advanced queries like:

List all DSLs used in authentication prompts by QA agents

Or vector lookups like:

Find DSLs similar to "async message handler with retry policy"

✅ Summary¶

The DSL Knowledge Index:

Serves as a centralized, evolving, multi-agent registry of semantic DSL definitions
Powers prompt injection, artifact validation, and reuse strategies
Bridges template generation, code output, trace validation, and feedback loops

🧱 DSL Types and Domains¶

🧬 Why Categorize DSLs?¶

The ConnectSoft AI Software Factory must support thousands of DSL definitions across varying:

Engineering layers (frontend, backend, observability, test, data)
Functional domains (identity, messaging, payments, reporting)
Artifact targets (models, events, UI, tests, spans, skills)
Agent workflows (blueprint generation, prompt planning, validation)

To support discoverability, prompt injection, and smart reuse, each DSL is categorized by type and domain.

📂 DSL Type Taxonomy¶

Type	Description	Typical Output
Blueprint DSL	Logical feature model or service spec	Classes, handlers, commands
Model DSL	Entities, aggregates, DTOs, records	NHibernate or EF models
UI DSL	Screens, form sections, UX flows	Blazor/Angular components
API DSL	Request/response pairs, endpoint definitions	REST or gRPC controllers
Event DSL	Domain events, integration events	Message classes
Span DSL	Observability/tracing structure	OpenTelemetry span spec
Test DSL	Scenarios, conditions, assertions	SpecFlow, BDD, MSTest
AI Skill DSL	Semantic Kernel skill definitions	Planner-friendly skill structure
Prompt DSL	Parameterized prompt + schema	Reusable prompt patterns
Validation DSL	Input validation and business rules	FluentValidation or JSON Schema
Infrastructure DSL	Health checks, queues, configs	Bicep/YAML/Infra-as-Code templates

🌍 DSL Domain Examples¶

Each DSL also belongs to a functional domain, allowing:

Tenant/domain-scoped reuse
Agent filtering during generation
Semantic trace linking

Domain	Example DSLs
`authentication`	`dsl-login-handler`, `dsl-auth-span`, `dsl-session-model`
`user-management`	`dsl-user-profile`, `dsl-invite-user`, `dsl-deactivate-flow`
`messaging`	`dsl-sms-event`, `dsl-email-template`, `dsl-retry-policy`
`notifications`	`dsl-notify-user`, `dsl-alert-type`, `dsl-push-channel`
`reporting`	`dsl-report-query`, `dsl-export-csv`, `dsl-scheduled-task`
`ai-agents`	`dsl-skill-call`, `dsl-agent-flow`, `dsl-memory-embedding`

DSLs can be reused across domains with explicit tags (shared, tenant-specific, agent-internal).

🔖 Recommended Tagging Convention¶

id: dsl-user-registration-flow
type: blueprint
domain: authentication
tags:
  - layer:backend
  - tenant:shared
  - usedIn: prompt-blueprint-user-registration
  - confidence: 0.91

📌 Key Takeaways¶

DSLs are not flat — they are typed, scoped, and linked
This categorization powers:
- 🔍 Better search and indexing
- 🤖 Agent compatibility and filtering
- 🔄 Prompt reuse and alignment
- 🧠 Semantic similarity retrieval
DSLs span the full stack — from telemetry to UI, events to validation

🧾 DSL Structure & Metadata Model¶

🧱 What Is a DSL Structurally?¶

Each DSL entry in the ConnectSoft Knowledge Graph is stored as a versioned semantic unit in YAML or JSON format. It includes:

A schema definition for generated artifacts
Metadata for indexing, discovery, and memory
Optionally, examples, injections, and validation rules

DSLs are not just definitions — they are knowledge-first building blocks.

📦 DSL Knowledge Unit Schema¶

id: dsl-user-handler
version: v1.2.0
type: blueprint
domain: user-management
layer: backend
description: >
  Declares a message-based command handler for user operations.
  Emits a response DTO and triggers a domain event.
fields:
  commandName: string
  responseType: string
  emitsEvent: bool
  retryPolicy: enum[None, Linear, Exponential]
tags:
  - tenant:shared
  - usedIn: prompt-blueprint-user-registration
  - status: active
  - createdBy: DSLDesignerAgent
confidence: 0.93
examples:
  - commandName: InviteUserCommand
    responseType: InviteUserResponse
    emitsEvent: true
    retryPolicy: Exponential
validationRules:
  - rule: "commandName must end with 'Command'"
    severity: error
    appliesTo: commandName
storage:
  location: blob://dsls/dsl-user-handler-v1.2.0.yaml
  embedded: true
embedding:
  vectorId: vec-dsl-user-handler-v1.2
  tags: [blueprint, backend, handler]

🧠 Metadata Breakdown¶

Field	Description
`id`	Unique identifier for the DSL
`type`	What kind of artifact the DSL defines
`domain`	Functional context (e.g., messaging, auth)
`layer`	Architecture layer (frontend, backend, test)
`version`	SemVer-style version string
`tags`	Semantic tags for indexing and filtering
`confidence`	Quality score based on feedback and reuse
`examples`	Canonical or tenant-specific samples
`validationRules`	Optional static checks
`storage`	Where the DSL file resides
`embedding`	Vector representation metadata

🔐 Reserved Tags¶

To support factory-level consistency:

Tag	Purpose
`tenant:shared`	Global DSL available to all tenants
`status:deprecated`	Obsolete and should not be used in new generation
`createdBy:AgentX`	Attribution to source agent
`linkedTo:template-user-registration`	Usage traceability

💡 Embedding DSLs¶

Each DSL is also embedded into a vector store (e.g., Azure Cognitive Search or Qdrant) using the DSL body and description.

This enables:

Prompt injection via similarity search
Agent lookup: “Find DSLs like this”
Drift detection based on structure changes

🧬 DSL Example: UI Form Section¶

id: dsl-ui-form-section
type: ui
domain: user-profile
layer: frontend
fields:
  label: string
  fieldName: string
  inputType: enum[text, email, phone, date]
  required: bool
  placeholder: string
  hint: string

Used in Blazor form generators and prompt UI design flows.

✅ Summary¶

Each DSL knowledge unit is:

📦 Structured as a YAML/JSON schema with metadata
🔗 Linked to tags, vectors, and generation usage
🧠 Fully embeddable for memory and prompt injection
🧪 Validatable with optional static rules

🔄 Lifecycle of a DSL¶

📘 Why Define a DSL Lifecycle?¶

DSLs in the ConnectSoft AI Software Factory are not static files — they are living semantic contracts. A clear lifecycle ensures:

Controlled introduction of new DSLs
Safe evolution and deprecation
Versioning across agents, tenants, and generations
Visibility into which versions are active, stable, or outdated

A broken DSL breaks everything that uses it: prompts, templates, outputs, validation, and feedback.

🧬 DSL Lifecycle States¶

State	Description	Who Can Transition
Draft	New DSL under design, not used in generation	DSLDesignerAgent, human
Active	DSL approved and ready for generation	ValidatorAgent, DSLCurator
Stable	Used in validated traces and production; locked for changes	CoordinatorAgent, Governance layer
Deprecated	Marked for removal; replaced by successor DSL	DSLJanitorAgent, DSLCurator
Archived	Read-only, no longer usable or referenced	ArchiverAgent, Admins only

🔁 Lifecycle Transition Rules¶

flowchart TD
    D[Draft] --> A[Active]
    A --> S[Stable]
    A --> Dep[Deprecated]
    S --> Dep
    Dep --> Arc[Archived]
    D --> Dep

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

Transitions require agent approval or confidence thresholds.

📦 DSL Evolution Patterns¶

Pattern	Use Case	Result
Minor version update	Add optional field	`v1.2.0 → v1.3.0`
Major version update	Breaking change to structure	`v1 → v2` fork
Fork for tenant	Customization by tenant	`dsl-login-handler → dsl-login-handler-petclinic`
Composition	Merge multiple DSLs	`dsl-feature → dsl-feature + dsl-metrics`
Extension	Add fields via inheritance	DSL uses `extends` tag or schema

🔖 Metadata for Lifecycle Tracking¶

status: stable
version: v1.2.0
replaces: dsl-user-handler-v1.1.0
replacedBy: null
createdOn: 2025-04-30
lastUsedInTrace: trace-id-872
validatedBy: TestAgent, BlueprintAgent

Each lifecycle action is auditable via the graph changelog and DSL index logs.

🤖 Agent Roles in DSL Lifecycle¶

Agent	Role
`DSLDesignerAgent`	Proposes and defines new DSL
`ValidatorAgent`	Runs conformance checks before activation
`FeedbackAgent`	Collects votes and trace impact to evaluate readiness
`CoordinatorAgent`	Marks DSL as stable when used successfully
`DSLJanitorAgent`	Identifies unused or obsolete DSLs for deprecation
`GovernanceOrchestrator`	Enforces policies on DSL ownership and lifecycle state

✅ Summary¶

DSL lifecycle is structured and controlled:

Prevents regressions and unsafe generation
Encourages stability before widespread reuse
Enables scoped customization by tenant/project
Links generation, usage, and memory with traceability

🧾 Versioning and Forking Rules¶

🎯 Why Version and Fork DSLs?¶

In a large-scale AI Software Factory, DSLs evolve due to:

New requirements
Breaking schema changes
Tenant-specific customizations
Feature specialization
Technology stack variations

To preserve trace integrity, reproducibility, and compatibility, we enforce clear rules for versioning and forking.

🔢 Semantic Versioning for DSLs¶

DSLs follow a SemVer-like scheme:

v<MAJOR>.<MINOR>.<PATCH>

Version Part	Meaning	Rules
`MAJOR`	Incompatible schema change	Requires deprecation or fork of prior version
`MINOR`	Backward-compatible enhancement	Existing traces remain valid
`PATCH`	Metadata or tag fix	No structural change; reindex only

DSLs with the same major version are assumed structurally compatible.

🔁 Example¶

dsl-user-handler-v1.1.0 → v1.2.0 → v2.0.0

v1.2.0: adds retry policy (optional field)
v2.0.0: changes field name from commandName to actionName → breaking

🌱 Forking DSLs¶

A fork is a derived DSL with intentional schema divergence for a:

Tenant (petclinic, ecomx, financeplus)
Project (connectsoft-gateway)
Architecture variation (e.g., actor-based vs HTTP-based)

Forks are linked via the forkedFrom and forks graph edge:

id: dsl-login-handler-petclinic
forkedFrom: dsl-login-handler
tenant: petclinic
changes:
  - renamed field `userId` → `vetId`
  - added field `clinicContextId`

Forks are treated as distinct nodes in the knowledge graph.

🧠 Versioning Rules Summary¶

Action	Allowed?	Result
Change required field → optional	✅ MINOR version bump
Add new optional field	✅ MINOR
Rename existing field	❌ Must create a new MAJOR version or fork
Remove field	❌ Fork or MAJOR required
Metadata/tag update only	✅ PATCH
Schema override by tenant	✅ Fork required

🔄 Graph Edges for Version and Fork Lineage¶

Edge	Purpose
`replaces →`	Shows upgrade path between versions
`forkedFrom →`	Links derived DSL to original
`extends →`	Reuse + override base DSL structure
`linkedTo →`	Shows usage in prompts/templates/tests

All versioning and forking history is queryable and visualizable.

📊 Version Coverage and Usage Metrics¶

Track:

Most used version per DSL
Fork divergence metrics
Obsolete DSLs used in old traces
Number of tenants sharing same DSL major version
Confidence trends per version

🛠️ Automation Support¶

Agents automatically detect incompatible changes and suggest forks
Coordinators trigger regeneration if DSL version becomes invalid
Prompts adapt DSL version via vector matching or upgrade rules

✅ Summary¶

Versioning and forking are core to safe DSL evolution:

MAJOR = break, MINOR = add, PATCH = fix
Forks isolate tenant-specific logic
Traceability ensures reproducibility and recovery
Agents collaborate via lineage metadata

🌐 DSL in the Knowledge Graph¶

🧠 Why Model DSLs in the Graph?¶

DSLs aren’t just isolated definitions — they’re semantic knowledge entities that participate in:

Prompt planning
Multi-agent collaboration
Memory embedding
Trace completeness scoring
Template and test generation

Representing them in the Knowledge Graph allows:

🔗 Linkage to outputs, prompts, tests, and telemetry
🧠 Similarity-based lookup using vector embeddings
🧬 Structural introspection (fields, versions, examples)
🔄 Automated lifecycle tracking

🔗 Graph Node Structure for DSL¶

Each DSL becomes a typed node in the Knowledge Graph:

type: DSL
id: dsl-user-handler-v1.2.0
title: User Handler DSL (v1.2.0)
layer: backend
domain: user-management
embeddingId: vec-dsl-user-handler-v1.2
confidence: 0.91
linkedArtifacts:
  - template-user-handler
  - span-user.handler.invoked
  - test-user-handler.feature
createdBy: DSLDesignerAgent
forkedFrom: dsl-user-handler-v1.0.0
tags: [blueprint, handler, shared]

🔁 Example DSL Graph Edges¶

graph LR
  DSL1[dsl-user-handler-v1.2.0]
  T1[template-user-handler]
  P1[prompt-blueprint-user-handler]
  S1[span-user.handler.invoked]
  TEST1[test-user-handler.feature]
  DSL1 -->|usedBy| T1
  DSL1 -->|usedIn| P1
  DSL1 -->|spans| S1
  DSL1 -->|validates| TEST1

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

🧬 Graph Relations¶

Relation	Meaning
`usedBy → template`	Template generation driven by this DSL
`usedIn → prompt`	Prompt planning enriched by this DSL
`defines → span/event`	DSL formalizes a trace/telemetry shape
`validatedBy → test`	Test references fields in this DSL
`forkedFrom → DSL`	Fork lineage
`replaces → DSL`	Version lineage
`extends → DSL`	Inheritance across DSLs

🔍 Example Query Use Cases¶

“Find all DSLs used in authentication flows”
“List prompts linked to deprecated DSLs”
“Which templates are still using v1.x of dsl-user-handler?”
“Show all DSLs related to user-invite span traces”
“Trace → what DSLs, templates, and tests it depends on”

📊 Graph Enrichment Over Time¶

As DSLs are used:

New nodes and edges are added (usedIn, linkedTo, forkedFrom)
Confidence scores increase or decay
Agents query DSLs based on vector proximity, not hard IDs
DSLs become routing surfaces for prompt orchestration and trace validation

🧠 Memory and Embeddings¶

Each DSL node also has:

Field	Purpose
`embeddingId`	Reference to vector index entry
`vectorTags`	Domain, layer, purpose, tenant
`embeddingSource`	DSL schema + examples + description

Agents can do queries like:

"Find a DSL similar to an event handler that emits retries"

And get a graph-matched DSL ready for prompt or code injection.

✅ Summary¶

Modeling DSLs in the graph gives:

First-class traceability and observability
Smart agent planning based on actual usage
Rich query, fork, and impact tracking
Embedding-powered similarity for prompt adaptation

🗂️ Indexing Strategy¶

🎯 Why Index DSLs?¶

DSLs must be:

🔍 Searchable by agents (semantic, metadata, usage context)
⚡️ Fast to retrieve for prompt injection
🧠 Embeddable into memory systems (vector search, hybrid search)
📈 Filterable by tags, layers, versions, tenants, and confidence levels

The DSL index is a high-performance semantic registry — not just a flat list.

🗂️ Types of Indexes¶

Index Type	Purpose
Tag Index	Fast filtering by metadata (type, domain, layer)
Vector Index	Similarity-based DSL retrieval using semantic embedding
Text Index	Full-text search on DSL description, examples, field names
Tenant/Project Index	Partitioned retrieval scoped to tenant, workspace, or project
Usage Index	Links DSLs to artifacts like prompts, templates, spans
Confidence Index	Score-based prioritization and decay tracking
Lifecycle Index	Status-based filtering (draft, active, deprecated)
Change Index	Diff- and version-aware change history
Agent Compatibility Index	DSLs usable by given agent role/type
Injection Suitability Index	Determines DSL’s usability for prompt injection
Span-Linked Index	Shows DSLs defining telemetry shapes
Fork Lineage Index	Cross-version and cross-tenant lineage browsing

🏷️ Example Tag-Based Index Fields¶

- id: dsl-notify-user-v1.1.0
  tags:
    type: event
    domain: notifications
    layer: backend
    confidence: 0.94
    status: active
    tenant: shared
    usedIn: prompt-user-invite
    linkedTo: span.notify_user.sent
    createdBy: DSLDesignerAgent

📦 Vector Embedding Strategy¶

Source: description, fields, examples, domain, prompt usage
Stored in: Azure AI Search, Qdrant, or Pinecone
Retrieval: topK, threshold, semantic match + tag filter

Example:

"DSL to define async retryable command in messaging layer"
→ matches dsl-retryable-command-handler

🔍 Search Examples¶

Query	Result
`tag:frontend AND domain:user-profile`	All UI DSLs for user profile forms
`similarTo: span-user.login → type:test`	DSLs defining test flows for login span
`usedIn: prompt-notify-user`	DSLs linked to notification prompt
`confidence < 0.6 AND status:active`	Weak DSLs needing review
`forkedFrom: dsl-auth-handler`	All forks of the auth DSL
`extends: dsl-feature-base`	Inherited DSLs for features

🤖 Agent Retrieval APIs¶

Agents will use DSLIndex.GetDSL(criteria) or DSLIndex.QueryByVector(text).

Retrieval Modes:¶

GetById("dsl-user-handler-v1.2.0")
SearchByTags({ type: "test", domain: "auth" })
FindSimilar("retryable backend command DSL")

📊 Confidence-Weighted Ranking¶

When retrieving DSLs:

DSLs with high confidence are ranked higher
Confidence affected by:
- Feedback votes
- Successful trace completions
- Prompt outcome quality
- Failed generations or missing spans

✅ Summary¶

The DSL indexing system supports:

🔍 Fast, tag-based filtering
🧠 Semantic similarity via embeddings
🤖 Agent-friendly APIs for prompt planning
🔄 Dynamic ranking based on usage and feedback
🧬 Multi-dimensional cross-indexing (domain, layer, lifecycle, tenant)

🔗 Linking DSLs to Templates and Prompts¶

🤖 Why Link DSLs to Prompts and Templates?¶

DSLs are not passive metadata — they are active drivers of:

🔧 Code template configuration
📤 Prompt-based artifact generation
✅ Output validation and trace alignment
🔄 Cross-agent planning (between DSL designers, blueprint builders, test agents)

A prompt without DSL is generic. A prompt enriched by a DSL is domain-aware, testable, and reusable.

🔗 Link Types in Knowledge Graph¶

From	Relation	To	Purpose
`DSL`	`usedIn`	`Prompt`	Injects structure into generation
`DSL`	`linkedTo`	`Template`	Guides template selection and validation
`DSL`	`defines`	`Span/Test/Model`	Drives expected structure
`Prompt`	`requiresDSL`	`DSL`	Prompt dependency declared explicitly
`Agent`	`usesDSL`	`DSL`	Agent relies on the DSL at runtime

📥 Prompt DSL Injection Example¶

Prompt Template:

promptId: blueprint-user-handler
requiresDSL: dsl-user-handler
template:
  "Generate a command handler using the following structure:
  {{dsl-user-handler | inject: fields, examples}}"

Result:

Prompt is dynamically updated with DSL content
Agent receives schema + examples for generation
Prompt adapts per tenant/fork/version

🧩 Template-Derived Generation Example¶

Template: template-user-handler.cs

Linked DSL: dsl-user-handler

Usage:

DSL fields → code snippets
DSL retry policy → Polly retry block
DSL emitsEvent → auto-generate domain event class
DSL used to validate generated class shape during test

🧠 Prompt Construction from DSL¶

Some prompts are generated on the fly from DSL using prompt blueprints:

dsl: dsl-telemetry-span
promptBlueprint:
  "You are designing a telemetry span for {{dsl.domain}}. The operation name should be: {{dsl.spanName}}.
   Include the following tags: {{dsl.tags}}.
   Ensure the span duration logic follows: {{dsl.durationLogic}}."

This allows agents like PromptPlannerAgent to generate DSL-aware prompt candidates automatically.

🔍 Query Examples¶

Use Case	Query
Prompts using deprecated DSLs	`prompt.usedIn → DSL[status=deprecated]`
Templates using outdated DSL versions	`template.linkedTo → DSL[version < v2.0.0]`
All prompts powered by UI DSLs	`prompt.requiresDSL → DSL[type=ui]`
DSLs used in prompt injection but never in template validation	Cross-link inconsistency

✅ Summary¶

DSLs power prompt and template generation:

💬 Prompts are semantically enriched with DSL content
🧱 Templates are scaffolded using DSL-defined structure
🔄 Graph tracks all usedIn, linkedTo, defines, and requiredBy relations
🧠 Agent prompts adapt based on DSL context (tenant, version, fork)
📊 DSL usage across prompts and templates drives traceability and refactor planning

🔍 Search and Retrieval Patterns¶

🔎 Why Searchable DSLs Matter¶

Agents must dynamically discover DSLs in order to:

Plan prompts with the most suitable DSLs
Reuse previously successful blueprints and examples
Avoid broken or deprecated versions
Match tenant-specific needs (e.g., forks, extensions)
Automatically validate trace completeness against DSL definitions

The system provides multi-modal search capabilities:

Textual
Tag-based
Vector semantic similarity
Graph-traversal queries

🛠️ Search Modes Supported¶

Search Mode	Purpose	Example
Text search	Search DSL titles, descriptions, and examples	`"login handler"`
Tag search	Filter by metadata tags	`type: blueprint AND domain: auth`
Vector similarity	Retrieve DSLs close to a textual concept	`"retryable async span for email"`
Graph queries	DSLs connected to certain prompts, spans, or tests	`usedIn → prompt-login-user`
Field match	DSLs with specific field structures or types	`fields[*].inputType = phone`

🧠 Agent-Level Retrieval API¶

Agents access DSLs using the DSLSearchService with layered query strategies:

var dsl = dslSearchService.FindBestMatch(new DSLSearchQuery {
  SemanticHint = "form to collect user's birthdate",
  RequiredTags = ["type: ui", "domain: user-profile"],
  MinConfidence = 0.85,
  MustBeActive = true
});

This returns DSL definitions injected into the prompt planner or generation step.

📦 Result Scoring and Ranking¶

Confidence-based prioritization
Reuse-based weighting (seen in successful traces)
Agent compatibility (linked to same agent family)
Tenant specificity (e.g., prefer fork for petclinic)
Semantic closeness (via embedding cosine similarity)

📘 Example: Semantic Retrieval Workflow¶

PromptPlannerAgent → wants to generate a UI form
DSLSearchQuery:
  - "Form section for user contact info"
  - Type: ui
  - Domain: user-profile
→ Result: dsl-ui-form-section-v1.3.1
→ Inject into prompt → Generate form scaffold

If tenant is ecomx, the agent may retrieve:

dsl-ui-form-section-ecomx-v1.1.0

🔍 Advanced DSL Queries (Human or Agent)¶

Query	Interpretation
`DSLs used in user-login prompt`	Reverse-lookup via graph edge `usedIn → prompt-login-user`
`Forks of dsl-user-handler still active`	`forkedFrom: dsl-user-handler AND status:active`
`All telemetry span DSLs with retry logic`	`type: span AND fields[*].name = retryPolicy`
`Most reused DSLs in the onboarding domain`	Usage index + domain filter

🧠 Smart DSL Suggestions¶

Prompt planners can query for:
- “Top 3 DSLs similar to current prompt”
- “DSLs most used with current agent’s output type”
- “DSLs seen in successful test traces with same tags”

✅ Summary¶

DSL search and retrieval is multi-layered and agent-aware:

🔎 Enables both precise lookup and fuzzy discovery
🧠 Vector search unlocks semantic-based planning
🧱 DSLs can be filtered, ranked, embedded, and injected at runtime
🌐 Graph links power reverse-trace and lineage queries
🤖 Planners, validators, and prompt builders rely on it daily

⚔️ DSL Conflict Resolution & Override¶

🎯 Why Do DSL Conflicts Happen?¶

In a multi-tenant, agentic AI system like ConnectSoft, DSL collisions can occur due to:

Different teams/agents defining similar DSLs independently
Forks evolving in incompatible directions
Deprecated DSLs still referenced in old prompts
Multiple DSLs with similar vector embeddings
Prompt injection using conflicting or ambiguous DSL structures

Without conflict resolution, the factory may generate invalid, mismatched, or duplicate artifacts.

⚠️ Types of DSL Conflicts¶

Type	Description	Example
Structural Conflict	Same DSL ID, different field structure	`dsl-user-handler` changed field `userId` → `userIdentifier`
Semantic Overlap	Different DSLs with nearly identical meaning	`dsl-auth-session` vs `dsl-session-auth`
Version Inconsistency	Prompt uses DSL v1 but template expects v2	`template-user-handler` requires `emitsEvent`, missing in v1
Fork Drift	Tenant fork diverges from base and breaks compatibility	`dsl-user-form-ecomx` no longer aligns with parent
Injection Override	Multiple DSLs match a prompt, both injected	Causes ambiguous prompt inputs

🔍 Conflict Detection Strategies¶

✅ Structural Diffing¶

Automatically compare field sets, types, constraints

✅ Embedding Similarity Collision¶

Flag DSLs with cosine similarity > 0.9 and different IDs

✅ Prompt Incompatibility Check¶

Detect if injected DSL violates prompt expectations

✅ Fork Validation¶

Check if forks removed required fields from base

✅ Feedback Score Divergence¶

High reuse DSLs with dropping confidence → investigate

🔄 Resolution Mechanisms¶

Scenario	Resolution Strategy
Same ID, different structure	❌ Invalid – block and require version bump
Conflicting semantic DSLs	Merge or rename with clearer scope
Fork drift	Freeze fork and regenerate from parent
Prompt-version mismatch	Auto-upgrade prompt or provide migration mapping
Duplicate injections	PromptPlannerAgent selects highest-confidence match or defers to user prompt

🧠 Conflict Metadata Example¶

conflictId: cfl-dsl-user-handler-v1.1-vs-v1.2
type: structural
description: v1.2 changes field name, breaks dependent templates
detectedBy: DSLValidatorAgent
severity: high
resolution: "Mark v1.1 as deprecated, regenerate trace plan"
linkedArtifacts:
  - prompt-user-handler
  - template-user-handler

All conflicts are indexed and linked to their origin DSLs and affected artifacts.

🤖 Agent Collaboration on Conflict Resolution¶

Agent	Role
`DSLValidatorAgent`	Detects structural/schema incompatibilities
`PromptPlannerAgent`	Flags prompt collisions and injection ambiguity
`TraceReplayerAgent`	Detects DSL-version mismatch from past trace
`GovernanceOrchestrator`	Approves major DSL changes and forks
`FeedbackAgent`	Surfaces usage errors that may stem from DSL issues

🧱 Prompt Injection Protection¶

Prompts define DSL strictness settings:

requiresDSL:
  id: dsl-user-handler
  version: ^1.2
  enforceStrictSchema: true
  allowForks: false

If a conflicting DSL is suggested, prompt planning fails or requests resolution.

✅ Summary¶

DSL conflict management is essential to maintain:

✨ Generation consistency
🧪 Test and span validation accuracy
🔄 Prompt injection safety
📚 DSL lineage clarity
🤖 Multi-agent reliability

The DSL Knowledge Graph tracks and resolves conflicts through versioning, tagging, and semantic analysis.

📦 Common DSL Examples¶

🎯 Purpose of Examples¶

Concrete examples:

Clarify the shape and structure of typical DSLs
Help agents understand how to generate, validate, and inject DSLs
Demonstrate cross-layer and cross-domain DSL usage
Act as reference patterns in prompt construction and regeneration flows

🔁 Example: Backend Command Handler DSL¶

id: dsl-user-handler
type: blueprint
domain: user-management
layer: backend
version: v1.2.0
fields:
  commandName: string
  responseType: string
  emitsEvent: bool
  retryPolicy: enum[None, Linear, Exponential]
description: Declares a command handler used to manage user actions.
examples:
  - commandName: "InviteUserCommand"
    responseType: "InviteUserResponse"
    emitsEvent: true
    retryPolicy: "Exponential"

Used by:

Prompt: prompt-blueprint-user-handler
Template: template-user-handler.cs
Test: test-user-handler.feature
Span: span.user.handler.invoked

🧬 Example: UI Form Section DSL¶

id: dsl-ui-form-section
type: ui
domain: user-profile
layer: frontend
version: v1.0.0
fields:
  label: string
  fieldName: string
  inputType: enum[text, email, phone, password]
  required: bool
  placeholder: string
description: Represents a UI input field for form section layout.
examples:
  - label: "Email Address"
    fieldName: "email"
    inputType: "email"
    required: true
    placeholder: "example@site.com"

Used in:

Blazor form generators
UI schema injection in prompts
Frontend spec generation for QA scenarios

📊 Example: Observability Span DSL¶

id: dsl-span-notify-user
type: span
domain: notifications
layer: observability
version: v2.1.0
fields:
  spanName: string
  tags:
    userId: string
    channel: enum[email, sms, push]
  successCondition: string
  failureEvent: string
description: Defines a tracing span emitted when a user is notified.
examples:
  - spanName: "notify_user"
    tags:
      userId: "{{user.id}}"
      channel: "email"
    successCondition: "response.status == 200"
    failureEvent: "NotifyUserFailed"

Used to:

Inject OpenTelemetry specs
Validate trace spans
Generate span-metrics.json

🧪 Example: SpecFlow Test DSL¶

id: dsl-specflow-user-invite
type: test
domain: user-management
layer: testing
version: v1.0.0
fields:
  scenarioTitle: string
  preconditions: list[string]
  steps: list[string]
  expectedOutcome: string
description: BDD-style SpecFlow test definition for user invite flow.
examples:
  - scenarioTitle: "Invite new user with valid email"
    preconditions: ["User does not exist"]
    steps:
      - "When admin submits invite form"
      - "Then system sends invite email"
    expectedOutcome: "User receives invite"

Used by:

QA Engineer Agent
TestTemplateAgent
Regression trace planners

🤖 Example: AI Skill Invocation DSL¶

id: dsl-skill-call-user-recommender
type: ai-skill
domain: recommendation
layer: ai-kernel
version: v1.0.0
fields:
  inputUserId: string
  recommendationType: enum[course, article, video]
  responseFormat: string
description: Semantic Kernel-compatible skill invocation spec for user recommendation.
examples:
  - inputUserId: "user-123"
    recommendationType: "course"
    responseFormat: "json"

Injected into:

PromptPlannerAgent
SK Planner DSL builder
AI observability traces

✅ Summary¶

These examples show DSLs across:

🧱 Layers: frontend, backend, observability, testing, AI
🎯 Domains: auth, user-management, notifications, UI, skill execution
🔗 Graph usage: templates, prompts, spans, tests
🧠 Prompt enrichment and code generation integration

🔄 DSL-Driven Generation Flow¶

🎯 Why Use DSLs to Drive Generation?¶

DSLs act as structured semantic anchors that guide:

Prompt planning
Template parameterization
Test scenario generation
Event and span shaping
Validation across all outputs

They turn generation from "free text prompting" into a deterministic, observable, and reusable process.

🧩 Generation Flow Powered by DSLs¶

flowchart TD
  A[PromptPlannerAgent] -->|Query DSL| B[DSL Index]
  B --> C[DSL Injection]
  C --> D[Prompt Generation]
  D --> E[Template Injection]
  E --> F[Test Planner]
  F --> G[Span Generator]
  G --> H[FeedbackAgent]
  H -->|Trace score| A

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

Each step uses the DSL to align generation outputs across agents and artifacts.

📥 1. DSL Retrieval¶

Agent searches DSLIndex by semantic intent, tags, or tenant context
Retrieves full schema, examples, usage history, vector ID
DSL is loaded into short-term memory for immediate planning

💬 2. Prompt Construction¶

Inject DSL into structured prompt template
Adapt fields and values using DSL structure
Resolve forks or extensions if tenant-specific
Ensure version and tag compliance

Example:

Prompt: "Create a command handler for {{dsl.commandName}}"
→ DSL field: commandName = InviteUserCommand

🧱 3. Template Mapping¶

Template contains markers linked to DSL fields
Optional blocks (e.g., retry logic) toggled by DSL flags
DSL → Code DSL Translator generates base classes/interfaces
Ensures reproducibility and compliance

🧪 4. Test Case Generation¶

If DSL has examples, convert to test cases
Create BDD-style feature file or xUnit scenario
Mark trace expectations based on DSL constraints

Example:

Given the command is InviteUserCommand
When the command is processed
Then a domain event InviteUserIssued should be emitted

📊 5. Span & Telemetry Planning¶

DSL defines span name, tags, success conditions
Used to generate OpenTelemetry spans or span-metrics.json
Aligns with Observability Agent workflows

🧠 6. Memory Embedding & Indexing¶

DSL instance used in generation is embedded into trace memory
Feedback from success/failure tied back to DSL confidence score
DSL is linked to output artifacts for traceability

📤 7. Output Validation¶

Output artifacts (code, prompt, span, test) are validated against the originating DSL
ValidatorAgent checks structural conformance
TraceReplayerAgent ensures runtime alignment

📈 Trace Score Contributions¶

DSLs influence:

GenerationCompletenessScore
TraceValidationScore
PromptInjectionSuccessRate
Template-DSL Alignment Index

Each metric contributes to the DSL's evolving confidence level.

✅ Summary¶

The DSL-driven generation flow ensures:

🧱 Structure-aware generation
🔄 Repeatable and traceable outputs
🤖 Agent interoperability
🧪 Prompt + Template + Test + Trace coherence
📊 Feedback loops for quality improvement

🧬 DSL Extension and Composition¶

🎯 Why Compose and Extend DSLs?¶

As the platform grows, DSLs must support:

🧱 Reuse of base structures
🔀 Mix-and-match of DSL fragments
🧩 Specialization per domain or layer
🧬 Inheritance without redundancy
🧠 Modular planning by AI agents

A composable DSL system reduces duplication and enables semantic layering — like building blocks for generation.

🧩 Extension via `extends`¶

A DSL can inherit another DSL's fields and constraints:

id: dsl-user-invite-handler
extends: dsl-user-handler
fields:
  inviteTokenExpiry: int
  roleAssignment: string

In graph:

Edge: extends → dsl-user-handler
Result: full merged field schema
Agents are aware of both base and derived DSLs

➕ Composition via `includes`¶

DSLs can embed multiple fragments modularly:

id: dsl-composite-onboarding
includes:
  - dsl-ui-form-user
  - dsl-span-user-registration
  - dsl-test-onboarding

All included DSLs are logically merged, but not structurally inherited.

Useful for bundle planning or full-feature flows.

🧠 Example: DSL Composition Tree¶

graph TD
  A[dsl-composite-onboarding]
  A --> B[dsl-ui-form-user]
  A --> C[dsl-span-user-registration]
  A --> D[dsl-test-onboarding]

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

This composite DSL is used by:

CoordinatorAgent to plan onboarding flow
BlueprintAgent to match correct artifacts
FeedbackAgent to trace coverage

🌍 Use Case Examples¶

Case	Approach
Common base handler + specialized features	`extends: dsl-handler-base`
Bundled user registration spec	`includes: [dsl-ui, dsl-span, dsl-test]`
UI form with nested reusable sections	DSL fragment composition (nested includes)
Tenant-specific override	Fork → extend parent DSL with tenant fields

🧠 Extension Semantics¶

Rule	Behavior
Extended fields are merged	Available to prompt/template/planner
Conflicts are flagged	Override must be explicit
Composition does not imply inheritance	Included DSLs remain semantically linked
Composed DSLs can be versioned independently	Each fragment version is tracked separately

🛠️ DSL Transformer Support¶

Agents like DSLTransformerAgent and PromptPlannerAgent support:

🧬 Extension resolution (flattened DSL view)
🧩 Composition parsing for modular workflows
🧪 Test matrix expansion across composed DSLs
🔗 Graph enrichment for cross-DSL traceability

📘 Composition Example (Flattened)¶

flattenedFields:
  - commandName
  - responseType
  - emitsEvent
  - inviteTokenExpiry
  - roleAssignment
  - spanName
  - tags.userId
  - tags.channel
  - testSteps[]

This flattened DSL is injected into prompts or templates for generation.

✅ Summary¶

DSL extension and composition unlock:

🧱 Reuse across domains and layers
🧩 Bundled generation and planning
🔄 Semantic graph enrichment
🤖 Intelligent modularity for agents
📦 DSL-as-blueprint for complete flows

🧭 DSL Traceability and Lineage¶

🎯 Why Trace DSL Usage?¶

In a factory that creates 3000+ microservices and components:

Every artifact should be traceable to its semantic root (DSL)
Lineage tracking ensures observability, reusability, and auditability
Enables rollback, revalidation, and compliance checks
Agents must reason about what generated what — not just the final output

DSLs are the semantic DNA of the software factory. Their lineage builds semantic observability.

📊 DSL Traceability Graph¶

Each DSL participates in a knowledge lineage graph:

graph TD
  DSL[dsl-user-handler-v1.2.0]
  P[prompt-user-handler]
  T[template-user-handler.cs]
  S[span-user.handler.invoked]
  TEST[test-user-handler.feature]
  TRACE[trace-run-983]

  DSL -->|usedIn| P
  DSL -->|linkedTo| T
  DSL -->|defines| S
  DSL -->|validatedBy| TEST
  P -->|produced| TRACE
  T -->|usedIn| TRACE
  TEST -->|executesIn| TRACE

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

This forms a full traceability chain from DSL → generation → runtime traces.

🔗 Lineage Metadata in DSL Nodes¶

Each DSL node has lineage metadata:

lineage:
  createdBy: DSLDesignerAgent
  usedIn:
    - prompt-user-handler
    - template-user-handler
    - span-user.handler.invoked
  produces:
    - trace-user-handler-onboarding
  replacedBy: dsl-user-handler-v2.0.0
  forkedBy:
    - dsl-user-handler-petclinic

🧠 Agent Lineage Queries¶

Agent	Query Example	Purpose
PromptPlannerAgent	`GetDSLTraceSources(dsl-user-handler)`	Inject original examples
DSLValidatorAgent	`TraceImpactOfDeprecatedDSL(dsl-v1.1.0)`	Mark downstream invalid
GovernanceAgent	`ListForksOfBaseDSL(dsl-user-handler)`	Approve/reject divergent branches
FeedbackAgent	`TraceConfidenceDrop(dsl-auth-span)`	Analyze usage failures

📂 DSL Trace Record Format¶

Each generation run linked to a DSL includes a record like:

traceId: trace-user-handler-202406
dslId: dsl-user-handler-v1.2.0
promptId: prompt-user-handler
templateId: template-user-handler.cs
testId: test-user-handler.feature
spanId: span-user.handler.invoked
confidenceImpact: +0.06
forkContext: null

Used to:

Rebuild generation history
Re-evaluate test coverage
Visualize impact across DSL versions

🔄 Fork and Replace Tracking¶

The lineage graph supports:

replaces →: version migration
forkedFrom →: tenant specialization
extends →: inheritance trees
usedIn →: artifact dependency
invalidates →: trace deprecation upon DSL removal

🧬 Temporal Lineage Support¶

The factory supports temporal queries:

“What version of DSL was active in March 2024?”
“Show all generation traces from v1.1 to v1.2 transition”
“Detect regressions introduced after fork divergence”

Time-aware lineage allows snapshot comparison and compliance audit.

📊 Confidence & Decay from Trace Lineage¶

Each DSL’s confidence score is dynamically updated via:

Trace usage frequency
Trace success/failure ratio
Number of forks/version bumps
Feedback signals on prompt effectiveness

This helps identify:

🟢 DSLs that are healthy and reusable
🔴 DSLs that cause regressions or conflicts

✅ Summary¶

DSL traceability and lineage enables:

📈 End-to-end semantic observability
🔁 Historical regeneration and debugging
🧠 Agent-level context awareness
🔍 Impact analysis for upgrades or forks
🤖 Continuous confidence learning via trace feedback

🏷️ DSL Metadata and Enrichment Fields¶

🎯 Why Metadata Matters¶

DSLs are not just schemas — they’re semantic assets. Rich metadata allows:

🔍 Advanced filtering and discovery
📊 Observability and lineage tracking
🤖 AI-driven prioritization and planning
🧠 Cross-agent reasoning (Who? What? Where? When? Why?)

Metadata fields help DSLs participate in search, scoring, injection, validation, and graph indexing.

📦 Core Metadata Fields¶

Field	Purpose
`id`	Unique DSL identifier (`dsl-user-handler-v1.2.0`)
`title`	Human-readable DSL name
`description`	What the DSL represents
`type`	Category: `blueprint`, `span`, `ui`, `test`, `ai-skill`, etc.
`domain`	Business domain (`auth`, `user-management`, `notifications`)
`layer`	Architecture layer (`frontend`, `backend`, `observability`)
`version`	DSL semantic version
`status`	`draft`, `active`, `deprecated`, `replaced`
`createdBy`	Originating agent or user
`tags`	Searchable keywords (`retry`, `feature`, `email`, `critical`)
`tenant`	Source context: `shared`, `petclinic`, `custom`
`confidence`	DSL trust score (0–1.0) based on usage/feedback

🧪 Enrichment Fields¶

Field	Purpose
`examples`	Example values, helps generation and embedding
`embeddingId`	Link to vector DB index entry
`embeddingTags`	Additional semantic indexing context
`linkedArtifacts`	Prompts, templates, spans, tests using this DSL
`forkedFrom`	Parent DSL (for tenant-specialized forks)
`replaces`	Previous version lineage
`extends`	Inherited DSL ID
`includes`	Composed DSLs (for bundling)
`metrics`	Usage count, success ratio, trace frequency

🧠 Observability Hooks¶

Field	Function
`definesSpan`	Declares OpenTelemetry span structure
`linkedTraceTypes`	List of trace types using this DSL
`metricTags`	Tags expected in span output
`spanInjectionPrompt`	Predefined prompt to inject span spec
`failurePattern`	Regex or rule to detect trace failure against DSL
`feedbackImpact`	Adjusts confidence based on success/failure events

🧑‍🎓 Authorship and Ownership Fields¶

Field	Meaning
`authorId`	Original human contributor (if applicable)
`agentGenerated`	`true/false` flag if generated autonomously
`approvedBy`	Governance entity validating the DSL
`teamContext`	DevOps or Git project/repo context
`sourceRepo`	Where the DSL originated from (`lib-dsl-models`)

These fields are useful for auditing, governance, and traceback.

📥 Prompt Injection Hints¶

Field	Meaning
`promptInjectionMode`	`full`, `partial`, `composed`, `optional`
`injectionConstraints`	Must include fields, block conflicts, etc.
`promptTemplateId`	Preferred prompt template to use with this DSL
`agentAffinity`	Agent types this DSL is most compatible with

📘 Sample DSL Metadata Block¶

id: dsl-notify-user-v1.1.0
type: blueprint
domain: notifications
layer: backend
version: v1.1.0
description: Handles async notification delivery
createdBy: DSLDesignerAgent
status: active
confidence: 0.87
tags: [email, retry, feature]
tenant: shared
examples:
  - notifyType: "email"
    template: "WelcomeEmail"
embeddingId: vec-dsl-notify-user-v1.1
linkedArtifacts:
  - prompt-notify-user
  - template-notify-user
  - span.notify_user

✅ Summary¶

Metadata fields power:

🔍 Search and filtering
🧠 Semantic embedding
📊 Trace analysis and feedback learning
🤖 AI prompt planning and agent orchestration
📚 Governance and auditing

The richer the DSL metadata, the smarter the system becomes.

📈 DSL Confidence Score Model¶

🎯 Why Track DSL Confidence?¶

The factory needs to:

🧠 Prioritize reliable DSLs during generation
⚠️ Flag risky or decaying DSLs for review
🔄 Learn from successful vs. failed traces
🚦 Decide injection eligibility based on trust level

Confidence is a dynamic, learned metric that governs DSL prioritization, validation, and deprecation readiness.

📊 Confidence Score Model¶

Each DSL has a confidence value in the range 0.0 – 1.0.

It is calculated using a weighted signal model:

Signal Type	Examples	Weight
✅ Usage Successes	Successful traces, valid generations, test pass rate	+++
❌ Usage Failures	Broken traces, generation mismatches, user feedback	---
🧠 Feedback Events	Human/AIAgent upvotes/downvotes	++ / --
⏳ Inactivity	Stale or unused over time	−
⚠️ Conflict Alerts	Structural diffs, overlapping matches	--
🔀 Fork Divergence	Rapid forking from base	-
🏗️ Prompt Rejections	Failed injection or mismatched schemas	-

🧠 Signal Sources¶

Signals are collected from:

🟢 TraceRun Logs → span/test/result match
🔁 Generation History → prompt → template → trace alignment
👤 FeedbackAgent → upvote/downvote traces tied to DSL
🤖 ValidatorAgent → conformance analysis
📉 ConflictResolverAgent → warnings and diffs
📦 ForkTrackerAgent → mutation tracking across tenants

🧮 Confidence Update Cycle¶

Confidence scores are periodically recalculated:

📆 Daily batch update from trace system
🔁 Real-time adjustments for major success/failure events
🔄 Decay model for stale DSLs

Decay example:

lastUsed: 2023-12-01
confidenceDropPerWeek: 0.01

🚥 Confidence Thresholds (Usage Policy)¶

Range	Meaning	Platform Behavior
`> 0.9`	Strong, validated	Auto-injected and reused
`0.7 – 0.9`	Acceptable	Available for reuse and planning
`0.5 – 0.7`	Risky	Requires validation before reuse
`< 0.5`	Untrusted	Blocked from injection, flagged for fix
`= 0`	Deprecated	Marked invalid or removed

🛠️ Example DSL Record with Confidence¶

id: dsl-auth-span
version: v1.2.1
confidence: 0.71
signals:
  traceSuccessRate: 92%
  testFailureRate: 5%
  promptRejection: 2
  forkCount: 3
  userVotes: +15 / -1
lastUpdated: 2025-06-01

🧠 DSL Selection with Confidence¶

Agents can filter with MinConfidence:

dslSearchService.FindBestMatch(new DSLSearchQuery {
  domain: "auth",
  type: "span",
  minConfidence: 0.8
});

This ensures agents prefer healthy DSLs.

📊 Confidence Heatmap (Future Visualization)¶

A future dashboard will visualize:

Confidence over time (line chart)
DSL family comparison (fork vs. parent)
Signal breakdown pie chart
Correlation to trace coverage

✅ Summary¶

DSL confidence scoring enables:

📈 Trust-based reuse
🧠 Continuous learning
❌ Fault propagation detection
🧪 Quality-aware generation
⚖️ Governance and review prioritization

✅ DSL Review and Approval Workflows¶

🎯 Why a Review Workflow Is Critical¶

In a multi-agent system generating production-grade software:

DSLs encode logic, structure, and intent
Errors or misalignments in a DSL propagate into all generated outputs
Forks and tenant-specific DSLs must be curated
Security-sensitive or observability DSLs require human approval
Auditability is mandatory for compliance and traceability

DSLs are the core of semantic control — their lifecycle must be governed, reviewed, and approved.

🔁 DSL Lifecycle Stages¶

Stage	Description
`draft`	Created but not yet validated
`under-review`	Submitted for human/agent validation
`active`	Approved and available for use
`deprecated`	Marked for replacement, not recommended
`replaced`	Superseded by a new DSL version
`archived`	Removed from prompt injection pool

🛠️ Review Triggers¶

A DSL enters review when:

Created manually by human (UI/CLI)
Generated by agent with low initial confidence
Forked from a shared DSL
Version bump introduces breaking change
Conflict with existing DSL detected
Confidence drops below threshold

🧠 Review Agents and Roles¶

Agent	Role
`DSLValidatorAgent`	Schema integrity, structural soundness, conformance to standards
`PromptPlannerAgent`	Ensures the DSL injects cleanly into prompts
`ConflictResolverAgent`	Checks for overlap, fork divergence, version drift
`GovernanceAgent`	Enforces review policy (approvals, merges, freezes)
`FeedbackAgent`	Surfaces usage results and human feedback during review
`TraceCoverageAgent`	Confirms test + span + trace coverage from DSL

👤 Human Review Workflow (Optional)¶

Humans may review high-risk DSLs using:

A web UI with full metadata, confidence scores, impact analysis
Review checklist (schema, examples, tags, trace coverage)
Approval/rejection comments
Merge/fork resolution tools

Example:

review:
  status: pending
  assignedTo: "Platform Architect"
  checklist:
    - [x] Valid JSON/YAML
    - [x] Prompt Injection Passed
    - [ ] Span Coverage ≥ 90%
    - [ ] Conflict Check Cleared
  comments: "Please add a test example and clarify field `retryPolicy`"

📦 Fork Handling & Merging¶

Scenario	Action
Tenant fork matches parent	Auto-approve inheritance
Fork diverges semantically	Require review/merge
Fork has conflicting ID	Auto-reject, request rename
Fork introduces observability changes	Mandatory human review

All fork events are logged and linked to original DSL.

🚦 Automated Review Rules (Policy Engine)¶

Rules can be defined in governance policy:

reviewRules:
  - type: "span"
    domain: "auth"
    requiredConfidence: 0.85
    requireHumanApproval: true
    minTraceCoverage: 95%
  - type: "ui"
    autoApproveIf:
      confidence > 0.9
      AND examples.count ≥ 2

🔍 Review Status and History¶

Each DSL contains a review block:

review:
  status: approved
  approvedBy: GovernanceAgent
  approvedAt: 2025-06-03
  comments: "Reviewed for onboarding use case"

Full history is preserved for compliance and observability.

✅ Summary¶

The DSL review workflow ensures:

🧠 Safety and accuracy of generation flows
🧪 Full trace and test coverage
🔀 Fork and version sanity
🧑‍⚖️ Governance and audit readiness
🔁 Collaboration between agents and humans

🌐 DSL Fork Management and Multi-Tenant Specialization¶

🎯 Why Forking DSLs Is Essential¶

In a platform supporting many SaaS domains and tenants:

DSLs need to evolve per-tenant, per-feature, or per-regulation
Forking enables custom logic without rewriting everything
Preserves semantic lineage and composability
Supports internal marketplaces of specialized DSLs
Avoids cross-tenant conflict while keeping inheritance traceable

Forking creates isolated yet traceable variations, enabling true SaaS multi-tenancy.

🌿 Forking Semantics¶

When a DSL is forked:

What Happens	Notes
New DSL ID is assigned	Often appends tenant suffix (e.g., `-ecomx`, `-vet`)
`forkedFrom` metadata is set	Links to source DSL
Field schema is cloned	Editable independently
Confidence resets to `0.5`	Needs local trace validation
Tags may include `fork` or `tenant:<id>`	Helps indexing and filtering
Prompt and template links are retained	But version mismatch may block injection

📘 Example: Forked DSL Metadata¶

id: dsl-user-handler-ecomx-v1.0.0
forkedFrom: dsl-user-handler-v1.2.0
tenant: ecomx
tags: [user, handler, ecomx, fork]
status: draft
confidence: 0.5
differences:
  - changedField: emitsEvent → false (was true)
  - addedField: segmentId
  - removedField: retryPolicy

The fork may redefine logic for specific use cases like segmentation or auditing.

🛠️ Fork Management Lifecycle¶

Stage	Description
`forked`	New DSL forked from a base
`edited`	Custom fields added, removed, or updated
`validated`	Trace, prompt, and span integration tested
`approved`	Ready for tenant-specific generation
`mergedBack` (optional)	If fork represents global improvement
`deprecated`	No longer used for tenant or use case

🔁 Fork vs. Extend¶

Criteria	Fork	Extend
Use case	Structural change, independence	Logical layering, reuse
Inheritance	Breaks it	Maintains it
Use in prompts	May conflict	Compatible
Agent behavior	Treats as distinct lineage	Treats as overlay
DSL Index Impact	Indexed separately	Linked hierarchically

🧠 Agent Behavior with Forks¶

Agent	Behavior
`PromptPlannerAgent`	Picks fork if tenant context matches
`ValidatorAgent`	Ensures fork doesn't break downstream
`ForkTrackerAgent`	Logs divergence, visualizes drift
`GovernanceAgent`	May require approval for sensitive forks
`ConflictResolverAgent`	Warns if fork overlaps another active DSL
`FeedbackAgent`	Tracks fork usage separately for scoring

🧠 Fork-aware Prompt Injection¶

promptContext:
  tenant: ecomx
dslSearch:
  domain: user-management
  type: blueprint
  allowForks: true
  preferTenantForks: true

This ensures tenant-specific DSLs are prioritized over global ones.

🗂️ Fork Dashboard View (Planned)¶

Future tooling will allow:

🌿 Fork trees by domain, tenant, type
🔍 Drift diff visualization (e.g., field added/removed)
🧪 Fork-specific test/trace coverage
📉 Fork decay tracking (inactive forks)
📤 Merge-back recommendations

✅ Summary¶

Fork management supports:

🌍 Tenant-specific customization
🧬 Semantic divergence without fragmentation
🔁 Traceable lineage and inheritance
🧠 Agent-awareness of scope and compatibility
🧪 Domain-specific DSL lifecycle management

🧠 DSL Indexing and Embedding Infrastructure¶

🎯 Why Embed and Index DSLs?¶

To enable scalable multi-agent collaboration and real-time discovery, the DSL system must support:

🧠 Semantic similarity search
🔍 Tag- and domain-based filtering
🧩 Prompt-to-DSL matching
🔄 Fast injection and regeneration lookups

DSLs are embedded and indexed to function as semantic APIs for intelligent agents.

📦 DSL Index Types¶

Each DSL is stored in one or more indexed stores, each with a different indexing strategy:

Index Type	Description
`DSLMetadataIndex`	Fast lookup by ID, tags, type, domain, status
`DSLVectorIndex`	Vector embeddings for similarity matching
`DSLForkGraph`	Inheritance and fork lineage relationships
`DSLTraceIndex`	Usage history, test/trace results
`DSLInjectionIndex`	Prompt compatibility, conflicts, history
`DSLSpanMetricIndex`	Observability tag-to-span relationships
`DSLFeedbackIndex`	Human and agent voting logs
`DSLConfidenceModelStore`	Dynamic trust computation metadata
`DSLTemporalIndex`	Time-based version and decay tracking
`TenantScopedDSLIndex`	Isolated view per tenant for forked/overridden DSLs

🧠 Semantic Embedding Pipeline¶

Every DSL is embedded using the DSL Embedding Service, which performs:

DSL normalization → removes ID/version/comments
Field + example flattening → into structured string
Prompt-style representation → e.g., "commandName: InviteUserCommand, emitsEvent: true..."
Vector embedding generation → via Azure OpenAI / custom model
Push to vector store → Pinecone, Qdrant, Azure AI Search, etc.

The result is stored in DSLVectorIndex.

🧠 Example Embedding Input¶

type: blueprint, domain: user-management
Fields:
- commandName: string
- emitsEvent: bool
Examples:
- commandName: InviteUserCommand, emitsEvent: true

Becomes an embedding vector linked to:

embeddingId: vec-dsl-user-handler-v1.2
vectorStore: AzureCognitiveSearch
semanticTags: ["invite", "user", "command", "event"]

🔍 Semantic Search Example¶

When an agent receives a task:

“Create a handler that invites a user via email and emits an event”

It performs:

Embed query → vector
Search DSLVectorIndex for cosine similarity
Return ranked DSL matches
Filter by:
- minConfidence
- tenantContext
- status != deprecated

📁 Tag-Indexing (MetadataIndex)¶

All DSLs are also searchable by:

type, domain, layer, tags, status, tenant, extends, forkedFrom, etc.

Used in filtering, dashboards, governance tools.

Example query:

dslIndex.FindAll(d => 
  d.domain == "auth" &&
  d.type == "span" &&
  d.status == "active" &&
  d.tags.Contains("login"));

🔁 Real-Time Index Updates¶

When a DSL is:

Created → new embedding, indexing
Forked → cloned metadata and index pointers
Updated → version re-embedded
Used → trace record updates signal success/failure
Reviewed → tags like approved, sensitive applied
Deprecated → removed from injection pool

All updates are propagated via event-driven triggers into the appropriate indexes.

🛠️ Multi-Store Architecture (Pluggable)¶

Indexes are decoupled and can use different backends:

Index	Backing Store
Metadata	Azure Table / CosmosDB / PostgreSQL
Embedding	Pinecone / Qdrant / Azure AI Search
Fork Graph	Neo4j / GremlinDB / GraphQL
Trace Usage	ElasticSearch / Redis / SQL
Confidence Store	In-memory + Redis + Blob for snapshots

✅ Summary¶

The DSL Indexing and Embedding Infrastructure enables:

🔎 Fast semantic discovery
🧠 Cross-agent knowledge reuse
🤖 Prompt-to-DSL alignment
📊 DSL health and usage tracking
🔄 Real-time updates and trace-linked learning

🧩 DSL-Prompt Inference and Semantic Matching¶

🎯 Why Prompt-DSL Inference Matters¶

Agents often receive freeform instructions:

"Create a handler that invites a user and emits an event."

To ground this in structured generation, they must:

🧠 Match this intent to an existing DSL
📦 Inject the correct schema into prompt planning
📊 Use DSL metadata to constrain, enrich, and validate generation
🔍 Avoid ambiguity or semantic collisions

Prompt-DSL inference transforms natural language into actionable structured generation.

🧠 Inference Flow¶

flowchart TD
  A[Agent Prompt or Task Input] --> B[Embed Prompt Intent]
  B --> C[Query DSL Vector Index]
  C --> D[Filter by Tags + Domain + Tenant]
  D --> E[Select Top-N DSL Candidates]
  E --> F[Prompt Injection Planner]
  F --> G[Selected DSL Injected into Prompt Template]

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

📘 Example Prompt → DSL Flow¶

Prompt Input:

Create a domain handler for onboarding new users, emit an event when ready.

Matched DSL:

id: dsl-user-handler-v1.2
domain: user-management
type: blueprint
fields:
  - commandName: InviteUserCommand
  - emitsEvent: true
  - eventType: UserOnboarded

Injected into prompt like:

Prompt:
You are generating a domain handler. 
Use this DSL:
{
  commandName: "InviteUserCommand",
  emitsEvent: true,
  eventType: "UserOnboarded"
}

📦 Prompt-to-DSL Matching Criteria¶

Dimension	Strategy
Semantic similarity	Vector cosine similarity from embedded prompt
Metadata filters	DSL domain/type/tags/layer
Prompt hints	`requiresDSL`, `preferTags`, `tenantContext`
Trace reuse	Prior successful traces with same intent
DSL confidence	Score > min threshold (default 0.7)
Tenant scope	Prefer fork if available for tenant

🧠 DSL-Aware Prompt Templates¶

Templates are DSL-bound:

promptTemplateId: prompt-domain-handler
requiresDSL:
  domain: user-management
  type: blueprint
  enforceFields:
    - emitsEvent
    - commandName
  preferConfidence: 0.85+

Ensures only high-trust DSLs are injected.

🔁 Runtime Injection Handling¶

Case	Result
Multiple DSL matches	Planner selects highest-score OR requests user clarification
No DSL match found	Agent may fallback to open generation (risky) or raise warning
Deprecated DSL matched	Planner blocks injection unless override specified
Tenant-specific fork	Prefer fork DSL if tenant context matches
Version mismatch	DSL updated automatically or planner flags injection version

📊 DSL Selection Debug Trace (Logged)¶

promptId: prompt-notify-user
queryVectorMatch:
  - dsl-notify-user-v1.1.0 (0.92)
  - dsl-email-dispatch (0.89)
  - dsl-user-message (0.73)
filters:
  domain: notifications
  status: active
  tenant: shared
selectedDSL: dsl-notify-user-v1.1.0
reason: Highest semantic match + active + confidence > 0.9

🧠 PromptPlannerAgent Skills Used¶

Skill	Description
`FindClosestDSLMatch`	Vector + metadata + confidence search
`EvaluateInjectionCompatibility`	Validates if DSL fits prompt slots
`ExplainInjectionDecision`	Logs injection rationale
`RequestClarificationPrompt`	Used if multiple DSLs conflict
`FallbackToOpenPrompt`	Used as last resort (guarded)

✅ Summary¶

Prompt-to-DSL inference enables:

🧠 Structured generation from freeform prompts
🔍 Semantic precision in matching DSLs
📦 Reliable injection of trusted schemas
🔁 Traceable alignment across planning, code, test, span
🤖 Autonomous agent behavior grounded in DSL knowledge

🧩 DSL-Aware Prompt Templates and Tokens¶

🎯 Why Use DSL-Aware Prompt Templates?¶

Prompt templates act as semantic bridges between agents and generation outputs. When DSLs are injected:

🧠 Prompts become structured, context-aware, and reproducible
🧩 DSL fields are mapped to placeholders or tokens in the template
📦 Generation is constrained and aligned to architectural contracts
🔍 Validation and trace replay become deterministic

DSL-aware prompt templates ensure every agent response remains tightly aligned with factory intent.

📦 Prompt Template Metadata¶

id: prompt-domain-handler
boundDSL:
  domain: user-management
  type: blueprint
requiresFields:
  - commandName
  - emitsEvent
tokens:
  - {{CommandName}}
  - {{EmitsEvent}}
  - {{HandlerLogic}}
outputType: csharp-handler
version: v1.3
confidenceBoost: +0.05

This metadata defines:

Which DSLs are allowed
Which tokens must be filled
Which DSL fields are mapped to which tokens

🧩 Token Mapping¶

DSL fields are dynamically mapped to tokens:

fieldToTokenMap:
  commandName: "{{CommandName}}"
  emitsEvent: "{{EmitsEvent}}"
  retryPolicy: "{{RetryLogic}}"

Prompt rendering example:

Generate a C# domain handler for {{CommandName}}.
It should emit an event: {{EmitsEvent}}.
Use this retry logic: {{RetryLogic}}.

🧠 Types of Tokens¶

Token Type	Description
`FieldToken`	Injects value of DSL field
`ExampleToken`	Uses example value or test case
`SchemaToken`	Injects full DSL field schema
`SpanToken`	Injects trace/span hints
`FeedbackToken`	Injects prior user feedback as context
`ConstraintToken`	DSL validation rules or limits

Each token can be static, dynamic, or runtime evaluated.

🤖 DSL-Aware PromptPlanner Behavior¶

Selects matching prompt template by DSL type/domain
Maps DSL fields to tokens
Validates all requiresFields are present
Fills tokens in template
Injects structured input into Semantic Kernel planner

🔍 Prompt Selection Decision Log¶

selectedPromptTemplate: prompt-notify-handler
boundDSL: dsl-notify-user-v1.1.0
missingTokens: []
confidenceBoost: +0.05
outputType: csharp-command-handler
templateVersion: v1.3

All prompt selection decisions are auditable and linked to trace metadata.

🔁 Dynamic Prompt Composition (Advanced)¶

Prompts may also be composed from:

DSL metadata (title, description, tags)
Prior generation logs
Related DSLs via includes or extends

Example:

You are generating a handler using the DSL: {{DSLTitle}}.
Tags: {{Tags}}
Inherited logic: {{ExtendedFieldDescriptions}}

✅ Summary¶

DSL-aware prompt templates:

Ensure precise alignment between DSLs and generation
Use token mapping to expose DSL semantics to LLMs
Are validated, versioned, and traceable
Support multiple output types (code, test, span, config, docs)
Drive reliable and controlled code generation

🧪 DSL-Driven Code Generation, Test Scaffolding, and Span Injection¶

🎯 How DSLs Drive Full-Stack Artifact Generation¶

In the ConnectSoft AI Software Factory, a well-defined DSL can generate:

✅ Source code (e.g., handlers, services, controllers)
✅ Unit and BDD-style tests (e.g., MSTest, SpecFlow)
✅ Observability spans and metrics (OpenTelemetry)
✅ Documentation fragments
✅ Validation rules and UI schemas

DSLs act as semantic generators, encoding not only structure but full intent coverage across factory layers.

🔧 1. Code Generation from DSL¶

Example DSL:

id: dsl-user-handler-v1.2
fields:
  commandName: InviteUserCommand
  emitsEvent: true
  eventType: UserInvited

Output handler:

public class InviteUserHandler : ICommandHandler<InviteUserCommand>
{
    public async Task Handle(InviteUserCommand command)
    {
        // business logic here

        _eventBus.Publish(new UserInvitedEvent { UserId = command.UserId });
    }
}

🧠 Generated using: prompt-domain-handler with dsl-user-handler-v1.2

🧪 2. Test Scaffolding via DSL¶

From the same DSL:

Scenario: Invite user successfully
  Given a valid InviteUserCommand
  When the handler is invoked
  Then a UserInvited event should be published

➡️ Used by: TestAutomationAgent, SpecFlowPlannerAgent

🔍 3. Span Injection and Tracing¶

DSL span definition:

span:
  name: user.handler.invite
  tags:
    - commandName
    - userId
    - correlationId
  emitOn:
    - success
    - failure

Agent generates:

{
  "span": "user.handler.invite",
  "tags": {
    "commandName": "InviteUserCommand",
    "userId": "1234",
    "correlationId": "abc-xyz"
  },
  "result": "success"
}

➡️ Injected into observability templates via TelemetrySpanAgent

📄 4. Documentation and DSL Snapshots¶

DSL fields are used to auto-generate:

Markdown summary of command handlers
Code annotation blocks
DSL tables in API documentation
Links to examples and traces

Example:

## DSL: Invite User Handler

| Field | Type | Description |
|-------|------|-------------|
| commandName | string | Name of the command |
| emitsEvent | boolean | Whether an event is emitted |
| eventType | string | Name of emitted event |

🧩 5. Validation from DSL Rules¶

DSL can include rules:

rules:
  commandName must match pattern: ^[A-Z][a-zA-Z]+Command$
  emitsEvent is required
  eventType is required if emitsEvent is true

➡️ Injected into validation pipelines and code linting

🔁 Trace Feedback Loop¶

When the DSL-generated code runs in production:

✅ Spans confirm successful execution
❌ Broken spans feed into FeedbackAgent
🔁 DSL confidence is adjusted
📦 Future generations adapt or fork

🧠 DSL → Artifact Mapping Summary¶

DSL Type	Output Artifact	Agent
`blueprint`	Code, handler, controller	`CodeGenAgent`
`span`	OpenTelemetry config	`ObservabilityAgent`
`test`	SpecFlow, MSTest file	`TestAutomationAgent`
`prompt`	AI input schema	`PromptPlannerAgent`
`ui-form`	Blazor/Angular form	`UIDesignerAgent`
`doc`	Markdown/Swagger notes	`DocWriterAgent`

✅ Summary¶

DSLs power end-to-end generation:

🎯 DSL → Prompt → Template → Artifact → Trace
✅ Test, trace, doc, and code all stem from the same semantic root
🔁 Feedback loops keep DSLs accurate and evolving
📊 Confidence and quality are measurable and traceable

⚠️ DSL Conflict Detection and Semantic Collision Resolution¶

🎯 Why Detect Conflicts?¶

As the factory scales with:

🧩 Multiple agents contributing DSLs
🧠 Forks and tenant overrides
🔁 Inherited templates and prompts
🤖 Real-time generation across domains

It becomes inevitable that DSLs:

❌ Overlap semantically
🧨 Contradict in structure or purpose
🧪 Interfere during injection or generation

Unresolved DSL collisions degrade generation quality, trace accuracy, and test coverage.

🔍 Types of Conflicts¶

Conflict Type	Example	Severity
🪞 ID Conflict	Two DSLs with same `id` but different structure	High
📚 Field Overlap	Two DSLs defining `commandName` with incompatible types	Medium
🧠 Semantic Collision	Two DSLs modeling the same concept with different names	Medium
🧩 Injection Ambiguity	Multiple DSLs matching the same prompt intent	High
🔄 Fork Drift	Forked DSL diverging too far from original without change tracking	Medium
🚧 Cross-layer Conflict	Frontend DSL and backend DSL generate incompatible types	High

🛠️ Conflict Detection Engine¶

The ConflictResolverAgent detects:

Exact ID collision (same id across tenants)
Overlapping tags + domain + type
Prompt mapping ambiguity (same prompt, multiple DSLs match > 0.9)
Structure similarity with incompatible outputs
Forks with critical field mutation
Span tags overlapping with conflicting observability rules

Logged with:

conflictId: conflict-dsl-user-handler-dup
detectedBy: ConflictResolverAgent
type: id-structure-mismatch
dslIds:
  - dsl-user-handler-v1.2
  - dsl-user-handler-vet-v1.0
impact: high
resolution: manual review required

⚖️ Collision Resolution Strategies¶

Strategy	Description
🏷️ Rename + Fork	Create tenant-specific variant
🧩 Merge	Reconcile fields and collapse into one DSL
🛡️ Suppress	Deactivate conflicting DSL temporarily
🔍 Isolate by Tag	Use disjoint tags/domains to separate matching logic
🧠 Add Prompt Constraint	Prevent conflicting injection by refining `requiresDSL` filters
🔁 Drift Tracker	Alert if fork deviates > N% from base

🧠 Auto-Triage Rules¶

conflictPolicies:
  - if: id-conflict
    then: block injection + notify governance
  - if: prompt-injection-ambiguity and confidence < 0.7
    then: deprioritize + log
  - if: fork diverges > 30%
    then: trigger ForkDriftReview

📊 Conflict Heatmap & Dashboard (Planned)¶

🔥 Show domains with highest collision rate
⚖️ Breakdown by DSL type (e.g., blueprint, span)
🔄 Conflict over time chart
📍 Trace failures due to DSL mismatch

🤖 Agents That Use Conflict Signals¶

Agent	Usage
`PromptPlannerAgent`	Avoids ambiguous DSLs
`GovernanceAgent`	Flags conflicting DSLs for review
`ForkTrackerAgent`	Triggers drift alerts
`TraceValidatorAgent`	Detects runtime DSL mismatches
`KnowledgeGraphAgent`	Annotates graph nodes as conflicting or deprecated

✅ Summary¶

Conflict detection:

🧠 Preserves semantic clarity and generation precision
🔍 Prevents trace failures and prompt ambiguity
🧩 Enables graceful fork evolution
📊 Supports governance and review workflows
🛠️ Resolves inconsistencies early in the generation chain

🧷 DSL-Enabled Governance, Freezing, Deprecation, and Audit Trails¶

🎯 Why Governance Matters for DSLs¶

As DSLs encode the semantic contracts of the AI Software Factory, governing their lifecycle ensures:

🛡️ Security and compliance
🔄 Controlled evolution
❌ Safe deprecation without regressions
📜 Traceable audit logs for every change
⚖️ Multi-tenant integrity and separation

DSL governance is the policy backbone of semantic generation.

📜 Governance Controls¶

Control	Description
`frozen`	DSL is locked from further edits unless explicitly unfrozen
`deprecated`	Marked for no further use, replaced by successor
`requiresApproval`	Only usable after manual or agent review
`tenanted`	DSL is scoped to a specific tenant, isolated
`immutableFields`	Prevent structural changes to protected fields
`reviewChecklist`	DSL cannot be activated until checklist is complete

🧊 Freezing DSLs¶

status: active
frozen: true
frozenBy: GovernanceAgent
reason: Used in 47 prompt templates and 123 trace runs

No updates allowed unless unfrozen
Protects critical DSLs in production
Can be time-based or usage-based

⚰️ Deprecating DSLs¶

status: deprecated
replacedBy: dsl-user-handler-v2.0
reason: New version aligns with refactored command model

Agents using deprecated DSLs:

⚠️ Receive warning
🔁 Are guided to replacement
📦 PromptPlanner falls back to replacedBy automatically

🧾 Audit Trail Structure¶

Each DSL has a full changelog:

audit:
  createdAt: 2025-04-12
  createdBy: VisionArchitectAgent
  changes:
    - at: 2025-05-01
      by: DSLValidatorAgent
      change: "Added field: retryPolicy"
    - at: 2025-05-08
      by: GovernanceAgent
      change: "Frozen due to production usage"

Stored in DSLChangeLogIndex, queryable by time, field, agent, or status.

🛡️ Enforcement Agents¶

Agent	Governance Role
`GovernanceAgent`	Freezing, deprecation, approvals
`AuditLogAgent`	Tracks lifecycle and publishes change events
`PolicyEngineAgent`	Evaluates DSL policies per tenant
`InjectionGuardAgent`	Prevents prompt injection of blocked DSLs
`ReviewChecklistAgent`	Enforces completion of human/agent validation before status change

✅ Governance Policy Example¶

dslPolicies:
  - type: span
    domain: security
    frozenIf: usedInTrace > 50 AND confidence > 0.9
  - type: blueprint
    requireHumanApproval: true
    immutableFields: [commandName, emitsEvent]
  - tenant: all
    enforceReviewChecklist: true

🔄 Freeze/Unfreeze Triggers¶

Trigger	Result
Used in > 50 successful traces	Auto-freeze
Conflict with newer DSL	Freeze old one, activate new
Tenant override detected	Freeze global, fork DSL
Policy update	Apply rules retroactively via audit run

🔍 Visual Governance Tools (Planned)¶

Status view: draft → reviewed → active → frozen → deprecated
DSL lifecycle timelines
Who approved or rejected which DSL
DSL family trees with freeze/deprecate annotations
Policy violation alerts

✅ Summary¶

Governance and audit tracking empower the platform to:

🔒 Lock trusted DSLs in place
🧯 Safely phase out legacy models
📜 Maintain full audit history
🧠 Enforce lifecycle and usage policy
🔍 Enable security, observability, and regulatory compliance

🌐 DSL Federation Across Projects and Agent Clusters¶

🎯 Why Federation Is Necessary¶

ConnectSoft’s AI Software Factory operates across:

💼 Multiple customer domains (e.g., HR, Healthcare, eCommerce)
🏗️ Multiple projects (microservices, libraries, UIs)
🤖 Multiple agent clusters (architecture, testing, observability, UX)

To support scalability, DSLs must:

Be discoverable, composable, and scoped
Allow reuse across agents and templates
Enable tenant-specific specialization without duplication
Maintain semantic coherence across clusters

DSL federation enables controlled sharing, versioning, and cross-domain interoperability.

🧩 Federation Principles¶

Principle	Description
📦 Shared Core	Foundational DSLs (e.g., `command-handler`, `span`) live in the `shared/core` namespace
🌱 Scoped Forks	Each project or tenant forks DSLs as needed, preserving lineage
🧠 Cross-Cluster Compatibility	DSLs contain type tags, version metadata for validation across agent clusters
🔁 Lifecycle Synchronization	DSL updates propagate via trace usage and approval events
🧭 Knowledge-Aware Routing	Agents query DSLs based on scoped federation rules

🗂️ DSL Namespace Convention¶

dsl/
  shared/core/blueprints/
  shared/core/spans/
  tenants/vetsoft/blueprints/
  tenants/hrvault/forms/
  domains/ecommerce/prompts/

This structure enables:

👁️ Clear visibility of DSL ownership and reuse
✅ Traceable governance paths
📚 Multi-layer agent lookup

📘 Example: Federated DSL Query¶

agentContext:
  project: vetsoft-user-service
  tenant: vetsoft
  agentCluster: backend
dslQuery:
  type: blueprint
  domain: user-management
  preferTenantDSL: true
  fallbackToShared: true

→ Searches in order:

tenants/vetsoft/blueprints/
shared/core/blueprints/

🧠 Agent Federation Capabilities¶

Agent	Federation Behavior
`PromptPlannerAgent`	Searches across federated DSL layers based on context
`ForkTrackerAgent`	Maintains lineage between shared and scoped DSLs
`GovernanceAgent`	Enforces scope-based policies
`TraceValidatorAgent`	Resolves DSL usage by project scope
`KnowledgeGraphAgent`	Links DSLs to their federated variants in graph view

🧠 DSL Federation Use Cases¶

Scenario	Federation Pattern
Shared login flow	`shared/core/blueprints/dsl-login-handler`
Custom marketing span	`tenants/commerce360/spans/dsl-campaign-click`
Project-specific retry logic	`domains/infra/blueprints/dsl-retry-policy`
Prompt-style adaptation	`shared/core/prompts/dsl-response-prompt-basic` + fork in `tenants/eduai/`

🌐 Federation Graph Node Example¶

dslId: dsl-login-handler-v1.2
scopedCopies:
  - dsl-login-handler-vetsoft-v1.2
  - dsl-login-handler-eduai-v1.1
linkedTemplates:
  - prompt-login-handler
usedInProjects:
  - vetsoft-login-api
  - hrvault-identity
confidenceAcrossScopes:
  shared: 0.92
  vetsoft: 0.88
  eduai: 0.75

✅ Summary¶

DSL federation enables:

🧩 Scalable semantic reuse
🧠 Multi-agent, multi-tenant generation alignment
🔁 Version-safe propagation
🧭 Project + cluster scoped DSL lookups
📊 Unified governance with scoped enforcement