🧠 Ubiquitous Language Agent Specification¶

🎯 Purpose¶

The Ubiquitous Language Agent is a core modeling assistant in the ConnectSoft AI Software Factory. Its primary purpose is to:

📖 Extract, define, and formalize the Ubiquitous Language across all business and technical domains, ensuring shared understanding and semantic consistency between stakeholders, agents, and generated software.

It acts as the semantic anchor for all downstream architectural, domain, and engineering agents by producing precise, contextual, and traceable term definitions aligned with DDD principles.

📋 Responsibilities¶

The Ubiquitous Language Agent fulfills the following responsibilities in the factory lifecycle:

Responsibility	Description
🧾 Extract Domain Terms	Analyze user prompts, business vision, and use case blueprints to identify candidate terms.
📘 Define Ubiquitous Language	Write formal definitions, relationships, and roles of terms, rooted in DDD and domain understanding.
🔗 Map to Bounded Contexts	Classify terms by their context: core, supporting, generic subdomains.
🧩 Generate Glossary Files	Output glossary artifacts in reusable formats: `glossary.yaml`, `terms.json`, markdown sections.
🧠 Provide Shared Context	Feed consistent terminology to Domain Modeler, Architect, and Engineering agents.
🔄 Detect Ambiguities	Flag overlapping, conflicting, or unclear terms for review or clarification.
🧪 Validate Term Usage	Ensure defined terms are used consistently across user stories, APIs, and entity models.
💬 Support Conversational Queries	Answer clarification requests about term meaning, scope, and example usage.

🧭 Strategic Role in the Factory¶

flowchart LR
    VisionAgent[Vision Architect Agent] --> ULA[Ubiquitous Language Agent]
    ULA --> DomainModeler[Domain Modeler Agent]
    ULA --> BlueprintGen[Architecture Blueprint Generator]
    ULA --> DocsAgent[Documentation Agent]
    DomainModeler --> MicroserviceGen[Microservice Generator Agent]

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🧠 The ULA anchors the semantic model of the product by acting as a first-pass interpreter between business language and technical models.

It ensures the entire agentic ecosystem speaks the same language, reducing misalignment and improving generative output quality across every phase.

🔁 Example Use Cases¶

Scenario	ULA Action
✅ New Product Vision	Extracts glossary from business narrative
🔁 Updated Domain Blueprint	Re-analyzes and adds missing or evolved terms
💡 Developer Query	Explains term meaning and how it maps to aggregates or ports
🔍 QA Verification	Ensures all entity properties and messages use correct terms
📦 Glossary Sync	Pushes terms to Engineering Agents and Documentation layers

📥 Inputs¶

The Ubiquitous Language Agent consumes structured and unstructured sources across multiple stages of the ConnectSoft AI Software Factory. These inputs form the semantic context from which domain terms are discovered and formalized.

Input Source	Format	Description
📝 Product Vision Prompt	Natural language (Markdown, plain text)	High-level product descriptions, goals, and business context.
📦 Feature Blueprints	JSON, YAML	Functional and non-functional feature specifications with user stories.
📄 DomainModel.yaml	YAML	Predefined aggregates, entities, and domain event structures.
🧠 Term Feedback Records	JSON	Previously rejected, refined, or overridden term definitions from human reviewers.
📚 Industry Lexicon Plugins (optional)	SK plugins, JSON	Injected domain-specific terminology (e.g., Healthcare, Insurance, Education).
💬 Conversation Logs	Structured message history	Captured Q\&A or clarification threads with users or agents.

📌 All inputs are tagged with trace_id, source, and context_stage to ensure alignment and traceability throughout the lifecycle.

📤 Outputs¶

The ULA generates semantically structured and machine-consumable artifacts that feed the entire agent pipeline.

Output Artifact	Format	Purpose
📘 `glossary.yaml`	YAML	Canonical glossary of terms, definitions, synonyms, contexts.
📄 `terms.json`	JSON	Normalized and structured term catalog with metadata (origin, confidence, usage count).
🧠 `SharedMemory.TermMap`	In-memory structure	Shared semantic memory injected into DomainModeler, Blueprint Agents, and API Designers.
📜 `Glossary.md`	Markdown	Human-readable glossary for documentation and onboarding.
📬 `TermEvents`	Event stream (SK, MCP, EventBus)	TermCreated, TermUpdated, TermDeprecated events for downstream updates.
🔁 `UbiquitousLanguageContext`	Object	Injected context for prompt chaining into other agents (Architect, QA, Engineer).

🔄 Output Example: `glossary.yaml`¶

- term: Order
  definition: A customer request that includes one or more line items to be fulfilled by the system.
  type: AggregateRoot
  synonyms: [PurchaseRequest]
  boundedContext: Sales
  usageFrequency: High
  lastValidated: 2025-05-01

- term: LineItem
  definition: A single unit of order representing a specific product and quantity.
  type: Entity
  boundedContext: Sales

🔌 Data Flow Context¶

graph TD
  FeatureSpec[📦 Feature Blueprint] --> ULA[🧠 Ubiquitous Language Agent]
  VisionPrompt[📝 Product Vision] --> ULA
  DomainModel[📄 DomainModel.yaml] --> ULA
  ULA --> GlossaryYAML[📘 glossary.yaml]
  ULA --> TermsJSON[📄 terms.json]
  ULA --> UbiquitousMemory[🧠 Shared Memory]
  ULA --> DomainModeler
  ULA --> DocsAgent

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📦 Output Consumers¶

Agent / Component	Consumes Output
Domain Modeler Agent	`glossary.yaml`, `SharedMemory.TermMap`
Architecture Blueprint Generator	`terms.json`, `UbiquitousLanguageContext`
Engineering Agents	For naming ports, adapters, and DTOs
QA/Test Agents	To verify term alignment in test plans
Documentation Agent	`Glossary.md` for human documentation sites

📚 Knowledge Base¶

The ULA is initialized with a multi-source knowledge base to ensure precise extraction and consistent usage of terms across contexts. This includes:

🧱 Built-In Semantic Models¶

Source	Description
📘 DDD Semantic Ontology	Pre-trained mapping of Domain-Driven Design concepts (Aggregate, Entity, Bounded Context, etc.).
🧩 Software Factory Term Atlas	A curated map of 5,000+ common terms used across SaaS domains, stored in vector DB.
🏷️ Contextual Pattern Rules	NLP patterns and heuristics to identify meaningful nouns, verbs, value objects.
📖 Industry Lexicons (optional)	Domain-specific extensions (e.g., Medical, Insurance, Finance) loaded via SK Plugins or API.

These static resources form the ULA’s foundational reasoning layer and are used during both training and real-time generation.

🧬 Memory Architecture¶

ULA utilizes both short-term and long-term memory to ensure traceability, correction, and continuity across multi-cycle factory flows.

🧠 Short-Term Memory¶

Layer	Purpose
🔄 Prompt Context	Holds input prompt(s), current glossary state, and last few validation rounds.
🧪 Session Trace	Stores `trace_id`, feature ID, and upstream agent references.
📍 Token-Scoped Term Cache	Keeps terms extracted in current execution window for deduplication.

Short-term memory is volatile and optimized for fast token-limited processing via Semantic Kernel.

🧠 Long-Term Memory (Persistent)¶

Store	Description
🗂️ Vector DB (e.g., Azure Cognitive Search / Pinecone)	Embedding-based semantic memory for glossary definitions, synonyms, and usage context.
📦 File Storage (glossary.yaml, terms.json)	Versioned outputs stored in Git-backed content repo or blob storage.
🔁 MCP Memory Server (optional)	External agent-accessible memory node for term sharing and lookup across agents.
📊 Telemetry Store	Historical metrics about term creation, frequency, validation events.

🧠 Memory Retrieval Strategy¶

flowchart LR
    PromptInput[🔁 Prompt Inputs] --> ContextCache[🔄 Term Cache]
    VectorDB[🗂️ Vector DB] --> TermRecall[Term Retrieval Engine]
    TermRecall --> ContextCache
    ContextCache --> TermValidator
    TermValidator --> OutputArtifacts

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📌 Example Term Recall Logic¶

Given the user prompt: “Customers can create quotes and convert them into orders”

Extracts candidate terms: Customer, Quote, Order
Vector DB is queried:
- Finds existing Order term → reuses with confidence boost
- Quote not found → triggers definition generation
Term definitions are assembled and cross-validated via rules

🔗 Versioned Glossary Memory¶

The ULA assigns version_id and source_stage metadata to every term.

Field	Example
`term`	`Quote`
`version_id`	`glossary.v2.3.1`
`source_stage`	`VisionPrompt + FeatureBlueprint`
`trace_id`	`fea-9842-term-quote`

🧠 This supports full-term traceability and enables glossary diffing, rollback, and auditing.

📌 Summary¶

The ULA’s memory design ensures:

🧠 Recall of known terms and prevention of duplicates
🪢 Semantic linking across aggregate roots, entities, and bounded contexts
🔁 Continuity of definitions over multi-agent, multi-cycle flows
🔍 Auditability and governance of glossary evolution

🧬 Process Flow¶

The ULA follows a deterministic, multi-stage pipeline to transform raw business prompts and specifications into a canonical, context-aware Ubiquitous Language Glossary.

This flow ensures repeatability, traceability, and semantic accuracy, integrating both LLM-based inference and rule-based postprocessing.

🧭 Agent Flow Diagram¶

flowchart TD
    A[📥 Inputs: Prompts, Blueprints, DomainModel.yaml] --> B[🧠 Term Extraction Stage]
    B --> C[🔍 Semantic Categorization]
    C --> D[📘 Glossary Generation]
    D --> E[🧪 Validation & Deduplication]
    E --> F[📤 Output Emission - YAML, JSON, Memory]
    F --> G[📬 Event Dispatch - TermCreated/Updated]

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🧩 Step-by-Step Process¶

Step	Description
1. Input Parsing	Normalize raw prompts and structured inputs (feature blueprints, vision, models).
2. Term Extraction	Apply NLP and SK skills to identify candidate terms and relationships.
3. Semantic Categorization	Assign term roles: `Aggregate`, `Entity`, `ValueObject`, `Process`, `Policy`, `Actor`, `ExternalSystem`.
4. Contextual Enrichment	Enrich terms using vector memory, domain patterns, and bounded context classification.
5. Glossary Synthesis	Generate term entries in structured format (YAML + JSON + Markdown view).
6. Deduplication & Conflict Detection	Detect existing definitions or similar terms, apply merge rules or escalate.
7. Validation Loop	Check definition format, naming conventions, and referential integrity (e.g., used in DomainModel).
8. Output Emission	Write `glossary.yaml`, `terms.json`, and update semantic kernel memory.
9. Event Dispatching	Emit `TermCreated`, `TermUpdated`, `TermRejected` for reactive consumption by other agents.

🧠 Term Extraction Logic¶

Sample prompt: “A Customer can submit an Application containing multiple Animals to register at a Clinic.”

Identifies: Customer, Application, Animal, Clinic
Categorizes:
- Customer → Actor
- Application → Aggregate Root
- Animal → Entity
- Clinic → ExternalSystem
Stores definitions, roles, synonyms, and relationships

🧪 Example Output Snippet¶

- term: Application
  definition: A registration request initiated by a Customer, containing one or more Animals, sent to a Clinic.
  role: AggregateRoot
  context: Registration
  synonyms: [PetApplication, RegistrationForm]
  trace_id: app-2025-05-04-001

🔁 Looping Behavior¶

If the output does not pass validation or receives correction signals:

❗ Triggers Term Re-generation
🔁 Applies correction rules (e.g., clarify overlapping term, split compound)
🔄 Re-runs from Glossary Synthesis → Validation

🧑‍🤝‍🧑 Human Feedback Loop (Preview)¶

Manual reviewers (or Reviewer Agent) can:
- Accept / reject terms
- Suggest better phrasing
- Force rename or redefine

🧠 This step integrates into Human Intervention Hooks

🧠 Observability Built-In¶

Each step emits:

⏱ Duration
📈 Token cost
🧾 Term count extracted
🐛 Invalid definitions
📬 Events emitted (by type)

✅ Summary¶

The ULA’s process flow is:

🔁 Repeatable
🧠 Memory-integrated
🧪 Validation-rich
📤 Multi-format aware
📬 Event-emitting and composable

🧩 Semantic Kernel Skills & Functions¶

The ULA is built as a Semantic Kernel-based orchestration agent, with modular skills enabling term extraction, validation, glossary generation, and event-driven communication.

It uses a combination of custom C# skills, prompt-based semantic functions, and pluggable tools (via SK plugins or MCP servers).

🔧 Core Semantic Kernel Skills¶

Skill Name	Type	Description
`ExtractDomainTerms`	Prompt Function	Uses prompt engineering to extract candidate domain terms from raw input.
`ClassifyTermRole`	Semantic / Native	Assigns roles like `Aggregate`, `Entity`, `ValueObject`, `Actor`, etc.
`GenerateTermDefinition`	Semantic	Creates structured definition with context, synonyms, and relationships.
`SuggestBoundedContext`	Semantic	Infers bounded context from term usage or feature alignment.
`CheckTermConflicts`	Native (C#)	Looks for naming collisions, duplicates, and similar definitions.
`FormatGlossaryYaml`	Native	Assembles YAML-compliant glossary artifact.
`EmitTermEvents`	Native / EventBus	Publishes lifecycle events (TermCreated, TermUpdated, TermRejected).
`RetrieveSimilarTerms`	Vector Memory	Queries term vector DB for existing definitions or overlap.
`InjectGlossaryMemory`	SK Planner	Feeds memory context to downstream agents (e.g., Domain Modeler).

🧬 Orchestration Flow (Internal Skill Chain)¶

flowchart TD
    A[ExtractDomainTerms] --> B[ClassifyTermRole]
    B --> C[GenerateTermDefinition]
    C --> D[RetrieveSimilarTerms]
    D --> E[CheckTermConflicts]
    E --> F[SuggestBoundedContext]
    F --> G[FormatGlossaryYaml]
    G --> H[EmitTermEvents]

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🧱 Modular SK Planner Functions¶

The ULA optionally supports planner-based chaining to adapt behavior based on:

Term complexity
Industry-specific lexicons
Conflict detection
Feedback loop status

✅ Enables dynamic pipeline variation without code change.

🔌 Plugin Integrations (Optional)¶

Plugin	Use
OpenAPI Plugin	Can call 3^rd-party glossaries or ontology APIs for industry-specific terms.
Web Plugin	Searches domain-specific sources or Wikipedia for enrichment.
Document Plugin	Parses PDFs or text files for term candidates.
Memory Plugin	Loads and stores glossary memory from Azure Cognitive Search / Pinecone.
Graph Plugin	(Future) To build term relationship graphs between aggregates and subdomains.

🧠 Function Configuration¶

Each skill has:

Retry strategy (max attempts, fallback mode)
Observability tags (trace_id, term_id, confidence score)
Toolchain signature (agent_id, skill_name, version, model_id)

🧪 Example Semantic Function: `GenerateTermDefinition`¶

Prompt Template:

"Given the domain term: {{term}}, write a one-sentence definition suitable for inclusion in a DDD glossary.
Include synonyms and the term’s bounded context if available."

Input: term = "Quote"
Output: 
- definition: "A preliminary pricing request made by a customer before placing an order."
- synonyms: ["Estimate", "Pre-Order"]
- boundedContext: Sales

✅ Summary¶

The ULA’s SK skillset ensures:

🔧 Composability across pipelines
🔄 Chained reasoning with planners
🧠 Integration with memory & validation tools
📦 Output in production-ready artifacts

🧠 Core Technologies¶

The ULA is implemented using a modular, AI-native, cloud-ready architecture, combining ConnectSoft platform standards with modern LLM orchestration tools.

Technology	Role
🧠 Semantic Kernel (C#)	Primary orchestration engine for skill chaining, memory access, and prompt templates
🤖 OpenAI / Azure OpenAI	Foundation model provider for term extraction, definition synthesis, and categorization
🔍 Azure Cognitive Search / Pinecone	Vector memory backend for semantic glossary lookup and retrieval
🧬 LangChain Plugins / SK Connectors	Access external ontologies, domain glossaries, and document stores
🧑‍💻 .NET Core 8	Hosting environment for runtime agents and skill logic
📂 Azure Blob Storage / GitHub Repo	Persistent artifact storage (`glossary.yaml`, `terms.json`, Markdown views)
📡 Event Bus (Azure Service Bus / Kafka)	Emit `TermCreated`, `TermUpdated`, and sync events to downstream agents
🌐 MCP Servers (Model Context Protocol)	Enable shared glossary memory and cross-agent glossary injection
📜 MkDocs (Material)	Documentation generator for human-readable glossary views

🔌 Integration with ConnectSoft Stack¶

Component	Integration Point
✅ Vision Architect Agent	Receives extracted terms from prompt analysis stage
✅ Domain Modeler Agent	Consumes canonical term list for building aggregates and entities
✅ Blueprint Generator Agent	Aligns DDD constructs and ports to glossary definitions
✅ Microservice Generator Agent	Uses terms for naming classes, folders, and services
✅ Documentation Agent	Renders Markdown glossary from `glossary.yaml`
✅ QA/Testing Agents	Validate semantic alignment between terms and test specs

🔐 Security & Identity¶

Area	Practice
API Access	Token-based access to Azure OpenAI or OpenAI APIs
Artifact Access	RBAC over Blob Storage or Git repositories
Event Publishing	Authenticated publishing to secured queues or service bus topics
Plugin Wrappers	SK plugins executed in sandboxed container context if needed

🔁 Deployment Context¶

The ULA runs as:

🧱 A microservice generated via ConnectSoft.MicroserviceTemplate with --UseSemanticKernel and --UseOpenAI enabled
🌐 Exposes a REST API for external glossary queries (GET /glossary, POST /term)
🔄 Acts as a headless agent when orchestrated by Vision or DomainModeler agents

✅ Fully agent-compatible, versioned, traceable, and deployable with CI/CD pipelines

📶 Optional Extensions¶

Extension	Description
📚 Ontology Plugin	Access standardized vocabularies like FIBO, SNOMED, HL7, OWL-based taxonomies
📖 Document Parser	Load PDFs, Excel sheets, or HTML pages as prompt input
🌐 Web Search Plugin	Fetch term definitions or synonyms dynamically from domain-specific sources
🧠 Graph Plugin	(Upcoming) Builds term relationship graphs for visualization in architecture docs

📌 Summary¶

The ULA is designed to operate as a cloud-native, AI-augmented, multi-agent service, tightly integrated with:

The ConnectSoft Agent Framework
SK/OpenAI capabilities
Cloud storage, memory, and event systems
Documentation and delivery pipelines

🧠 System Prompt (Bootstrap Instruction)¶

This is the foundational instruction injected during agent initialization. It defines the ULA's identity, expected behavior, and alignment to ConnectSoft’s Clean Architecture and DDD-first practices.

You are the Ubiquitous Language Agent (ULA) in the ConnectSoft AI Software Factory.

Your goal is to extract, define, and validate domain-specific terms from product descriptions, user stories, and domain models. These terms should reflect the Ubiquitous Language as defined in Domain-Driven Design (DDD) and must be contextually aligned with bounded contexts and aggregates.

For each term you extract, classify it as one of: Aggregate, Entity, ValueObject, DomainEvent, Actor, Policy, Process, ExternalSystem, or Concept.

Your definitions must be:
- Clear and concise (1–2 sentences)
- Use the same terminology as used in upstream prompts
- Provide synonyms if available
- Include the relevant bounded context
- Use markdown-safe formatting and YAML-serializable structure

Avoid generating overlapping terms, generic technical words, or vague labels. Ensure consistency across existing domain models and feature blueprints.

All outputs should be structured, testable, and traceable.

🧾 Input Prompt Template¶

This is the reusable semantic function prompt format used during skill execution (e.g., ExtractDomainTerms, GenerateTermDefinition, ClassifyTermRole).

📄 Template: Term Extraction¶

Given the following product or feature description:

{{input}}

Extract the list of domain terms that appear meaningful from a Domain-Driven Design (DDD) perspective.

For each term, return:
- Term name
- Type (Aggregate, Entity, etc.)
- The sentence or context it appeared in

Output in YAML list format.

📄 Template: Term Definition¶

Given the term "{{term}}" and its context: "{{context}}",

Write a one-sentence definition that is domain-relevant, specific, and aligned with Clean Architecture.

Also return:
- Synonyms (if any)
- Suggested bounded context
- Classification type

Output in this YAML format:
- term: {{term}}
  definition: "... "
  type: Aggregate | Entity | ...
  synonyms: [ ... ]
  boundedContext: ...

📄 Template: Glossary Generation (Full)¶

Using the following list of extracted terms:

{{terms}}

Generate a complete glossary in YAML format, where each term includes:
- definition
- type
- bounded context
- synonyms
- trace_id (you generate it as traceable UID)

📥 Example Input¶

"A customer can create a quote that includes multiple line items and convert it into an order."

📤 Expected Output (Excerpt)¶

- term: Quote
  definition: A pricing proposal generated by a customer that outlines intended products or services.
  type: Aggregate
  boundedContext: Sales
  synonyms: [Estimate, Pre-Order]
  trace_id: term-20250504-01

✅ Summary¶

ULA’s prompts are designed to:

Guide large language models with precision
Maintain YAML-safe, schema-aligned outputs
Embed DDD understanding and bounded context awareness
Support multi-cycle reuse and chaining across agents

📤 Output Expectations¶

ULA outputs are designed to be:

🧱 Structurally consistent for machine agents
🧠 Human-readable for business and product stakeholders
🔗 Traceable across factory execution flows
♻️ Reusable across multiple agents and factory runs

Each output includes semantic data, classification metadata, and contextual traceability.

📘 Primary Artifacts Produced¶

Artifact	Format	Description
`glossary.yaml`	YAML	Canonical DDD glossary file containing all discovered terms and their properties
`terms.json`	JSON	Normalized term records for use in web UIs, planners, and downstream APIs
`Glossary.md`	Markdown	Human-friendly glossary view, used by the Documentation Agent
`UbiquitousLanguageContext`	Object	In-memory structure injected into downstream agent executions
`TermEvents`	Event Stream	Used to notify Domain Modeler, Engineering, and Blueprint agents of glossary changes

📂 Output Format: `glossary.yaml`¶

- term: Quote
  definition: A pricing proposal generated by a customer for a set of goods or services.
  type: Aggregate
  synonyms: [Estimate, Proposal]
  boundedContext: Sales
  trace_id: term-2025-05-04-Q001
  source: FeatureBlueprint:Registration
  confidenceScore: 0.96
  lastUpdated: 2025-05-04T14:33:00Z

- term: LineItem
  definition: An individual product entry within a Quote, specifying quantity and product.
  type: Entity
  boundedContext: Sales
  synonyms: []
  trace_id: term-2025-05-04-LI01
  confidenceScore: 0.91

🧩 Output Fields¶

Field	Required	Description
`term`	✅	The domain-specific label (e.g., Quote, Application, LineItem)
`definition`	✅	One-sentence description
`type`	✅	DDD role (Aggregate, Entity, ValueObject, Actor, etc.)
`boundedContext`	✅	Logical grouping of the term
`synonyms`	⬅️ Optional	Alternative labels
`trace_id`	✅	Unique traceable identifier for linking outputs to factory flows
`confidenceScore`	✅	LLM-determined confidence (0–1)
`source`	⬅️ Optional	Originating blueprint, prompt, or agent stage
`lastUpdated`	✅	UTC timestamp for version tracking

📄 `terms.json` Sample¶

[
  {
    "term": "Order",
    "definition": "A finalized customer request for fulfillment.",
    "type": "Aggregate",
    "boundedContext": "Sales",
    "trace_id": "term-2025-05-04-O001",
    "confidenceScore": 0.95
  }
]

📜 Markdown Rendering (Glossary.md)¶

### 🧾 Quote
- **Definition**: A pricing proposal generated by a customer for a set of goods or services.
- **Type**: Aggregate  
- **Bounded Context**: Sales  
- **Synonyms**: Estimate, Proposal  

---

### 📄 LineItem
- **Definition**: An individual product entry within a Quote, specifying quantity and product.
- **Type**: Entity  
- **Bounded Context**: Sales

Generated automatically by the Documentation Agent from glossary.yaml.

🔁 Event Emission: `TermCreated`, `TermUpdated`¶

Each term lifecycle emits an event (via Service Bus or Event Grid):

{
  "eventType": "TermCreated",
  "term": "Order",
  "boundedContext": "Sales",
  "source": "VisionAgent",
  "trace_id": "term-2025-05-04-O001",
  "timestamp": "2025-05-04T14:33:00Z"
}

Enables reactive term propagation to Domain Modeler and Engineering agents.

📦 Output Consumers¶

Agent	Usage
Domain Modeler Agent	Builds aggregates/entities from terms
Blueprint Generator Agent	Aligns ports and contexts
Engineering Agents	Names DTOs, adapters, and handlers
Testing Agents	Enforces term-aligned test coverage
Documentation Agent	Renders glossary section for onboarding and product docs

✅ Summary¶

ULA’s output is:

📐 Structured (YAML, JSON, Markdown)
🔗 Traceable (trace_id, source, confidenceScore)
♻️ Reusable across agents and documentation layers
🔄 Emittable via events for pipeline continuity

✅ Validation Process¶

To guarantee that the generated Ubiquitous Language:

🧠 Aligns with Domain-Driven Design (DDD) principles
❌ Avoids ambiguity, duplication, or inconsistency
🔗 Connects accurately to upstream input and downstream usage
📦 Produces output that is consumable by other agents

🧪 Multi-Layer Validation Pipeline¶

Validation is applied at three levels:

Layer	What It Validates
1️⃣ Syntactic	YAML/JSON formatting, required fields, naming conventions
2️⃣ Semantic	DDD role correctness, context alignment, clarity of definitions
3️⃣ Contextual	Reuse consistency, cross-term references, duplication across stages

🔁 Step-by-Step Validation Flow¶

flowchart TD
    A[⬇️ Glossary Generation] --> B[📐 Format Validator]
    B --> C[🧠 Semantic Consistency Validator]
    C --> D[🔍 Term Deduplicator]
    D --> E[🔗 Cross-Agent Alignment Check]
    E --> F{✅ All Pass?}
    F -- Yes --> G[📤 Output Emission]
    F -- No --> H[🚨 Correction Path Triggered]

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🔎 Validators Defined¶

1. 📐 Format Validator¶

Ensures:
- YAML or JSON is parseable
- Fields are not missing (term, definition, type, trace_id)
- type is one of: Aggregate, Entity, ValueObject, DomainEvent, etc.

2. 🧠 Semantic Validator¶

Runs checks on:
- Definitions being concise and domain-specific
- Correct use of DDD concepts (e.g., don't assign Aggregate to Email)
- Avoids circular definitions or tautologies

3. 🔍 Deduplication Engine¶

Checks against:
- Existing glossary memory (vector similarity > 0.85)
- Same term names in different contexts
- Misspellings or case-sensitive variants

4. 🔗 Alignment Validator¶

Confirms term presence in:
- Feature blueprints
- DomainModel.yaml
- Past glossary versions

📉 Example Invalid Entries¶

Issue	Sample
❌ Missing definition	`- term: Customer`
❌ Ambiguous type	`type: Object`
❌ Duplicate with small variation	`LineItem` vs `Line Item`
❌ Overly generic term	`Data` or `Info` as a root-level term
❌ Undefined context	`boundedContext: ""`

📤 Validation Metadata¶

Each term includes a validation status block:

validation:
  passed: false
  issues:
    - "Missing boundedContext"
    - "Definition is too generic"
  validatedAt: 2025-05-04T14:40:00Z

Only passed: true terms are emitted to final artifacts or injected into other agents.

🧠 Integration with Other Agents¶

Agent	Validation Usage
Domain Modeler Agent	Refuses to build aggregates from invalid terms
Docs Agent	Skips glossary rendering of rejected entries
Reviewer Agent	Highlights flagged terms for human approval

✅ Summary¶

ULA’s validation ensures:

⚠️ Bad terms are caught early
🔍 Inconsistent or vague definitions are filtered
📊 Outputs meet Clean Architecture expectations
🔗 Terms integrate safely with DomainModeler, Engineering, QA

🔁 Retry & Correction Flow¶

The retry and correction flow ensures:

✨ Glossary completeness and clarity
🧠 Semantic precision after validation failures
🔄 Autonomous recovery without human dependency (unless escalated)
📈 High-quality inputs for downstream agents (DomainModeler, Engineering, QA)

🔁 Retry Flow Overview¶

flowchart TD
    A[⬇️ Validation Fails] --> B[🧠 Retry Triggered]
    B --> C[📥 Input Context Review]
    C --> D[🧠 Regenerate Term or Definition]
    D --> E[📄 Revalidate Glossary Block]
    E --> F{✅ Passes Now?}
    F -- Yes --> G[📤 Emit Updated Artifact]
    F -- No --> H[🧑 Human Escalation (optional)]

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

📦 Triggers for Retry¶

Scenario	Triggered Action
❌ Missing fields	Auto-fill defaults or re-query LLM
❌ Conflict with similar term	Re-define or merge candidate
❌ Weak/confusing definition	Add prompt context and re-run generation
❌ Invalid type classification	Reroute through `ClassifyTermRole` function
❌ Out-of-context term	Reject or propose alias in more relevant context

🔄 Retry Strategy¶

Component	Strategy
🔁 LLM Re-prompting	Retry with enriched prompt (adds context, examples, constraints)
🔍 Context Refinement	Pull in surrounding blueprint text or historical glossary memory
🔧 Fallback Rules	Use prebuilt rules (e.g., default to `Entity` if uncertain)
🧠 Term Merge Handler	Combine near-duplicates with confidence weighting
🧑 Reviewer Path (optional)	Escalate to human-in-the-loop for final decision

📄 Enriched Retry Prompt Example¶

Retrying definition for the term: "Submission"

Previous output was vague or too generic. Here's more context:
- Appears in: "A clinic staff member can initiate a submission for review"
- Possible meaning: A formal request sent to an external system

Try again. Define clearly and classify it within a DDD bounded context.

📍 Correction Metadata (Versioned Term Block)¶

- term: Submission
  definition: A formalized set of documents or forms submitted to an external agency for processing.
  type: DomainEvent
  boundedContext: Compliance
  trace_id: term-2025-05-04-SUB001
  corrections:
    - "Initial definition was too vague"
    - "Added context from blueprint: 'clinic staff review flow'"
  retries: 2
  validated: true

🧠 Intelligent Retry Control¶

The retry loop:

Caps attempts (maxAttempts: 3)
Logs retryReason and correctionStrategy
Emits telemetry for retry count, fail reason, and correction outcome
Escalates only when retry confidence is below threshold (e.g., < 0.7)

🔔 Human Escalation Path (If All Fails)¶

Action	Tool
📝 Add to `term_review_queue.yaml`	Reviewer Agent or human approves
🚦 Flag via `TermRejected` event	Consumed by monitoring dashboards
📬 Alert via Teams/Slack plugin	Optional integration for term moderators

✅ Summary¶

ULA's retry/correction loop:

🔄 Recovers gracefully from glossary failures
🧠 Enhances definitions using semantic and contextual reasoning
🧑 Allows human curation only when needed
📊 Emits correction metadata for audit, observability, and QA use

🤝 Collaboration Interfaces¶

To enable semantic continuity and factory-wide interoperability, the ULA exposes clean, contract-based collaboration interfaces to all upstream and downstream agents that depend on consistent domain terminology.

These interfaces are:

🧠 Memory-aware
🔄 Event-driven
🔌 API-compatible
♻️ Retryable and traceable

🔄 Primary Collaboration Targets¶

Target Agent	Interaction Role
🧠 Vision Architect Agent	Receives raw product vision → provides initial prompt inputs
🧩 Domain Modeler Agent	Consumes canonical glossary → builds aggregates, entities, events
📐 Architecture Blueprint Agent	Aligns glossary to ports, transport rules, and DDD contexts
🧰 Engineering Agents (Code Generators)	Use glossary for naming conventions, DTOs, adapter interfaces
📜 Documentation Agent	Renders glossary sections in Markdown and Docs UI
✅ QA/Testing Agents	Ensure usage of defined terms in test cases and acceptance criteria

🧬 Interface Types¶

Type	Method	Description
🧠 Shared Memory Injection	SK Kernel Context	Injects `UbiquitousLanguageContext` object into other agents' reasoning scope
📡 Event Bus Signals	Service Bus / Kafka	Emits term lifecycle events (`TermCreated`, `TermUpdated`, `TermRejected`)
🌐 HTTP API	REST / JSON	Exposes glossary endpoints: `GET /terms`, `GET /term/{name}`, `POST /feedback`
🔁 Prompt Context Injection	Prompt Block	Adds `## Known Terms` section to other agents' prompt templates
🔌 MCP Server Memory API (optional)	Plugin Adapter	Shared semantic glossary node accessible by multiple agents in distributed setups

🧠 UbiquitousLanguageContext Format (Injected Memory Object)¶

{
  "glossary": [
    {
      "term": "Quote",
      "definition": "A pricing proposal generated by a customer.",
      "type": "Aggregate",
      "boundedContext": "Sales"
    }
  ],
  "sourceAgent": "ULA",
  "traceId": "glossary-2025-05-04"
}

Used by:

🧩 Domain Modeler → builds aggregates/entities
📐 Architecture Blueprint Agent → maps ports
🧰 Engineering Agents → names adapters/DTOs

📡 Event Format (TermCreated)¶

{
  "eventType": "TermCreated",
  "term": "Quote",
  "trace_id": "term-2025-05-04-Q001",
  "agent": "ULA",
  "boundedContext": "Sales",
  "timestamp": "2025-05-04T15:02:00Z"
}

Emitted to glossary.events.topic for real-time term sync.

🌐 REST API Endpoints¶

Endpoint	Purpose
`GET /glossary`	Retrieve full glossary as JSON
`GET /term/{name}`	Lookup specific term
`POST /term-feedback`	Accept feedback or correction for a term
`GET /terms/context/{boundedContext}`	Query terms by bounded context

🤝 Cross-Agent Prompt Injection Example¶

Injected into Domain Modeler:

## Known Terms

- Quote (Aggregate): A pricing proposal by a customer.
- LineItem (Entity): An individual product line in a Quote.

Use the above as canonical definitions when generating domain models.

🧠 Sample Collaboration Sequence¶

sequenceDiagram
    participant Vision as Vision Agent
    participant ULA as Ubiquitous Language Agent
    participant DomainModeler as Domain Modeler
    participant Blueprint as Blueprint Agent
    participant Docs as Documentation Agent

    Vision->>ULA: Submit product vision
    ULA->>DomainModeler: Inject UbiquitousLanguageContext
    ULA->>Blueprint: Emit glossary.yaml
    ULA->>Docs: Send Glossary.md

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

✅ Summary¶

ULA's collaboration interfaces:

🔄 Enable agent-to-agent glossary continuity
🧠 Allow semantic memory reuse
📡 Power event-driven architecture
🌐 Expose REST APIs for observability, debugging, and sync
📦 Feed agent prompts and reasoning layers

📊 Observability Hooks¶

To ensure the ULA operates reliably and transparently at scale, all core behaviors are instrumented with:

📈 Metrics
🧵 Tracing
🪵 Logging
🧪 Validation Reporting

These observability features enable:

🚦 Agent health and uptime monitoring
🔍 Prompt-output traceability
🧠 Glossary quality reporting
🧑‍🔧 Debugging of generation or validation failures
📊 Semantic glossary usage analytics

📈 Key Metrics (Prometheus / Azure Monitor)¶

Metric Name	Type	Description
`ula_terms_generated_total`	Counter	Number of terms created by ULA
`ula_terms_validated_total`	Counter	Number of terms that passed all validation
`ula_retry_count`	Counter	Total retries triggered for invalid terms
`ula_invalid_term_ratio`	Gauge	% of terms rejected per batch
`ula_avg_glossary_size`	Gauge	Average number of terms per output cycle
`ula_vector_hits_total`	Counter	Times vector memory retrieved an existing term
`ula_trace_errors_total`	Counter	Prompt/response chain failures

🧵 Distributed Tracing (OpenTelemetry)¶

Each ULA invocation is traceable via:

📌 trace_id attached to:
- Inputs (prompt, blueprint, upstream agent)
- Output (term blocks, events)
📎 Spans for:
- Term extraction
- Definition generation
- Validation steps
- Output rendering
📡 Trace propagation across:
- Vision Agent → ULA → DomainModeler
- ULA → Event Bus → Engineering Agents

Traces are compatible with Azure Application Insights, Jaeger, and Grafana Tempo.

🪵 Structured Logging (Serilog / OpenTelemetry Logs)¶

Log Type	Fields
`TermGeneration`	`term`, `confidenceScore`, `modelId`, `source`
`ValidationFailure`	`term`, `issues[]`, `retryCount`, `trace_id`
`MemoryHit`	`term`, `similarTerm`, `similarityScore`
`EventDispatch`	`eventType`, `term`, `boundedContext`, `recipientAgent`
`CorrectionAction`	`term`, `correctionType`, `retries`, `fallbackStrategy`

All logs are emitted in JSON format and support ElasticSearch, Azure Log Analytics, or Seq backends.

📬 Event Stream Monitoring¶

Each emitted event includes:

eventId, traceId, eventType, timestamp, agent, term
Can be tracked in:
- Azure Event Grid Dashboard
- Kafka Topics + Lag Monitoring
- Grafana Alerting (failed terms, high retry count)

📊 Glossary Quality Dashboard¶

Recommended dashboard views:

📦 Glossary Size per Product
✅ Validation Pass/Fail Ratio
🔄 Retry Frequencies
⏱ Generation Time per Batch
📚 Top Bounded Contexts by Term Count
🧠 Most Frequent Terms (memory hits)

These can be visualized via Grafana, Power BI, or custom dashboards.

🛠️ Debug Mode (Developer Hook)¶

If triggered via header or API param:

GET /glossary?debug=true

Returns:

Prompt inputs
Intermediate reasoning (term selection, classification logs)
Validation report
Trace metadata
Retry/correction history

Useful for developers, reviewers, or auditor agents.

✅ Summary¶

ULA is deeply observable, with:

📈 Real-time glossary generation and quality metrics
🧵 Full traceability across prompt→term→output→event
🪵 Actionable structured logs and validation feedback
📡 Event observability across agent network
📊 Dashboards for glossary health, retries, term counts

🧑‍💼 Human Intervention Hooks¶

While ULA is designed for full autonomy, some situations require human guidance, especially when:

🤖 Ambiguity exceeds model confidence
❌ Definitions conflict with domain reality
🧠 Terminology carries strategic implications
🧑 Product Owners, Architects, or Domain Experts request oversight

ULA supports both proactive reviews (triggered by rules) and on-demand overrides (triggered by users).

🔔 Trigger Conditions for Human Review¶

Condition	Description
❌ Validation Failure After Max Retries	Term fails all retry/correction attempts
📉 Low Confidence Score	Model outputs score < threshold (default: 0.75)
⚠️ Semantic Ambiguity	Conflicting synonyms, unclear role or bounded context
🧩 Human-Tagged Terms	Terms marked `requiresReview: true` in upstream inputs
📜 Regulatory/Industry Keywords	Sensitive domains (e.g., “claim”, “treatment”, “submission”) trigger review
🧠 Feedback Loop Input	Term received negative or unclear feedback from previous agent or user

🗂️ Review Queue Artifact¶

Invalid or pending terms are written to a structured queue:

reviewQueue:
  - term: Claim
    reason: "Low model confidence + industry-regulated term"
    lastAttemptedDefinition: "A document submitted to request payment"
    trace_id: term-2025-05-04-CL001
    retries: 3
    requiresAttention: true
    suggestedAction: "Human confirmation or reword"

This can be:

📄 Written to term_review_queue.yaml
📨 Sent as a message to a Reviewer Agent
📬 Posted to Microsoft Teams / Slack (via integration plugin)

🧑‍💻 Manual Review Actions¶

Action	Result
✅ Approve	Term marked valid and emitted as usual
✍️ Edit	Reviewer updates definition, synonyms, or type
🚫 Reject	Term is excluded from glossary and event stream
🔄 Resubmit	Retriggers generation with human-supplied hint/context
📁 File for Later	Term deferred to another iteration or team

All manual actions are versioned and stored.

🧠 Reviewer UX Channels¶

Channel	Description
📜 Review Queue UI	Embedded in Docs UI or ConnectSoft Ops Portal
📬 Email/Teams Plugin	Term review digests or alerts sent to stakeholders
🧠 Reviewer Agent	(Optional) AI-assisted review agent that summarizes, suggests, and applies changes
🧾 Markdown Summary	Generated `GlossaryReview.md` section for manual inspection

📍 Trace & Override Metadata¶

Every human-impacted term is annotated:

- term: Coverage
  definition: The extent of financial protection under a policy.
  correctedBy: human
  reviewer: a.romero@connectsoft.io
  reviewReason: "Ambiguous across BoundedContexts"
  validated: true
  trace_id: term-2025-05-04-COV01
  version: 3

🧠 Human Feedback Injection into SK Memory¶

All human corrections or confirmations are embedded into memory:

{
  "term": "Coverage",
  "approvedBy": "Alex Romero",
  "confidenceOverride": 1.0,
  "reviewNotes": "Clarified for Insurance context only"
}

This feedback improves future glossary generations and reduces recurrence of errors.

✅ Summary¶

ULA supports a structured human-in-the-loop flow with:

🚦 Review triggers based on validation, ambiguity, and model scores
📜 Structured queue artifact for consistent review
🧑 UI and agent channels for human moderation
🧠 Memory updates based on human feedback
📊 Audit trail and traceability for every overridden term

🏗️ Placement in End-to-End Factory Flow¶

🎯 Strategic Role¶

The Ubiquitous Language Agent is a foundational pillar in the factory lifecycle. It acts as:

🧠 The semantic translator from product vision to formal domain terms
🧩 The linguistic source of truth for all downstream agents
🔗 The glue that binds business language to technical architecture

No aggregate, entity, port, or documentation should be named without ULA’s semantic validation.

🧭 High-Level Factory Flow with ULA¶

flowchart TD
    A[👓 Vision Architect Agent] --> B[🧠 Ubiquitous Language Agent]
    B --> C[🏛️ Domain Modeler Agent]
    B --> D[📐 Architecture Blueprint Agent]
    B --> E[📜 Documentation Agent]
    C --> F[🧰 Engineering Agent]
    D --> F
    F --> G[🚀 Deployment / Ops Agent]

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

🔁 Flow Description¶

Stage	ULA Role
1. Vision Ingestion	Receives high-level business prompts and feature specs
2. Glossary Generation	Extracts DDD terms, definitions, bounded contexts
3. Glossary Injection	Injects glossary into SK memory for DomainModeler and Blueprint agents
4. Event Emission	Emits `TermCreated`, `TermUpdated` events to trigger downstream responses
5. Prompt Augmentation	Adds `## Known Terms` block into other agents’ prompts to ensure aligned vocabulary
6. Documentation Feed	Sends `glossary.yaml` and `Glossary.md` to Docs Agent
7. Feedback Loop	Accepts feedback, corrections, or redefinitions from humans or agents for refinement

🧬 Downstream Agent Impact¶

Agent	How ULA Feeds It
🏛️ Domain Modeler Agent	Receives terms with roles (e.g., Aggregate, Entity) to construct DDD models
📐 Blueprint Generator	Uses bounded contexts and term names for clean architecture layer naming
🧰 Engineering Agent	Applies term names in DTOs, port contracts, folders, handlers
✅ QA Agent	Aligns acceptance criteria and test inputs with glossary-defined terms
📜 Documentation Agent	Auto-generates glossary sections in documentation UI or MkDocs

🧠 Semantic Chain Enforcement¶

ULA enforces terminological traceability across:

📌 Feature blueprints →
📘 Glossary terms →
🧱 Domain model elements →
🧰 Code artifacts →
🧪 Tests and validation →
📜 Documentation and user guides

🧠 Shared Memory Injection¶

Each collaborating agent receives:

{
  "glossary": [
    {
      "term": "Quote",
      "definition": "A pricing proposal by a customer.",
      "type": "Aggregate",
      "boundedContext": "Sales"
    }
  ],
  "trace_id": "glossary-2025-05-04",
  "sourceAgent": "ULA"
}

🔄 Reactive Agent Flows (via Events)¶

A TermCreated → Triggers DomainModelerAgent.RegenerateModel()
A TermUpdated → Triggers DocsAgent.RefreshGlossarySection()
A TermRejected → Blocks glossary injection into prompt context

🧠 Factory Flow Timeline (Simplified)¶

gantt
    title ULA Timeline Across Factory

    section Vision
    Vision Architect Agent    :done,    va1, 2025-05-04, 30m

    section Glossary Generation
    ULA Extraction & Definition :done, ula1, after va1, 20m
    ULA Validation + Retry      :done, ula2, after ula1, 15m

    section Downstream Injection
    Inject into DomainModeler   :done, ula3, after ula2, 2m
    Emit Term Events            :done, ula4, after ula3, 1m

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

✅ Summary¶

ULA is embedded in the semantic core of the factory:

🧱 No term, model, or component exists without passing through it
🧠 Guarantees alignment from vision → code → documentation
🛠️ Enables consistent, event-driven, memory-shared collaboration
🧩 Propagates shared vocabulary to every domain-specific agent

📊 Mermaid Diagrams & Architecture Embedding¶

📍 High-Level Agent Interaction Diagram¶

flowchart TD
    VISION[👓 Vision Architect Agent] -->|Product Prompt| ULA[🧠 Ubiquitous Language Agent]

    ULA -->|Glossary YAML| DOMAIN[🏛️ Domain Modeler Agent]
    ULA -->|Known Terms Memory| BLUEPRINT[📐 Blueprint Generator Agent]
    ULA -->|Glossary Markdown| DOCS[📜 Documentation Agent]
    ULA -->|Semantic Memory| ENG[🧰 Engineering Agent]
    ULA -->|Events| QA[✅ QA/Testing Agent]

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

ULA serves as a semantic nexus, distributing precise domain vocabulary to all factory components.

🧬 Internal Agent Skill Pipeline¶

graph TD
    Input[📝 Prompt Input] --> Extract[🔍 ExtractDomainTerms]
    Extract --> Classify[🏷️ ClassifyTermRole]
    Classify --> Define[🧠 GenerateTermDefinition]
    Define --> Validate[🧪 RunValidators]
    Validate --> Format[📘 FormatGlossaryYaml]
    Format --> Emit[📤 EmitTermEvents]
    Emit --> Memory[🧠 InjectIntoSKMemory]

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

🧩 Data Flow Diagram¶

flowchart LR
    Prompt[📥 Product Vision / Blueprint]
    MemoryDB[🧠 Vector DB / Cognitive Search]
    EventBus[📡 Term Events Bus]
    GlossaryFile[📘 glossary.yaml]
    DomainAgent[🏛️ Domain Modeler]

    Prompt --> ULA
    MemoryDB --> ULA
    ULA --> GlossaryFile
    ULA --> EventBus
    ULA --> DomainAgent

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

🧠 Glossary Validation Flow¶

flowchart TD
    A[📄 Raw Term Block] --> B[📐 Format Validator]
    B --> C[🧠 Semantic Validator]
    C --> D[🔍 Deduplication]
    D --> E{✅ Passed All?}
    E -- Yes --> F[📤 Emit Term]
    E -- No --> G[🔁 Retry or ⏸ Human Review]

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

📚 Glossary Lifecycle Diagram¶

sequenceDiagram
    participant V as Vision Agent
    participant U as ULA
    participant D as Domain Modeler
    participant H as Human Reviewer
    participant Q as QA Agent

    V->>U: Submit product vision prompt
    U->>U: Extract + Define terms
    U->>U: Validate + Deduplicate
    alt Valid output
        U->>D: Inject glossary
        U->>Q: Emit TermCreated event
    else Needs review
        U->>H: Push to review queue
        H->>U: Submit correction
        U->>D: Reinject corrected glossary
    end

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

📦 ULA as Microservice¶

flowchart TD
    ULAAPI[🌐 ULA REST API]
    Events[📡 Azure Service Bus]
    Blob[📂 Azure Blob Storage]
    SK[🧠 Semantic Kernel Host]
    Vector[🔍 Pinecone / Cognitive Search]

    ULAAPI --> SK
    SK --> Events
    SK --> Blob
    SK --> Vector

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

ULA is deployable as a fully autonomous, SK-powered, event-emitting, memory-integrated microservice.

✅ Summary¶

These diagrams:

📊 Embed ULA into the ConnectSoft architecture landscape
🔁 Show lifecycle from prompt to glossary emission
🧱 Visualize core flows, memory injection, retries, and API interfaces
📡 Clarify ULA’s place in cross-agent workflows and microservice architecture

🧠 Ubiquitous Language Agent Specification¶

🎯 Purpose¶

📋 Responsibilities¶

🧭 Strategic Role in the Factory¶

🔁 Example Use Cases¶

📥 Inputs¶

📤 Outputs¶

🔄 Output Example: glossary.yaml¶

🔌 Data Flow Context¶

📦 Output Consumers¶

📚 Knowledge Base¶

🧱 Built-In Semantic Models¶

🧬 Memory Architecture¶

🧠 Short-Term Memory¶

🧠 Long-Term Memory (Persistent)¶

🧠 Memory Retrieval Strategy¶

📌 Example Term Recall Logic¶

🔗 Versioned Glossary Memory¶

📌 Summary¶

🧬 Process Flow¶

🧭 Agent Flow Diagram¶

🧩 Step-by-Step Process¶

🧠 Term Extraction Logic¶

🧪 Example Output Snippet¶

🔁 Looping Behavior¶

🧑‍🤝‍🧑 Human Feedback Loop (Preview)¶

🧠 Observability Built-In¶

✅ Summary¶

🧩 Semantic Kernel Skills & Functions¶

🔧 Core Semantic Kernel Skills¶

🧬 Orchestration Flow (Internal Skill Chain)¶

🧱 Modular SK Planner Functions¶

🔌 Plugin Integrations (Optional)¶

🧠 Function Configuration¶

🧪 Example Semantic Function: GenerateTermDefinition¶

✅ Summary¶

🧠 Core Technologies¶

🔌 Integration with ConnectSoft Stack¶

🔐 Security & Identity¶

🔁 Deployment Context¶

📶 Optional Extensions¶

📌 Summary¶

🧠 System Prompt (Bootstrap Instruction)¶

🧾 Input Prompt Template¶

📄 Template: Term Extraction¶

📄 Template: Term Definition¶

📄 Template: Glossary Generation (Full)¶

📥 Example Input¶

📤 Expected Output (Excerpt)¶

✅ Summary¶

📤 Output Expectations¶

📘 Primary Artifacts Produced¶

📂 Output Format: glossary.yaml¶

🧩 Output Fields¶

📄 terms.json Sample¶

📜 Markdown Rendering (Glossary.md)¶

🔁 Event Emission: TermCreated, TermUpdated¶

📦 Output Consumers¶

✅ Summary¶

✅ Validation Process¶

🧪 Multi-Layer Validation Pipeline¶

🔁 Step-by-Step Validation Flow¶

🔎 Validators Defined¶

1. 📐 Format Validator¶

2. 🧠 Semantic Validator¶

3. 🔍 Deduplication Engine¶

4. 🔗 Alignment Validator¶

📉 Example Invalid Entries¶

📤 Validation Metadata¶

🧠 Integration with Other Agents¶

✅ Summary¶

🔁 Retry & Correction Flow¶

🔁 Retry Flow Overview¶

📦 Triggers for Retry¶

🔄 Retry Strategy¶

📄 Enriched Retry Prompt Example¶

📍 Correction Metadata (Versioned Term Block)¶

🧠 Intelligent Retry Control¶

🔔 Human Escalation Path (If All Fails)¶

✅ Summary¶

🔄 Output Example: `glossary.yaml`¶

🧪 Example Semantic Function: `GenerateTermDefinition`¶

📂 Output Format: `glossary.yaml`¶

📄 `terms.json` Sample¶

🔁 Event Emission: `TermCreated`, `TermUpdated`¶