🧠 Knowledge Graph¶

🧠 Purpose of the Knowledge Graph¶

The ConnectSoft Knowledge Graph is a unified, semantically enriched, memory-augmented graph that models all software artifacts, their relationships, and the reasoning paths between them. It serves as the foundational intelligence substrate for all AI agents in the ConnectSoft AI Software Factory.

It enables:

🔎 Contextual retrieval: Agents retrieve semantically similar or causally related artifacts
🤖 Agent orchestration: Coordinators use graph paths to chain agents and skills
🧠 Memory linkage: Traces agent-generated artifacts across sessions and runs
🔍 Traceability and validation: Connects features to events, spans, tests, and metrics
🔁 Feedback loops: Allows runtime memory evolution and auto-correction

📘 What It Is Not¶

❌ Not a static dependency map
❌ Not just a database schema
❌ Not limited to source code or documents

Instead, the graph fuses semantic, functional, temporal, and agent-generated memory into a unified structure that continuously evolves.

🧩 High-Level Structure¶

At its core, the Knowledge Graph consists of:

Element	Description
Nodes	Memory entities, semantic concepts, code files, events, spans, prompts, etc.
Edges	Directed relationships with typed meaning (`generatedFrom`, `validates`, `uses`)
Embeddings	High-dimensional vectors enabling semantic reasoning
Trace IDs	Causal or lineage-based chains between related nodes
Scopes	Project, session, or global partitioning of graph segments

🌐 Graph Roles in the Factory¶

Role	Use
Agents	Traverse the graph to recall, infer, and validate knowledge
Indexers	Populate the graph from Git, pipelines, observability, DSLs
Coordinators	Plan execution across agents based on graph segments
Validators	Use subgraphs to check consistency and completeness
Prompt Engines	Resolve grounding facts from nearest nodes and edges

🎯 Goals of the Graph¶

Enable memory-backed generation (not stateless prompts)
Model cross-domain knowledge (from UI to API to observability)
Support real-time traceability (from input to test to log to release)
Act as the reasoning backbone for autonomous agents

📌 Summary¶

The ConnectSoft Knowledge Graph is the intelligent, memory-rich foundation that:

Connects artifacts, ideas, events, and agents
Supports full-lifecycle traceability
Powers smart retrieval, validation, and agent collaboration
Evolves in real time as the platform is used

In the next cycle, we’ll explore the core elements: nodes, edges, embeddings, and how they form the semantic and structural basis of the graph.

🧠 Core Concepts: Nodes, Edges, and Embeddings¶

🔹 What Are Graph Nodes?¶

In the ConnectSoft Knowledge Graph, a node is any identifiable, semantically meaningful unit of software or knowledge. Nodes may be:

Node Type	Description
`blueprint`	A feature-level functional specification
`event`	Domain or integration-level event
`span`	A telemetry trace span
`prompt`	A semantic prompt template or conversation
`test`	A test case, scenario, or coverage definition
`component`	A UI or backend code module
`skill`	A semantic kernel or agent capability
`memory`	A persistent knowledge object with embeddings
`doc`	Markdown-based documentation or wiki page
`model`	DTO, command, entity, or projection

Each node has a nodeId, optional embedding, tags, source, and metadata.

🔗 What Are Graph Edges?¶

Edges represent semantic or causal relationships between nodes.

Edge Type	Meaning
`generatedFrom`	A was created from B (e.g., test generated from blueprint)
`validates`	A verifies B (e.g., span validates feature execution)
`uses`	A uses or calls B (e.g., component uses model)
`linkedTo`	Soft link without directionality
`tracedTo`	Causally derived from (e.g., user flow to log span)
`resolves`	A completes or implements B (e.g., skill resolves prompt)
`emits` / `handles`	Event-related relationships
`correlatesWith`	Analytics-based similarity or co-occurrence

Edges always have:

fromNodeId
toNodeId
type
Optional weight, timestamp, and sourceAgent

📐 Graph Representation (Mermaid)¶

graph TD
  A[Blueprint: User Registration]
  B[Test: UserRegistration.feature]
  C[Span: user.register]
  D[Model: RegisterUserCommand]
  E[Event: UserRegistered]

  B -- generatedFrom --> A
  C -- validates --> A
  C -- tracedTo --> B
  D -- usedIn --> A
  E -- emittedBy --> A

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

This traceable path allows agents to follow intent → execution → validation.

📊 What Are Embeddings?¶

Each node can include a vector embedding — a mathematical representation of its meaning — which allows:

🔍 Semantic search ("find similar events to UserRegistered")
🧠 Prompt augmentation (nearest memory match)
🧩 Automatic linkage based on vector proximity
🧪 Anomaly detection (disconnected but semantically similar nodes)

Embeddings are usually generated via OpenAI or Azure OpenAI APIs and stored in a vector database (e.g., Qdrant, Pinecone, Azure Cognitive Search).

🧬 Composite and Hybrid Nodes¶

Some nodes represent composite artifacts, like:

A generated microservice (containing blueprint + code + test + observability)
A project-wide SLO (connecting metrics + span + release + team)

These are virtual nodes or supernodes, enabling group operations or reasoned summarization.

📦 Metadata Fields on Nodes¶

Field	Purpose
`nodeId`	Global ID across memory graph
`type`	Node type (`event`, `test`, `prompt`, etc.)
`embedding`	Optional semantic vector
`traceId`	Optional memory chain this belongs to
`createdByAgent`	Which agent authored/generated the node
`sourcePath`	Optional file or repo origin
`tags[]`	Semantic tags for filtering
`lastUsedAt`	Recency metadata for optimization and pruning

✅ Summary¶

In this section, we established:

Nodes are semantically unique artifacts, memory-bound or physical
Edges express directional, typed relationships between them
Embeddings provide semantic reasoning across nodes
Together, this forms a semantic, memory-aware, traceable graph that powers ConnectSoft’s AI Factory

🗂️ Node Types Overview¶

This section provides a taxonomy of node types in the ConnectSoft Knowledge Graph, each representing a different software or knowledge artifact. Understanding these node types allows agents to reason across domains — from high-level vision to low-level telemetry.

📚 Node Type Categories¶

We group node types into 6 major domains, each with concrete examples:

1. Planning & Specification Nodes¶

Node Type	Description
`vision`	Strategic goals, market positioning, or product context
`blueprint`	Feature or capability defined by Product Manager or Architect
`dsl`	DSL fragment representing a domain contract or projection
`requirement`	High-level need or constraint derived from vision
`design-rule`	Constraint from UX, UI, architecture, or security policy

2. Code & Generation Nodes¶

Node Type	Description
`component`	Backend or frontend component (microservice, UI module, etc.)
`model`	DTO, command, query, event, projection
`adapter`	External integration or system wrapper
`code-snippet`	Small, reusable code block or recipe
`template-instance`	Instantiated project or module template

3. Prompt & Skill Nodes¶

Node Type	Description
`prompt`	Prompt template or system message used by an agent
`skill`	Semantic Kernel function or plugin
`planner`	Skill that orchestrates or composes other agents or skills
`feedback`	User or agent-generated signal (accept, correct, reject)

4. Quality & Validation Nodes¶

Node Type	Description
`test`	BDD feature file, unit test, integration test, or load script
`assertion`	Atomic claim to be validated (in tests or contracts)
`coverage-map`	Links between features and their coverage scope
`validator`	Agent-generated check for completeness or alignment

5. Runtime & Observability Nodes¶

Node Type	Description
`event`	Domain or integration event emitted/consumed
`span`	OpenTelemetry trace span
`metric`	Aggregated quantitative measure (counter, histogram, gauge)
`dashboard`	Grafana, Kibana, or other visualization layout
`alert`	Trigger condition linked to span or metric
`health-check`	Liveness/readiness endpoint validator

6. Documentation & Memory Nodes¶

Node Type	Description
`doc`	Markdown documentation, wiki, or pipeline artifact
`memory`	Vector-based knowledge entry stored in embedding DB
`trace-chain`	Temporal/causal sequence across nodes
`knowledge-source`	External knowledge object (e.g., API docs, tutorials)

🔁 Composite Node Examples¶

Composite Node	Contains
`microservice-node`	`blueprint` + `component` + `model` + `event` + `test`
`frontend-feature-node`	`dsl` + `component` + `prompt` + `accessibility-check`
`release-node`	`test` + `metric` + `dashboard` + `doc`

These are logical aggregations agents reason with at higher abstraction levels.

🧩 Agent Usage of Node Types¶

Agent	Typical Nodes Used
Vision Architect Agent	`vision`, `blueprint`, `requirement`, `dsl`
Developer Agent	`component`, `model`, `adapter`, `template-instance`
QA Agent	`test`, `assertion`, `span`, `coverage-map`
Observability Agent	`metric`, `dashboard`, `alert`, `span`
Documentation Writer Agent	`doc`, `prompt`, `dsl`, `trace-chain`

✅ Summary¶

This node taxonomy enables:

🔍 Precise graph queries by type
🧠 Semantic segmentation of memory spaces
🤖 Agent-specific views and responsibilities
🔁 Reusability and evolution of features across the lifecycle

🔗 Edge Types and Semantics¶

In the ConnectSoft Knowledge Graph, edges are directional, typed connections between two nodes. They describe how one node relates to another, whether causally, semantically, or structurally.

Edges are essential for:

🌐 Navigating memory relationships
🧠 Deriving traceable reasoning chains
🔁 Validating artifact consistency
🤖 Orchestrating agent workflows

🔁 Core Edge Types¶

Edge Type	Description	Example
`generatedFrom`	A was created using B as input	`Test.feature` ← `generatedFrom` ← `Blueprint`
`validates`	A checks correctness of B	`Span.user.register` → `validates` → `Feature: User Registration`
`linkedTo`	Soft association (manual or inferred)	`Prompt: Save Button` ↔ `Component: ButtonPrimary`
`uses`	A calls or depends on B	`Service: EmailSender` → `uses` → `Model: EmailCommand`
`implements`	A realizes the behavior described by B	`Service: SmsNotificationService` → `implements` → `Blueprint: NotifyUser`
`emits`	A outputs an event	`Service: OrderHandler` → `emits` → `Event: OrderCreated`
`handles`	A listens to an event	`Saga: PaymentWorkflow` ← `handles` ← `Event: OrderCreated`
`resolves`	A skill resolves an intent or prompt	`Skill: GenerateDSL` → `resolves` → `Prompt: Create domain object`
`extends`	A is a specialization of B	`Blueprint: Schedule Appointment` → `extends` → `Blueprint: Create Entity`
`tracedTo`	A was causally followed by B	`Test: RegistrationFlow` → `tracedTo` → `Span: user.register`
`coveredBy`	A is covered by B	`Feature: Reset Password` ← `coveredBy` ← `Test: Reset.feature`
`correlatesWith`	A and B appear together or are linked statistically	`Span: PaymentFailed` ↔ `correlatesWith` ↔ `Alert: RetryQueueTooLarge`

📐 Edge Metadata¶

Every edge can include:

Field	Purpose
`type`	Edge type (see above)
`fromNodeId`	ID of source node
`toNodeId`	ID of target node
`confidence`	Optional AI-based or user-defined certainty (0–1)
`timestamp`	Time of creation or update
`sourceAgent`	Who created the edge (agent ID or system)
`traceId`	Optional chain/group this edge belongs to
`tags`	Labels for traceability or filtering (e.g. `observability`, `security`)

🕸️ Sample Edge Chain¶

Blueprint: User Login
  └─ generatedFrom ─> Prompt: Generate Login Feature
  └─ uses ─> Model: LoginCommand
  └─ emits ─> Event: UserLoggedIn
      └─ handledBy ─> Service: AuditLogger
  └─ validatedBy ─> Span: user.login
  └─ coveredBy ─> Test: Login.feature

This represents a full graph path from idea to code, telemetry, and tests.

🧠 Agent Use of Edges¶

Agent	Edge Usage
QA Agent	Follows `coveredBy`, `validates`, `generatedFrom` to build test coverage matrix
Observability Agent	Follows `validates`, `correlatesWith` to trace telemetry sources
Orchestrator Agent	Follows `resolves`, `implements`, `uses` to map runtime flows
Feedback Agent	Updates `confidence` or suggests new `linkedTo` based on usage
Prompt Agent	Uses `resolves` to bind prompt → skill → output flow

🧪 Edge Creation Methods¶

🔄 Automatically during generation (e.g., test → blueprint)
🤖 Inferred by agent (e.g., prompt → blueprint)
📝 Manually annotated or enriched during review
🧬 Learned from usage (e.g., correlatesWith from logs)

✅ Summary¶

Edges are the semantic glue of the Knowledge Graph. They:

Define causality, structure, and behavior relationships
Enable agents to reason across connected concepts
Support traceability, validation, and multi-agent collaboration

In ConnectSoft’s AI Software Factory, every intelligent agent relies on the Knowledge Graph to:

🔍 Find related artifacts and context
🧠 Recall past generation outcomes or memory entries
🔄 Plan, validate, and revise workflows
🎯 Ground prompt execution in traceable inputs

Agents don’t operate in isolation — they traverse semantic, causal, and usage-based paths in the graph.

Mode	Description	Example
`direct lookup`	Resolve a node by ID or label	Get `blueprint:NotifyUser`
`semantic search`	Use embeddings to find similar nodes	“Find similar spans to `user.register`”
`type-scope traversal`	Traverse specific edge types across nodes	`Blueprint → emits → Event → handledBy → Component`
`trace follow`	Follow a `traceId` path	Reconstruct memory of `UserRegistration`
`backtrack`	Navigate reverse edges	Find all prompts that generated this component
`subgraph extraction`	Select all related nodes within a project or flow	“All nodes connected to `Feature: InviteUser`”
`filter by tags`	Select nodes by semantic tags	Find all `prompt` nodes tagged `frontend` and `accessibility`

🧭 Example: Multi-Hop Traversal by QA Agent¶

graph TD
  BP[Blueprint: Reset Password]
  EV[Event: PasswordResetRequested]
  SP[Span: user.reset-password]
  TS[Test: ResetPassword.feature]

  BP -->|emits| EV
  EV -->|validatedBy| SP
  BP -->|coveredBy| TS

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

The QA Agent traverses:

From Blueprint
To Test to confirm coverage
To Span to confirm telemetry
To Event to validate emission

Agents use specialized skills to interact with the graph:

Skill Name	Purpose
`GraphNodeSearchSkill`	Finds nodes by ID, label, type
`GraphSemanticQuerySkill`	Embedding-based similarity search
`GraphTraceReconstructionSkill`	Rebuilds memory flows by traceId
`GraphEdgeExplorerSkill`	Retrieves neighbors by edge type and direction
`GraphValidationSkill`	Detects missing or conflicting links
`GraphPathPlannerSkill`	Finds optimal traversal between concepts

🧩 Use Case Examples¶

Agent	Graph Navigation
Vision Architect	Starts at `vision` → finds `blueprints` → validates against existing `dsl`
Dev Agent	From `blueprint` → `model` + `component` + `event` + `span`
Observability Agent	Finds all `span` → `metric` → `dashboard` connected to a `component`
Feedback Agent	Finds all usages and confidence scores linked to a `prompt`
Release Coordinator	Gathers validation chain before deployment: `test` → `span` → `metric` → `slo`

📦 Subgraph Templates¶

Some agents operate on standard graph substructures, such as:

Feature execution graph: Blueprint → Event → Span → Test
Microservice node: Component + Model + Event + Span + HealthCheck
Prompt flow graph: Prompt → Skill → Blueprint → Component

These subgraphs can be retrieved, validated, and enriched.

If nodes are missing, agents use graph traversal + semantic similarity to:

Suggest likely missing connections
Create synthetic edges (e.g., inferred coveredBy)
Offer suggestions for validation or regeneration

✅ Summary¶

Agents traverse the graph to:

🧭 Understand full execution context
🔄 Reuse or evolve existing artifacts
📍 Trace paths from vision to release
🤖 Trigger downstream actions based on semantic links

🔍 Retrieval Patterns from Graph¶

In ConnectSoft's AI Software Factory, agents don’t just traverse the graph blindly — they use standardized retrieval strategies to extract relevant knowledge with:

🧠 High precision (for grounding prompts)
🔁 Repeatability (for planning and orchestration)
📊 Observability (for audit, validation, and metrics)

These patterns optimize performance and ensure semantic, structural, and temporal alignment across AI workflows.

📂 Categories of Retrieval Patterns¶

Pattern Type	Purpose
Direct	Exact-match lookup by ID or label
Semantic	Vector-based similarity search
Relational	Traversal by edge types (graph walk)
Causal	Follow trace chains across memory
Scoped	Limit by project/session/global
Temporal	Fetch by creation time, usage recency
Filtered	Query by tag, source agent, or file type

🔎 1. Direct Lookup¶

Use: Retrieve a known node by ID or type.

Graph.GetNodeById("blueprint-user-registration");
Graph.GetLatestNodesOfType("event", limit: 5);

Used by: Coordinators, Orchestrators, Validators

🔮 2. Semantic Similarity Search¶

Use: Find nodes related in meaning to a query (via embeddings).

Query: “handle payment failure”
→ Returns: `Event: PaymentDeclined`, `Span: payment.retry`, `Alert: queue.dead-letter`

Used by: Prompt Agents, Refinement Agents, Memory Agents

🔗 3. Edge-Based Traversal (Relational)¶

Use: Navigate nodes by edge relationships.

From: Blueprint: NotifyUser
→ emits → Event: UserNotified
→ validatedBy → Span: notification.sent
→ coveredBy → Test: NotificationFlow.feature

Used by: QA, Observability, Code Generator, Orchestrators

🔁 4. Trace-Based Retrieval¶

Use: Reconstruct memory flows using traceId.

traceId: trace-register-user
nodes:
- blueprint-register
- model-register-command
- span-user.register
- test-register-user.feature
- dashboard-user-activity

Used by: Validation Agents, Memory Indexers, Feedback Loops

🔍 5. Scoped Queries¶

Use: Restrict retrieval to a project/session/global scope.

Graph.GetNodesByScope("project:microservice-order-tracker");

Ensures agents don’t access unrelated or unauthorized knowledge.

Used by: Agents operating in sandboxes, Replay agents, User-specific agents

⏱️ 6. Time-Based Queries¶

Use: Fetch recent, stale, or expired knowledge.

Graph.GetNodesOlderThan(days: 60);
Graph.GetRecentlyUsedNodes(limit: 10);

Used by: Feedback agents, Memory pruning, Change detectors

🏷️ 7. Tag-Based or Property-Based Filters¶

Use: Select knowledge using structured metadata.

tags: ["frontend", "accessibility"]
createdByAgent: "UXDesignerAgent"

Used by: UI Composition Agents, Code Auditors, Regulators

🧪 Sample Combined Query¶

“Give me all Blueprints in the notifications module that were validated by a Span, covered by a Test, and used in the last 7 days.”

This query combines:

Type filtering: blueprint
Edge traversal: validates, coveredBy
Time constraint: lastUsedAt > now() - 7d
Domain tags: module:notifications

📦 Agent API Support¶

These patterns are exposed via:

GraphQuerySkill
GraphSemanticSearchSkill
GraphSubgraphExtractorSkill
GraphTraceExplorerSkill

Agents use skills + embedding APIs + vector store access to perform composite queries.

✅ Summary¶

Retrieval patterns give agents powerful ways to:

🔍 Access the right knowledge in the right scope
🧠 Enable semantic generalization and reuse
🔄 Ensure traceability and lifecycle alignment
🔐 Maintain security and isolation across tenants/projects

🕸️ Example Subgraph: “User Registered”¶

Let’s walk through a real-world subgraph that models the flow and memory chain for the “User Registered” feature.

This example demonstrates how a simple feature becomes a rich, traceable graph spanning:

Feature blueprint
Models and events
Tests and spans
Prompts and skills
Documentation and telemetry

🗂️ Nodes Involved¶

Node Type	Node Label	Description
`blueprint`	`blueprint-user-registration`	Defines the core capability
`model`	`RegisterUserCommand`	Command used in service
`event`	`UserRegistered`	Domain event emitted
`span`	`user.register`	Observability trace span
`test`	`UserRegistration.feature`	BDD test specification
`prompt`	`Prompt: Generate User Flow`	Prompt used to synthesize blueprint
`component`	`UserService`	Microservice owning the logic
`doc`	`user-registration.md`	Documentation page
`dashboard`	`dashboard-user-activity`	Monitoring dashboard linked to span
`alert`	`UserRegistrationDropRateHigh`	Alert rule on failed registrations

🔗 Edges and Relationships¶

From → To	Type
`prompt` → `blueprint`	`generatedFrom`
`blueprint` → `model`	`uses`
`blueprint` → `event`	`emits`
`blueprint` → `component`	`implementedBy`
`event` → `span`	`validatedBy`
`span` → `dashboard`	`visualizedBy`
`span` → `alert`	`triggers`
`test` → `blueprint`	`validates`
`test` → `span`	`tracedTo`
`doc` → `blueprint`	`documents`
All nodes → `traceId: trace-user-registration`	Trace link

🧬 Visual Representation (Mermaid)¶

graph TD
  PR[Prompt: Generate User Flow]
  BP[Blueprint: User Registered]
  CM[Component: UserService]
  MD[Model: RegisterUserCommand]
  EV[Event: UserRegistered]
  SP[Span: user.register]
  TS[Test: UserRegistration.feature]
  DB[Dashboard: user-activity]
  AL[Alert: drop-rate-high]
  DC[Doc: user-registration.md]

  PR -->|generatedFrom| BP
  BP -->|implementedBy| CM
  BP -->|uses| MD
  BP -->|emits| EV
  EV -->|validatedBy| SP
  SP -->|visualizedBy| DB
  SP -->|triggers| AL
  TS -->|validates| BP
  TS -->|tracedTo| SP
  DC -->|documents| BP

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

🧠 Embedded Knowledge¶

Each node in the graph:

Has an embedding (semantic vector) for similarity queries
Is linked to its traceId: trace-user-registration
Includes metadata like createdByAgent, sourcePath, lastUsedAt, confidence

🧩 Subgraph Utility for Agents¶

Agent	Use of Subgraph
Code Generator	Re-generates `UserService` if `blueprint` is updated
QA Agent	Traces `test → span → event` to ensure runtime alignment
Observability Agent	Validates that `span` is linked to a `dashboard` and `alert`
Documentation Writer	Pulls `blueprint`, `event`, `test`, and `span` into wiki
Feedback Agent	Updates confidence scores on `prompt` → `blueprint` path

🔁 Real-Time Update Flow¶

If a prompt is improved or blueprint evolves:

Regenerates affected nodes (test, component, event)
Triggers graph edge rewrite
Updates semantic embeddings
Pushes downstream changes via event bus to all subscribed agents

✅ Summary¶

This subgraph demonstrates how a single user feature becomes:

🧠 A semantically indexed knowledge bundle
🕸️ A traceable chain of execution and validation
🔄 A reactive, update-aware system of memory

🔄 Real-Time Graph Updates¶

In the ConnectSoft AI Software Factory, the Knowledge Graph is not a static database — it’s a living memory network that evolves as agents operate.

Every new:

📄 Prompt
🧠 Memory
🧪 Test
📦 Component
📊 Span or Metric

...can modify the graph in real time, triggering downstream updates, rewrites, or revalidations.

🔁 Real-Time Update Triggers¶

Trigger Type	Description	Example
`memory.created`	New semantic node or embedding	Generated new feature blueprint
`memory.updated`	Existing node rewritten or reclassified	Blueprint changed → revalidate test
`artifact.generated`	New code, test, span, or event emitted	Agent emits new `Event: UserActivated`
`feedback.received`	Signal triggers change in edge or confidence	User rejects a generated component
`agent.heartbeat`	Periodic check for graph pruning or inference	Cleanup unused spans after 30 days

🧠 Update Flow Overview¶

sequenceDiagram
    participant Agent
    participant MemoryIndex
    participant GraphUpdater
    participant VectorStore
    participant EventBus

    Agent->>MemoryIndex: Emit new memory node
    MemoryIndex->>VectorStore: Embed and store
    MemoryIndex->>GraphUpdater: Register node and edges
    GraphUpdater->>EventBus: Publish GraphNodeCreated event
    EventBus->>OtherAgents: Trigger downstream enrichment

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

🧩 Node Update Actions¶

When a node is added or modified, the following occur:

Embedding calculated or updated
Edges evaluated: add, remove, update weights
Tags and metadata refreshed
Trace chains regenerated if applicable
Indexes refreshed (index-codebase, index-prompts, etc.)

🔗 Edge Update Behavior¶

New memory may auto-link to similar past nodes via linkedTo
Tests may back-reference generated code via validates
Updated span may correlate with alerts or metrics
Feedback can reduce or increase confidence score on edges

🛠️ Agent Collaboration on Updates¶

Agents may publish:

Event	Purpose
`GraphNodeCreated`	New semantic unit added
`GraphEdgeUpdated`	A relationship changed
`TraceCompleted`	A full causal chain formed
`GraphNodeDeprecated`	Old or stale node marked inactive
`GraphMemoryInferred`	New relationship inferred by AI

Subscribed agents (e.g., validators, orchestrators, documentation writers) respond in real-time.

📂 Example Update Chain¶

Agent: ProductManagerAgent
→ Creates `blueprint: ReferralProgram`

↓ Triggers:

- `component: ReferralService`
- `event: UserReferred`
- `test: Referral.feature`
- `dashboard: ReferralMetrics`

↓ Updates:

- New edges (`emits`, `coveredBy`, `visualizedBy`)
- Semantic embeddings
- Trace ID: `trace-referral-program`

🔒 Governance and Constraints¶

TTL-based pruning (e.g., memory expires after 90 days if unused)
Access policies enforced on sensitive nodes (e.g., security tests)
Version control optionally tracks node changes over time

🧠 Auto-Healing and Edge Rewriting¶

The Graph Updater may:

Auto-repair broken implements or validates links
Reweight outdated nodes
Flag low-confidence prompts for regeneration

✅ Summary¶

Real-time updates allow the Knowledge Graph to:

🌱 Grow organically with agent operations
🔁 Evolve from feedback and memory changes
🤖 Trigger collaboration and regeneration
🕸️ Maintain full traceability across AI software workflows

🔄 Graph and Index Synchronization¶

In the ConnectSoft AI Software Factory, indexes and the knowledge graph must remain tightly synchronized to ensure:

🧠 Semantic consistency across memory stores
🔍 Accurate, up-to-date retrieval for all agents
📈 Traceability from raw data to high-level knowledge
🔁 Real-time updates between code, templates, docs, and memory

Think of the indexes as views and fast access tables, while the graph acts as the relational, semantic brain.

🧩 What Are Knowledge Indexes?¶

Each index-* file describes and stores nodes, their metadata, and some partial edges.

Examples include:

Index Name	Description
`index-libraries`	Maps NuGet/library definitions, use cases, and samples
`index-templates`	Lists available templates, usage, and context
`index-prompts`	Describes prompt templates, inputs, outputs
`index-dsl`	DSL definitions, node types, and usage examples
`index-tests`	Test coverage maps and BDD-to-blueprint links
`index-metrics`	Aggregated SLOs, alerts, dashboards, spans
`index-codebase`	Summarized component and model-level architecture
`index-memory-graphs`	Trace chains and embedded memory relationships
`index-project-structure`	Repositories, solutions, pipelines, folder maps

🔁 Synchronization Flow¶

flowchart TD
    IDX[index-libraries.md]
    IDX2[index-prompts.md]
    IDX3[index-tests.md]
    G(Graph Core)
    VS[Vector Store]
    TRC[Trace Store]

    IDX -->|parse+emit nodes| G
    IDX2 -->|emit prompts, skills| G
    IDX3 -->|add span+test edges| G
    G -->|semantic updates| VS
    G -->|trace updates| TRC

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

🔄 Index-to-Graph Mapping¶

Index	Graph Contribution
`index-templates`	`template-instance`, `prompt`, `model` nodes + `generatedFrom` edges
`index-codebase`	`component`, `adapter`, `uses`, `implements` edges
`index-memory-graphs`	Full subgraph topology, trace-based chains
`index-tests`	`test`, `assertion`, `validates`, `tracedTo` edges
`index-prompts`	`prompt`, `resolves`, `feedback` edges
`index-observability`	`span`, `metric`, `dashboard`, `correlatesWith`, `validatedBy`
`index-project-structure`	`repo`, `pipeline`, `deployment`, `ci-task` nodes
`index-docs`	`doc`, `documents`, `linkedTo`, `createdByAgent` edges

🔄 Graph-to-Index Feedback¶

The graph may also emit updates back to indexes:

Trigger	Index Affected	Description
New prompt used in test	`index-prompts`	Update prompt usage stats
Agent rewrites event span	`index-observability`	Refresh `validatedBy` edge
Blueprint links added to docs	`index-docs`	Insert new `documents` edge

✅ This allows indexes to be partially materialized views of the core evolving graph.

🧠 Agent Roles in Sync¶

Agent	Role
Indexer Agent	Periodically ingests from filesystem, repos, CI
Graph Updater Agent	Rebuilds edges, resolves trace IDs, inserts into core graph
Semantic Embedding Agent	Computes vector embeddings and updates vector DB
Feedback Loop Agent	Adjusts confidence, validity, usage metrics in graph/index

🔧 Change Detection and Delta Update¶

Graph synchronization tools support:

Hash-based file diffing
Git history scans (for index drift detection)
Trace consistency checks (for orphaned nodes)
Auto-suggestions for index patching

🧩 Sample Use Case¶

A .feature file is added for User Login:

index-tests updates with test node
Graph adds validates → blueprint-user-login
span-user.login inferred from coverage and added
dashboard-auth-metrics linked by embedding similarity
index-observability updated automatically

✅ Summary¶

Graph–Index Synchronization ensures that:

📂 Raw data → semantic structure → traceable memory
🧠 Index files remain fresh and usable across projects
🔄 Agents can reason over all relevant knowledge
🤖 Real-time edits propagate to memory, prompts, and tests

🤖 Agent-Specific Graph Views¶

In ConnectSoft’s AI Software Factory, the full knowledge graph is too large and complex to be consumed as-is by every agent.

Instead, agents operate on scoped, filtered, or role-specific views of the graph that match:

Their functional responsibilities
Required input/output types
Trust and access boundaries
Performance and memory constraints

Agent views are like lenses over the graph — focused, filtered, and fit-for-purpose.

🧭 Types of Graph Views¶

View Type	Description	Example
Role-Based View	Limited by the agent’s job	QA Agent sees `test`, `span`, `assertion`
Scope-Limited View	Limited to a project/session	Dev Agent views only the current repo’s graph
Type-Limited View	Operates on a subset of node types	Prompt Agent only sees `prompt`, `skill`, `feedback`
Semantic-Filtered View	Limited by tags or embedding clusters	UX Agent sees all nodes tagged `accessibility`
Trust-Based View	Scoped by permission	Security Agent can see `alert`, `health-check`, `audit-log` nodes only

📂 View Construction Pipeline¶

flowchart TD
    G[Graph Core]
    F[Filter Criteria]
    V[Agent View Builder]
    A[Agent]

    G --> F
    F --> V
    V --> A

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

Filters are defined declaratively or dynamically per agent
Views are built and cached based on usage or prompt execution
Some agents (e.g. Coordinators) have multi-agent merged views

🧠 Sample View: QA Agent¶

Included Node Types	Edge Types Followed	Exclusions
`blueprint`, `test`, `span`, `assertion`, `metric`	`validates`, `tracedTo`, `coveredBy`, `correlatesWith`	No prompts, skills, or DSL nodes

Used for: test validation, trace coverage, runtime alignment

🔐 Sample View: Security Agent¶

Included Node Types	Edge Types Followed
`blueprint`, `alert`, `audit-log`, `span`, `policy`, `health-check`	`emits`, `correlatesWith`, `documents`, `violates`, `dependsOn`

Used for: vulnerability triage, compliance checks, policy inference

🧩 View Personalization Features¶

🧠 Embedding filters: show only nearest N results to a node or concept
🗂️ Tag filters: view nodes tagged with frontend, mobile, core-domain
🔁 Trace filters: view only one traceId-scoped graph
📍 Agent-local memory: augment graph with in-memory nodes not yet persisted

⚙️ How Views Are Used¶

Purpose	Agent Use
Prompt grounding	Load nearby blueprint → model → test nodes
Skill selection	Filter for matching `resolves` edges
Validation paths	Follow only `validates`, `tracedTo`, `emits` edges
Graph scoring	Identify gaps, inconsistencies, or low-confidence nodes
Report generation	Visualize a filtered graph (e.g., SLO trace, BDD coverage)

🔁 View Refreshing and Invalidation¶

Views are refreshed:

On agent startup
After a graph update event (e.g., GraphNodeCreated)
When usage patterns shift (e.g., semantic similarity moves)

View consistency is guaranteed using a versioned graph snapshot hash.

✅ Summary¶

Agent-specific views enable:

🚀 Focused memory reasoning
🔒 Secure, role-aware knowledge access
⚡ Faster agent performance
🧠 Aligned prompt augmentation and traceability

🔄 Memory Traces as Dynamic Graph Edges¶

In the ConnectSoft Knowledge Graph, a memory trace is a temporal and semantic chain of nodes that represent:

A generation journey
A reasoning session
A user interaction episode
A pipeline execution
A feedback lifecycle

Each trace forms a dynamic subgraph — a linked path across nodes involved in a task or decision process.

🧠 Why Use Traces?¶

Traces help the system:

🧭 Reconstruct agent decision-making steps
🔁 Replay generation or failures for debugging
📊 Visualize feature-to-telemetry-to-test coverage
🔄 Synchronize prompt → code → test → span lifecycles
🧪 Enable observability and backpropagation from outcomes

🔗 How Traces Are Formed¶

Traces are automatically assigned by orchestration agents or semantic workflows using:

Mechanism	Source
`traceId` propagation	Across prompt executions, generations, tests
`sessionId` mapping	From user or agent conversation context
`event-bus` correlation	From emitted/handled events with shared metadata
`graph hook` binding	When nodes share a causal generation path

🔗 Example: Trace `trace-user-registration`¶

Node	Relationship
`prompt: Create User Flow`	Seed prompt
`blueprint-user-registration`	Generated from prompt
`model-RegisterUserCommand`	Used in blueprint
`event-UserRegistered`	Emitted by service
`span-user.register`	Captured by telemetry
`test-UserRegistration.feature`	Validates blueprint and runtime
`dashboard-user-activity`	Visualizes related span

All nodes share:

traceId: trace-user-registration

🧭 Trace Edge Type¶

Internally, all trace-based node connections are rendered as:

{
  "type": "tracedTo",
  "traceId": "trace-abc",
  "confidence": 1.0,
  "createdByAgent": "OrchestratorAgent"
}

These are soft edges, dynamically recomputed if trace membership evolves.

🧪 Use Cases for Traces¶

Use Case	Description
Debug generation issues	Walk the chain of memory and outputs
Validate coverage	Find gaps in test → span → blueprint links
Summarize flows	Collapse trace into a Markdown artifact or wiki page
Re-run flow	Re-trigger blueprint → model → test sequence
De-duplicate memory	Collapse near-duplicate traces across projects

🔁 Dynamic Nature¶

Traces are updated:

When new nodes attach to existing traceId
When agents add missing nodes or inferred links
When feedback suggests alternate edge directionality
When subgraph re-clustering reassigns trace membership

📐 Visual Example (Mermaid)¶

graph TD
  A[Prompt: Create User Flow]
  B[Blueprint: User Registration]
  C[Model: RegisterUserCommand]
  D[Event: UserRegistered]
  E[Span: user.register]
  F[Test: UserRegistration.feature]

  A --> B --> C --> D --> E --> F
  classDef trace fill:#e3f6f5,stroke:#00a1ab,stroke-width:2px;
  class A,B,C,D,E,F trace;

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

This path is what agents reason over when validating or regenerating a feature.

🧠 Memory vs Graph¶

Memory	Graph
Raw, semantically embedded objects	Structured, typed, linked topology
Stored in vector DB	Stored in graph DB
Used for similarity	Used for reasoning
Indexed by content	Indexed by ID/type/edges

Traces link memory and graph — semantically and causally.

✅ Summary¶

Memory traces:

🔗 Connect temporally related nodes
📊 Power reasoning, coverage, observability
♻️ Allow replays and root cause analysis
🧠 Form the dynamic backbone of graph-driven automation

🏷️ Semantic Tagging and Ontologies in the Graph¶

As the ConnectSoft Knowledge Graph grows across thousands of features, templates, and memory traces, we need semantic organization mechanisms to support:

🔍 Intelligent filtering
🧠 Knowledge clustering and routing
🤖 Agent-specific view generation
🏗️ Cross-domain knowledge reuse
📦 Multi-tenant and product-line segregation

Semantic tags and ontological classification enable reasoning at scale across diverse software components.

🏷️ Tagging in the Graph¶

✅ Tags Are Simple Key/Value Labels¶

Tags are assigned to nodes (and sometimes edges) as:

tags:
  - domain:authentication
  - layer:application
  - purpose:validation
  - language:csharp
  - interface:rest
  - capability:multi-tenant

🔁 Applied During:¶

Generation (agent auto-tags blueprint, model, span, etc.)
Index parsing (index-tests, index-codebase, etc.)
Human enrichment (feedback, prompt injection)
Ontology inference (from DSL, path, repo)

🧠 Sample Tag Classes¶

Class	Example Tags
`domain`	`authentication`, `notifications`, `invoicing`, `ai-agent-orchestration`
`layer`	`frontend`, `backend`, `infrastructure`, `observability`, `orchestration`
`technology`	`csharp`, `typescript`, `blazor`, `grpc`, `openapi`, `nhibernate`
`functionality`	`email-verification`, `audit-logging`, `file-upload`, `token-expiry`
`compliance`	`gdpr`, `hipaa`, `iso27001`
`ownership`	`agent:product-manager`, `agent:observability-engineer`
`project`	`template:auth-server`, `repo:user-service`, `epic:epic-01234`

🧩 Example: Tagged Node¶

nodeId: blueprint-user-registration
type: blueprint
tags:
  - domain:authentication
  - layer:application
  - functionality:user-onboarding
  - agent:product-manager

Agents can now:

Retrieve all blueprints related to user-onboarding
Filter prompts for product-manager agents only
Traverse the application layer exclusively

🧬 Ontologies and Inference Rules¶

Tags evolve into ontological classes and relationships. For example:

Ontology Term	Description
`is-a`	Blueprint is-a onboarding-feature
`part-of`	`span-user.register` part-of `user-registration-flow`
`requires`	`2fa-login` requires `email-verification`
`relates-to`	`accessibility-check` relates-to `component:ButtonPrimary`
`enables`	`agent-UX-designer` enables `a11y-improvements`

Ontologies are often inferred using DSL structures and prompt context.

📚 Ontology Registry¶

A shared internal dictionary defines canonical terms and hierarchies:

- domain:
    - authentication:
        - login
        - 2fa
        - oauth
    - notifications:
        - email
        - sms
        - push

- functionality:
    - user-onboarding:
        - invite
        - register
        - confirm

Agents can register, suggest, or subscribe to ontology segments.

🔧 Use Cases for Tags and Ontologies¶

Use Case	Benefit
Prompt Augmentation	Filter only relevant prompts per domain or agent
Graph Traversal	Traverse only nodes tagged `layer:observability`
Index Scoping	Limit to `project:auth-server` nodes
Multi-Agent Dispatching	Trigger `agent:security-engineer` if tag `compliance:gdpr` exists
Trace Aggregation	Group traces under `capability:multi-tenant` or `functionality:user-onboarding`

🧠 Graph View Enhanced by Tags¶

Views described in Cycle 10 rely heavily on tags to:

Build slices per project
Compose agent-specific filters
Drive UI-based filtering and report generation

✅ Summary¶

Semantic tags and ontologies provide:

🧠 Structured meaning across knowledge artifacts
🤖 Automation in agent dispatching and view generation
🕸️ Enhanced filtering, tracing, and aggregation
🧩 Scalable categorization for multi-domain SaaS generation

🧠 Graph-Powered Prompt Enrichment¶

The quality of generated code, DSL, tests, and documentation depends directly on the context fed into each agent’s prompt.

The Knowledge Graph acts as a context amplifier, supplying:

🔍 Prior artifacts (blueprints, models, tests)
🧩 Domain-specific instructions
📎 Trace-based history
🧠 Semantically related nodes
📚 Tags, examples, and DSL references

Enrichment enables prompts to be grounded, consistent, and relevant — essential for deterministic, high-quality output.

🧠 Prompt Enrichment Sources¶

Source	Description
Graph neighborhood	Traverses nearby `blueprint`, `model`, `span`, etc.
Semantic similarity	Finds close `prompt`, `component`, `test` via embeddings
Trace history	Recalls earlier nodes in the current flow
Tags	Injects domain/layer-specific modifiers (e.g., `layer:frontend`)
Ontology	Infers broader context (e.g., `2fa` implies `email-verification`)
Feedback metadata	Adapts based on what worked previously (`confidence`, `rating`)

📄 Prompt Enrichment Example¶

Prompt Template:

Generate a new feature for user onboarding.
The output should include a blueprint, model, and test.

Use domain tags: {{ domain_tags }}
Reference past blueprints: {{ related_blueprints }}
Follow compliance constraints: {{ security_tags }}

Graph Enrichment Result:

{
  "domain_tags": ["domain:authentication", "functionality:user-onboarding"],
  "related_blueprints": [
    "blueprint-user-registration",
    "blueprint-email-verification"
  ],
  "security_tags": ["compliance:gdpr", "capability:multi-tenant"]
}

🔄 Graph Enrichment Pipeline¶

graph TD
    P[PromptRequest]
    G[Graph Context Engine]
    N[Nearby Nodes]
    T[Tags & Ontology]
    E[Enriched Prompt]

    P --> G
    G --> N
    G --> T
    N --> E
    T --> E

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

Steps:

GraphContextEngine receives the prompt request
It loads:
- Semantic neighbors
- Prior trace chain (if present)
- Tags + ontological mappings
Merges context into prompt template
Passes enriched prompt to Semantic Kernel planner or agent skill

🧪 Skill Injection via Graph¶

Some prompts refer to reusable skills or injected fragments that are:

Stored as prompt nodes in the graph
Tagged by resolves → blueprint types
Selected by graph traversal + scoring

The graph becomes a function registry, allowing agents to "compose" prompts dynamically from reusable, validated units.

🧠 Example Agent Flow¶

UX Designer Agent

Receives prompt: “Improve accessibility for mobile onboarding”
Graph lookup:
- Finds component:ButtonPrimary, span:user.tab-navigate
- Tags: layer:frontend, accessibility, mobile
Injects:
- Prior blueprint: blueprint-mobile-registration
- Feedback: "screen reader issue" (score: low)
Resulting enriched prompt triggers regeneration of more accessible button blueprint and BDD test

Generated prompt fragments can be:

Published back to graph as prompt nodes
Shared across agents via trace linkage or skill references
Tagged with agent-intent, resolves, generatedFrom for reuse

✅ Summary¶

Graph-powered enrichment ensures that:

📎 Prompts reflect real system state and past learnings
🔁 Reuse of successful context improves generation quality
📚 Knowledge is shared across agents, roles, and iterations
🧠 Enrichment is dynamic, traceable, and context-sensitive

🔁 Graph Feedback Loops and Confidence Scoring¶

In the ConnectSoft AI Software Factory, agents and users constantly evaluate:

✅ Prompt quality
🧪 Test effectiveness
🧠 Blueprint usefulness
🔁 Generated artifacts

These evaluations must feed back into the graph to:

Improve future generations
Prioritize or prune memory
Influence routing and prompt enrichment
Trigger agent interventions

Feedback transforms the graph from a static memory to a self-improving intelligence core.

🔄 Feedback Loop Mechanisms¶

Source	Feedback Type	Targets
Agent self-check	Code/test/prompt confidence	Nodes/edges it created
Agent-to-agent	Validation or review outcomes	Peer agent outputs
Human-in-the-loop	Ratings, thumbs up/down	Prompts, docs, UX
Observability	Runtime errors, SLO violations	Spans, events, blueprints
Test coverage	Pass/fail, edge case hits	Tests, spans, events

📊 Confidence Model¶

Each node and edge in the graph includes:

{
  "confidence": 0.92,
  "lastEvaluatedBy": "QAEngineerAgent",
  "evaluationType": "unit-tests+trace-alignment",
  "feedback": [
    { "source": "agent-qa", "score": 1.0 },
    { "source": "feedback-loop", "score": 0.85 },
    { "source": "user", "score": 0.5 }
  ]
}

Confidence is a weighted average
Feedback history is retained for explainability
Source attribution enables traceable learning

🧠 Example Flow¶

ProductManagerAgent generates blueprint for User Invite
CodeGeneratorAgent uses it → generates code
QAAgent validates → finds span missing
Confidence drops to 0.6 and edge blueprint → span marked missing
PromptRegenerationAgent triggered via feedback rule
Blueprint enriched, span added, feedback loop closed

📎 Graph Edge Feedback¶

Edges also evolve via feedback:

Edge Type	Feedback	Result
`validates`	Test missing span	Edge dropped or marked low confidence
`generatedFrom`	Prompt not reproducible	Confidence score adjusted
`resolves`	Prompt–skill mismatch	Ontology tag updated

🔔 Triggered Events from Feedback¶

Event	Purpose
`GraphConfidenceDropped`	Signal downstream agents to revalidate
`GraphEdgeConflict`	Multiple conflicting relationships exist
`GraphNodeFlaggedForReview`	Human moderation or approval required
`PromptRetrainSuggested`	Agent prompt needs reinforcement or rewrite

🧠 Visual: Feedback Loop (Mermaid)¶

sequenceDiagram
    participant Agent as CodeGenAgent
    participant QA as QAAgent
    participant Graph as KnowledgeGraph
    participant Loop as FeedbackLoopAgent

    Agent->>Graph: Create blueprint node
    QA->>Graph: Mark test failed, span missing
    Graph->>Loop: GraphConfidenceDropped
    Loop->>Agent: Trigger prompt regeneration
    Agent->>Graph: Update blueprint + span

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

📌 Feedback Use Cases¶

Use Case	Impact
Regenerate underperforming prompts	Improve template quality
Flag weak tests	Trigger test reinforcement
Reward high-confidence outputs	Promote reuse in prompt composition
Auto-suggest missing spans/events	Close trace gaps

🧠 Feedback Propagation¶

A feedback change can:

Affect multiple connected nodes (e.g., all generatedFrom the same prompt)
Influence future prompt enrichment
Trigger a new trace chain via FeedbackImprovementTrace

✅ Summary¶

Graph feedback loops power:

🧠 Self-correcting agents
🔁 Closed-loop generation improvement
📊 Confidence-aware decision-making
📎 Traceable scoring with source context

🤖 Multi-Agent Coordination Through the Graph¶

In the ConnectSoft AI Software Factory, dozens of autonomous agents must:

Collaborate without hardcoded flows
Share knowledge and trace context
React to each other’s outputs
Plan multi-step transformations

The Knowledge Graph becomes a coordination substrate, enabling declarative collaboration through:

Shared memory
Trace-linked dependencies
Edge-based triggers
Tag- and ontology-based routing

The graph is the shared workspace that agents use to talk, delegate, trace, and evolve.

🧠 Agent Interaction via Graph¶

Mechanism	Purpose	Example
Trace Continuation	Agent picks up where another left off	`CodeAgent` → `TestAgent` → `PerfAgent`
Tag Dispatching	Agents subscribe to nodes by tag	`tag:accessibility` triggers `UXAgent`
Graph Event Hooks	Agent acts on node creation/update	`GraphNodeCreated` → regenerate docs
Validation Loops	Agent validates/extends another’s output	`QAAgent` validates `CodeAgent` output
Conflict Resolution	Two agents propose competing edges → resolved by `CoordinatorAgent`

📎 Example Flow: Feature Launch¶

1. ProductManagerAgent → blueprint: ReferralProgram
2. CodeAgent → model, service class, event
3. QAAgent → test coverage
4. ObservabilityAgent → span, dashboard
5. ReleaseAgent → connects deployment pipeline

Each step emits graph nodes and edges like:

generatedFrom
validates
coveredBy
emits
documents

… which other agents watch for and respond to.

🔁 Coordination via Shared Trace¶

traceId: trace-referral-program
agents:
  - ProductManagerAgent
  - CodeAgent
  - QAAgent
  - ObservabilityAgent

This shared trace becomes:

🧠 A reasoning context
🧩 A scope for prompt enrichment
🔁 A closure loop for validation, testing, and release

🧩 Graph-Centric Routing Logic¶

Declarative routing by agents:¶

triggers:
  - onNodeCreated:
      type: blueprint
      tags: [domain:referrals]
      then: CodeGeneratorAgent
  - onNodeValidated:
      type: span
      then: ObservabilityAgent

Runtime graph engine uses:¶

Tags
Ontology
Confidence level
Missing edges
Agent history

To dispatch and queue agents dynamically — no static pipeline needed.

🤖 Agent Graph Skills Used¶

Skill	Purpose
`GraphEventSubscriberSkill`	Subscribe to graph events like node created or updated
`GraphRoleAssignerSkill`	Assign agent roles per node type or tag
`GraphDependencyScannerSkill`	Detect unfulfilled edges (e.g., test → span missing)
`GraphTraceStitcherSkill`	Combine multiple agents into a single causal trace

📊 Agent Orchestration Metrics¶

The graph allows the system to answer:

Which agent worked on which trace?
What’s the confidence level of the full trace?
Are there conflicting edges or node types?
Who last updated a node or edge?
What’s the audit trail across agents for a feature?

This enables trustable, auditable, and observable agent-based collaboration.

🔐 Role-Based Edge Ownership¶

Each edge can record its creator agent:

edge:
  type: validates
  from: test-registration.feature
  to: span-user.register
  createdBy: QAAgent
  confidence: 0.91

Other agents (like GraphAuditorAgent) can verify edge consistency or enforce rules like:

A test must be validated by at least one span
A prompt must not emit conflicting blueprints
Only CoordinatorAgent can resolve disputed implements edges

✅ Summary¶

The Knowledge Graph enables:

🧠 Decentralized, trace-based coordination
🧩 Role-based participation across agents
🔁 Feedback-driven refinement and conflict resolution
🛠️ Declarative routing and orchestration without code

🔍 Graph Queries, APIs, and Agent Interfaces¶

For autonomous reasoning, validation, enrichment, and collaboration, agents must interact with the Knowledge Graph programmatically.

This requires:

📡 Read access to structured + semantic nodes
✏️ Write access for creating/updating edges and confidence
🔍 Query interfaces (structured and vector-based)
🔐 Role-aware, scoped views and access control

The Graph becomes a runtime knowledge interface for all autonomous reasoning and agent operations.

🔧 Core Graph API Capabilities¶

Function	Description
`getNode(id)`	Fetch a node by ID
`findNodesByType(type)`	Get all nodes of a specific kind
`queryByTag(tag)`	Retrieve nodes tagged with a semantic key
`getEdges(from	to)`	Fetch incoming/outgoing edges
`getTrace(traceId)`	Fetch full causal path
`searchSimilar(embedding	text)`	Vector-based semantic similarity search
`addNode(node)`	Insert new memory or knowledge node
`addEdge(from, to, type, metadata)`	Link nodes with directed edge
`updateConfidence(node	edge)`	Adjust confidence based on feedback
`deleteNode(id)`	Remove a deprecated or invalid node
`subscribe(eventType, criteria)`	Register agent to receive future updates

🧠 Agent Interface Abstractions¶

Each agent uses an abstracted KnowledgeGraphClient or skillset like:

graph.FindBlueprintsByDomain("authentication");
graph.GetTrace("trace-user-onboarding");
graph.SearchSimilar("generate user registration span");
graph.AddEdge("test-UserRegistration.feature", "span-user.register", "validates");
graph.UpdateConfidence("prompt-LoginFlow", 0.8);

The interface abstracts persistence, inference, and vector scoring behind a clean API.

📦 Graph Backend Technologies (Suggested)¶

Component	Tech Stack
Graph Store	Neo4j, Azure Cosmos DB (Gremlin), RedisGraph
Vector DB	Azure Cognitive Search, Qdrant, Pinecone, Weaviate
Access Layer	REST/GraphQL or gRPC API exposed by `GraphService`
Authorization	Embedded per-agent scopes, token-based identity
Change Feed / Events	Azure Event Grid, Kafka, or Redis Streams

🧩 Query Languages¶

Graph supports multiple access modes:

🧾 Structured: REST, GraphQL (nodes, edges, paths)
🧠 Semantic: searchSimilar, findNear("register blueprint")
🔁 Event: subscribe to "node.created" where type = 'blueprint'
🧮 DSL: internal DSL like:

query:
  start: blueprint
  filter:
    tags: [authentication]
  follow:
    - emits → span
    - validates ← test

🔐 Role-Based Access¶

Each agent or service has a Graph Role, e.g.:

Role	Can Read	Can Write
`UXDesignerAgent`	blueprints, UI spans, prompts	prompts, tests
`ObservabilityAgent`	spans, dashboards	dashboards, alerts
`CoordinatorAgent`	all nodes	all nodes and edges
`QAAgent`	tests, spans, events	tests, validations

Access is enforced at the API level and optionally at the data layer.

🚦 Event Subscriptions¶

Agents can react in real-time via event streams:

subscribe:
  - type: node.created
    filter:
      type: blueprint
      tags: [domain:referrals]
  - type: confidence.dropped
    target: span

These events are streamed via:

Azure Event Grid
Redis Streams
gRPC streaming
Webhooks (for external plugins)

🧠 Example Usage: Prompt Agent¶

PromptAgent receives request to generate a span
Calls:
- searchSimilar("user registered telemetry")
- getTrace("trace-user-registration")
- findTags("layer:observability")
Composes enriched prompt
Generates output
Writes to graph:
- addNode("span-user.register")
- addEdge("blueprint-user-registration", "span-user.register", "emits")

✅ Summary¶

The Graph API provides agents with:

🔍 Fine-grained query access to all knowledge
📡 Realtime event and confidence signals
✏️ Writeback, feedback, and coordination hooks
🔐 Secure, role-scoped interaction with system memory

🧹 Graph Confidence Decay and Pruning Strategies¶

As the AI Software Factory continuously generates:

🧠 Blueprints
🔤 Prompts
🧪 Tests
📈 Spans
📚 Docs

… the Knowledge Graph rapidly expands.

Without pruning and decay, the graph may:

🐢 Slow down lookups and routing
🧯 Surface outdated or invalid nodes
🔁 Interfere with prompt enrichment via stale context
❌ Lead to inaccurate inferences or decisions

Decay and pruning are essential for maintaining precision, performance, and trustworthiness.

📉 Confidence Decay Model¶

All nodes and edges in the graph have a confidence score (see Cycle 14).

This score can decay over time if:

The node isn’t referenced or used
Newer, higher-confidence alternatives emerge
Related traces are deprecated
Observability shows weak performance or errors

🕒 Time-Based Decay Function (Example)¶

decay:
  initial_confidence: 0.95
  half_life_days: 30
  decay_function: exponential
  usage_boost: +0.05 per confirmed reuse

🔁 Dynamic Confidence Adjustments¶

Trigger	Effect
Node unused for 60+ days	Confidence drops by 20–40%
Test fails repeatedly	Related blueprint confidence drops
Node reused by prompt/test	Confidence boosted
Human/agent feedback (downvote)	Score penalty
Related trace deprecated	Downstream decay

🧹 Pruning Criteria¶

Condition	Action
Confidence < 0.3 and unused	Delete node/edge
Conflicts with newer, higher-confidence node	Replace and log change
Expired TTL + no incoming edges	Garbage collect
Feedback flagged for removal	Queue for approval or auto-prune
Deprecated project/tag	Archive subtree and remove from active views

🗂️ Node Metadata for Pruning¶

Each node/edge stores:

lastUsed: 2025-04-01
timesUsed: 3
lastConfidenceUpdate: 2025-05-10
createdBy: Agent:ProductManagerAgent
confidence: 0.42
ttl: 90d

These values power cleanup heuristics and auditability.

🔍 Visualizing Decay (Optional)¶

graph TD
    A[Blueprint: ObsoleteLoginFlow]
    B[Test: LoginV1]
    C[Span: user.login]
    D[Prompt: login-template]

    A --> B --> C --> D
    classDef lowConfidence fill:#fdd,stroke:#c00;
    class A,B,C,D lowConfidence;

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

All nodes marked for pruning due to < 0.3 confidence, 90d inactivity.

🧠 Pruning Agent & Strategy¶

Agent: GraphJanitorAgent

Responsibilities:

Sweep low-confidence, low-usage nodes
Archive deprecated content
Emit GraphNodePruned events
Resolve orphaned edges
Optionally trigger agent reruns (regeneration)

🛠️ Archive vs Delete¶

Operation	Use When	Result
Archive	May be reused historically	Moved to `archive/` namespace, hidden from enrichment
Delete	Obsolete, broken, unsafe	Permanently removed, backup retained
Mark as Legacy	Still needed for reference, but not active	Tagged `status:legacy`, filtered from suggestions

✅ Summary¶

Confidence decay and pruning:

♻️ Keep the graph clean, fast, and relevant
🧠 Reduce prompt noise from stale nodes
🧪 Improve quality of suggestions and routing
🔐 Ensure knowledge evolves with real-world feedback

🏢 Multi-Tenant and Environment-Aware Graph Partitioning¶

In the ConnectSoft AI Software Factory, the graph powers:

🧱 Product generation across domains
🏭 Multiple tenants and clients
🧪 Development, test, and production environments
🧠 Shared global memory + tenant-specific knowledge

To avoid leakage, noise, and conflicts, the graph must support strong logical partitioning.

The Knowledge Graph behaves like a multi-tenant, multi-environment database, with shared + isolated memory zones.

🧩 Partitioning Dimensions¶

Dimension	Examples	Purpose
Tenant	`connectsoft.io`, `client123`, `internal`	Isolate by company or subscription
Environment	`dev`, `test`, `prod`, `qa`, `preview`	Separate volatile vs production memory
Product Line	`booker-v3`, `ai-factory`, `scheduling-core`	Avoid template/prompt collision
Project	`repo:auth-server`, `template:api-gateway`	Scope local trace or index context
Agent Cluster	`vision`, `engineering`, `qa`, `infra`	Limit collaboration zones
Confidentiality	`public`, `internal`, `restricted`, `regulated`	Privacy boundaries for storage and enrichment

🧠 Partition Tags and Metadata¶

Each node/edge includes metadata like:

tenant: connectsoft.io
environment: dev
product: ai-factory
project: user-service
confidentiality: internal

This metadata is used for query scoping, routing, view filtering, and enrichment constraints.

📚 Global vs Scoped Memory¶

Type	Description	Example
Global	Shared prompts, DSLs, libraries	`prompt-generate-domain-model`, `dsl-actor-handler`
Scoped	Project- or tenant-local blueprints, spans	`blueprint-email-verification` (in `booker-v3`)
Hybrid	Base prompt + scoped trace input	Combine `template:multi-tenant-auth` + `client-specific-dsl`

🔐 Partition Isolation Policies¶

Mode	Behavior
Hard Isolation	Tenants cannot read/write across boundaries
Soft Isolation	Cross-tenant nodes allowed via `shared:` tags
Environment Cascade	Test nodes copied to prod if confidence > threshold
Read-Only Mirrors	Dev environments mirror stable prompts from prod
Redaction/Anonymization	Sensitive data is scrubbed when copied to lower environments

🛠️ Agent Awareness of Partitions¶

Agents receive scope metadata at runtime:

{
  "tenant": "client123",
  "environment": "test",
  "project": "template:referral-system"
}

They then:

Query within this scope
Filter graph updates accordingly
Emit traceId that includes tenant/environment suffix
Register feedback separately per environment

🧬 Partitioned Trace Example¶

traceId: trace-user-registration:connectsoft.io:dev
nodes:
  - blueprint-user-registration
  - model-RegisterUserCommand
  - span-user.register
  - test-UserRegister.feature

Nodes are visible only in this environment and tenant context, unless explicitly promoted.

🔁 Promotion & Demotion Paths¶

Action	Description
Promote	Move `test` or `span` from `test` → `prod` if high confidence
Demote	Temporarily exclude flaky blueprint from `prod`
Replicate	Copy validated global prompts into each tenant’s `dev` env
Fork	Tenant forks `template:auth-server` to local scope for customization

🧠 Agent Use Cases¶

Agent	Partition Usage
`CoordinatorAgent`	Orchestrates across environments (e.g., promotes validated features)
`QAAgent`	Works in `test` to validate prod candidates
`PromptAgent`	Resolves prompts from both local and global graph scopes
`SecurityAgent`	Reads only `restricted` and `prod` nodes
`GraphJanitorAgent`	Prunes old `dev` or `qa` artifacts across projects

✅ Summary¶

Partitioning allows:

🧱 Clear separation of tenant and project concerns
🧪 Safe testing and feedback collection
🧠 Efficient context routing and reuse
🔐 Secure multi-environment reasoning with traceable lineage

While agents operate programmatically, humans need intuitive tools to:

Explore trace flows
Understand how features are composed
Debug generation and validation paths
Audit agent behaviors
Identify knowledge gaps or low-confidence regions

Visualization bridges machine reasoning and human comprehension, making the AI Software Factory explainable and auditable.

🖼️ Graph UI Capabilities¶

Feature	Description
Interactive node explorer	View nodes, types, tags, metadata
Edge tracing	Follow paths like `generatedFrom`, `validates`, `emits`
Confidence heatmaps	Color-code nodes by confidence or freshness
Search + filter	Filter by tag, agent, domain, project, traceId
Timeline slider	Animate changes over time or trace progression
Trace replays	Step through how a feature was generated
Partition toggle	Switch between tenants/environments visually
Agent overlay	Highlight which agent created or validated each node

🔍 Example UI Modes¶

Mode	Use Case
Trace Mode	Follow a full feature delivery trace
Prompt Mode	Visualize prompt → blueprint → model flow
Test Coverage Mode	Show test → span → blueprint coverage
Feedback Mode	Show high/low confidence + feedback events
Project Mode	View all nodes in a single repo or product line

🧩 Suggested Tech Stack¶

Component	Tool
Graph Engine	Neo4j, Azure Cosmos DB Gremlin, RedisGraph
Visualization	Cytoscape.js, D3.js, Mermaid, Graphistry, Observable
UI Framework	Blazor (WebAssembly), React, Angular
Graph Service API	REST/gRPC/GraphQL exposed by `GraphService`
Access Layer	Auth via Azure AD, GitHub OAuth, or token scopes

🖥️ Embedded Usage in Dev Portals¶

Graph views should be embed-enabled in:

Agent debug dashboards
DevOps project pages (Azure DevOps Wiki, Backlog)
Generated documentation (MkDocs site overlays)
Internal IDE extensions or VS Code plugin
Web-based memory explorer panel

Developers can explore feature reasoning without reading raw logs or YAML.

🧠 Trace Replayer UI¶

[▶ Step 1] Prompt issued: Generate onboarding flow
 ↓
[▶ Step 2] Blueprint created: blueprint-user-registration
 ↓
[▶ Step 3] Model generated: RegisterUserCommand
 ↓
[▶ Step 4] Test created: UserRegister.feature
 ↓
[▶ Step 5] QAAgent validation failed
 ↓
[▶ Step 6] Prompt regeneration triggered

A timeline view with graph animation shows each step, involved agent, and edge type.

🖌️ Graph Diagram Export¶

Format	Purpose
Mermaid	Markdown + documentation
SVG/PNG	Static embeds
Graphviz/DOT	Complex rendering
JSON-LD	Interoperability with knowledge tools
Neo4j Browser export	Direct exploration in Neo4j UI

🔐 Access Control and Privacy¶

Each graph visualization honors:

Tenant and environment filters
Agent role scopes
Confidentiality tags (e.g., restricted, regulated)
Confidence thresholds (e.g., hide low-confidence noise)

✅ Summary¶

Graph visualization enables:

🧠 Human-friendly debugging and exploration
📈 Metrics and traceability for generation pipelines
🔎 Search, audit, and context comprehension across features
🤝 Collaboration between developers, agents, and orchestrators

In the AI Software Factory, Domain-Specific Languages (DSLs) are the building blocks behind:

🧩 Templates
🧪 Tests
🔁 Orchestration
📚 Documentation
🧠 Agent reasoning

These DSLs include:

dsl:actor-handler, dsl:feature-flow, dsl:retry-policy
dsl:specflow-scenario, dsl:graph-index, dsl:ai-prompt-template

The Knowledge Graph must represent and expose these DSLs as first-class nodes to support:

Discovery
Reuse
Enrichment
Trace linkage
Version evolution

🔤 DSL Node Representation¶

Each DSL is represented as a node with:

nodeType: dsl
nodeId: dsl-actor-handler
domain: backend
language: yaml
examples:
  - templates/handlers/email-handler.yaml
  - tests/actor-handler.feature
agent: DSLDesignerAgent
confidence: 0.95

🔗 DSL Edge Relationships¶

Edge Type	Meaning	Example
`usedBy`	A blueprint, prompt, or template uses this DSL	`blueprint-invite-user → dsl-actor-handler`
`extends`	DSL A is based on DSL B	`dsl-async-handler → dsl-actor-handler`
`composes`	DSL includes fragments of another	`dsl-feature-flow → dsl-actor-handler`, `dsl-telemetry-span`
`validates`	Test DSL or schema matches a target	`dsl-specflow → blueprint-user-registration`

🔍 DSL Discovery Queries¶

Agents and dev tools can query:

findDSLsByDomain("backend")
listDSLsUsedInTemplate("email-service")
searchDSLs("handles commands with async response")
getDSLExampleUsages("dsl-actor-handler")
getDSLDependencies("dsl-retry-policy")
graph.ResolveCompatibleDSLs("span generation")

🧠 DSL-Aware Agents¶

Agent	Purpose
`DSLDesignerAgent`	Generates and registers new DSLs
`PromptGeneratorAgent`	Embeds DSL syntax in prompt structure
`TestAgent`	Validates DSL structure, outputs matching tests
`KnowledgeManagementAgent`	Builds index of DSLs and usage metadata
`GraphAuditorAgent`	Verifies DSLs match templates and prompt grammar

🧪 DSL Use Case in Generation Flow¶

1. Prompt: "Generate actor-based email handler"
2. Graph → matches `dsl-actor-handler` with confidence 0.97
3. Prompt enriched with example DSL usage and constraints
4. Output blueprint references DSL node
5. TestAgent validates conformance to DSL

Each step forms a graph-linked, DSL-driven reasoning path.

🧩 DSL Index View¶

dsl:
  - dsl-actor-handler
  - dsl-telemetry-span
  - dsl-feature-flow
  - dsl-ai-skill-call
  - dsl-graph-index
  - dsl-specflow-scenario

Each DSL is versioned, enriched with:

Examples
Tags (domain:observability, language:yaml)
Used-by lists
Confidence levels
Link to live prompt templates or template files

🔀 DSL Inheritance & Evolution¶

Scenario	Graph Representation
Forked by tenant	`dsl-invite-handler → forkedBy → tenant:ClientX`
Improved by agent	`dsl-actor-handler → version:2.1`
Conflicting versions	Linked via `conflictsWith` → requires manual resolution
Agent-suggested merge	Edge `mergeSuggestedBy → DSLDesignerAgent`

In the dev portal or DSL explorer, devs/agents can:

Browse DSLs by domain/layer
View example usages in real projects
See test coverage and conflicts
Register a new DSL (via agent or UI)
View DSL graph lineage (e.g., extends, composes, forks)

✅ Summary¶

By mapping DSLs in the graph, the platform enables:

🔍 Powerful cross-agent DSL discovery
🧠 Semantic prompt enrichment
🔁 DSL lifecycle and reuse
🔗 Graph reasoning about syntax, constraints, and lineage

📈 Temporal Graph Analysis: Trends, Deltas, and Drift¶

The Knowledge Graph isn't static — it evolves as:

Features are generated and validated
Agents regenerate prompts or spans
Templates, blueprints, and tests are versioned
Confidence decays or increases
DSLs and architectures drift over time

Temporal graph analysis enables:

📊 Change monitoring
🔁 Trace evolution comparison
🧠 Drift detection
⏪ Rollbacks and audits
📈 Trend-based metrics for factory operations

Understanding how the graph changes over time is crucial for reliability, observability, and product lineage.

⏱️ Timestamped Entities¶

Each node and edge stores:

createdAt: 2025-06-01T12:34:00Z
lastUpdatedAt: 2025-06-04T08:21:00Z
lastConfidenceUpdate: 2025-06-05T11:12:00Z

These allow time-scoped queries like:

Changes in the last 24h
Weekly drift analysis
Trace progression over a milestone

📉 Delta and Drift Detection¶

Drift Types:¶

Type	Description	Example
Blueprint–Prompt Drift	Prompt changed but output blueprint no longer aligns
Trace Drift	Test or span removed, causing regression
Model Drift	Entity class structure changed post-generation
DSL Drift	Generated code no longer conforms to DSL
Confidence Drift	Confidence changed > X% without corresponding feedback

Delta Representation:¶

deltaId: delta-user-invite-feature
timestamp: 2025-06-05T10:00:00Z
changes:
  - removed: edge (validates) between span-user.invite and test-InviteUser.feature
  - confidenceDrop: span-user.invite (0.92 → 0.54)

🔁 Temporal Views¶

View	Description
Time Slider	Visualize graph state at past points in time
Delta Overlay	Color-code node/edge additions, removals, and confidence shifts
Trace Replay	Show how a trace evolved during a sprint or feature build
Drift Dashboard	Detect growing misalignment over time

🧠 Agent Behaviors Using Temporal Analysis¶

Agent	Use
`GraphAuditorAgent`	Scan for drift and emit `GraphDriftDetected`
`RegeneratorAgent`	Auto-trigger prompt regeneration for misaligned traces
`GraphJanitorAgent`	Prune abandoned or regressed nodes from stale traces
`CoordinatorAgent`	Analyze feature evolution over milestones
`MetricsAgent`	Publish weekly graph growth/decay stats

📊 Graph Trends & KPIs¶

Track KPIs like:

⚙️ Feature throughput per week
🎯 Prompt success rate over time
🔁 Regeneration frequency
📉 Confidence volatility
📦 DSL usage trends
🧪 Test coverage deltas
📚 Knowledge volume growth per domain

🔄 Snapshot & Rollback¶

The graph supports versioned snapshots:

snapshotId: graph-prod-2025-06-01
Stored as tagged partitions (or in backup vector store)
Enables diffing, rollback, or tenant migration

Agents can request:

compareSnapshots:
  from: graph-prod-2025-05-20
  to: graph-prod-2025-06-01

Returns node/edge adds, removals, and confidence drifts.

✅ Summary¶

Temporal analysis powers:

🧠 Understanding of graph evolution and quality
🔍 Drift detection and automatic repair
📊 Metrics for coordination and factory health
🔁 Snapshotting, rollback, and trend insights

🧠 Knowledge Graph Roles in Agentic Prompt Planning¶

In the ConnectSoft AI Software Factory, agents don’t just emit one-shot prompts — they often:

Plan multi-turn sequences
Chain across agents
Recall past flows
Reuse blueprints, spans, tests, and feedback
Validate prior outputs for reuse or regeneration

The Knowledge Graph acts as a planning substrate, helping agents reason through:

📚 What has been done
🔍 What is missing
🔁 What should be reused, fixed, or extended
🤝 What agents or DSLs should be involved

🧠 Roles the Graph Plays in Prompt Planning¶

Role	Description	Example
Memory	Recall prior prompts, outputs, and traces	Reuse login blueprint
Context Provider	Find relevant examples, spans, or DSLs	Inject `dsl-user-handler` and `test-onboarding.feature`
Constraint Source	Enforce policies, tag filters, confidence gates	Block prompt if last 3 runs failed
Routing Engine	Suggest next agent or skill	After blueprint, trigger `TestAgent`
Evaluator	Validate whether prompt is still aligned	Confidence check of span → test linkage
Planner	Stitch prior nodes to create a generation plan	Generate blueprint → model → test with known gaps

🔍 Prompt Planning Graph Query Patterns¶

1. What’s missing?¶

findMissingEdges:
  from: blueprint-user-registration
  expects:
    - emits → span
    - coveredBy → test
    - documentedBy → prompt

2. What can be reused?¶

findReusableArtifacts:
  domain: onboarding
  tags: [layer:backend, dsl:actor-handler]
  confidence: > 0.8

3. Who should act next?¶

getNextAgent:
  basedOn: currentNode
  missingEdges: [validates, emits]

🤖 Prompt Planning Agents¶

Agent	Graph Use
`PromptPlannerAgent`	Queries graph for what’s missing and reusable
`ContextEnrichmentAgent`	Gathers nearby nodes and injects context
`ChainedPromptAgent`	Uses previous nodes to sequence prompt goals
`CorrectionAgent`	Validates prompt vs trace expectations
`FeatureComposerAgent`	Builds generation plan graph before prompt creation

🧩 Example: Onboarding Flow Generation Plan¶

goal: Generate onboarding feature
input:
  domain: authentication
  tags: [layer:frontend, layer:api]
plan:
  - prompt → blueprint: blueprint-user-onboarding
  - blueprint → emits → span-user.onboard
  - span → validates → test-UserOnboarding.feature
  - prompt → emits → UIComponent: OnboardingForm

Each line maps to graph edge creation or validation.

🛠️ Prompt Assembly via Graph¶

Agents use the graph to:

Pull context: DSLs, previous tests, telemetry
Enrich the prompt dynamically
Apply planning constraints
Generate and emit nodes/edges
Re-enter feedback loop for validation

Prompt generation becomes data-driven and trace-aware, not stateless.

🎯 Prompt Planning Outcomes¶

Benefit	Impact
Context-rich prompts	Higher generation accuracy
Multi-agent orchestration	Smarter handoffs and reuse
Self-correcting plans	Regeneration on drift or failure
Knowledge coverage	Traceable artifact alignment
DSL compliance	Reuse of standards and structure

✅ Summary¶

Prompt planning with the Knowledge Graph enables:

🧠 Smart, sequenced generation
🔁 Feedback-aware flows
🧩 Rich, reusable context
🤖 Coordinated multi-agent collaboration

🧾 Advanced Indexing Patterns for Agents and Coordinators¶

The Knowledge Graph is powerful, but searching and routing at scale require efficient indexing.

Agents must:

Rapidly find reusable assets (DSLs, prompts, spans)
Discover what’s missing in a trace
Be routed to relevant tasks
Access scoped memory by role, domain, or confidence
Filter based on agent capabilities, constraints, or feedback

Indexes are how agents stay focused, fast, and context-aware in real time.

🔠 Types of Indexes¶

Index Type	Description
Prompt Index	Semantic index of prompts by topic, DSL, output type
Blueprint Index	Organized by domain, scope, and agent origin
Test–Span Index	Map from tests to trace spans they cover
Trace Delta Index	Quickly find incomplete or drifted traces
Agent Capability Index	Maps agent → roles → artifacts handled
DSL Usage Index	Which DSLs are used where and how often
Template–Repo Index	Links generated features back to template + Git
Confidence Index	Segments by confidence threshold and decay
Tenant/Environment Index	Partitioned subgraphs for each client/env
Feedback Index	Stores agent + user feedback for scoring and rerouting
Changelog Index	Node-level versioning with diff tracking
Security/Access Index	Controls access by agent role or confidentiality tag

📦 Index Storage Patterns¶

Pattern	Technology	Purpose
Vector Index	Azure Cognitive Search, Qdrant	Prompt + span similarity
Tag Index	Redis Sorted Sets	Fast tag-based lookup
Ontology Index	Neo4j label hierarchy	Domain-specific routing
Event Index	EventStoreDB / Kafka	Temporal search on change events
Blob Index	Azure Blob Tags	Map source files to knowledge graph

🤖 Agent Usage Examples¶

Agent	Index Access Pattern
`PromptPlannerAgent`	Vector + tag + DSL usage index
`QAAgent`	Test–span index, feedback index
`ObservabilityAgent`	Confidence + span + template index
`CoordinatorAgent`	Agent capability + trace delta index
`GraphJanitorAgent`	Confidence + decay + changelog index

🧩 Indexing and Routing Pipeline¶

AgentX receives trace event or request
Queries:
- DSL index → to find structure
- prompt index → to reuse enriched template
- confidence index → to skip weak candidates
Updates:
- feedback index (after evaluation)
- trace delta index (if incomplete)
Emits output → edge → triggers new index update

🔄 Index Update Triggers¶

Trigger	Indexes Updated
Node creation	Prompt, DSL, repo, changelog, capability
Feedback received	Feedback, confidence, delta
Node decay	Confidence, delta, cleanup indexes
Agent role change	Capability, access, routing
DSL update	DSL usage, prompt index, changelog

📊 Index Metrics¶

Track system performance and health via index-based KPIs:

Prompt reuse rate
Test–span coverage %
Feedback-to-generation ratio
Confidence decay over time
Agent throughput per domain
Trace completion velocity

🧠 Coordinator Optimization¶

Coordinators rely on indexes to:

Route high-priority traces
Balance agent load
Avoid redundant generations
Allocate tasks by capability
Ensure coverage goals (e.g., all blueprints validated)

Indexes are the coordination layer’s fast brain.

✅ Summary¶

Advanced indexing provides:

🧠 Fast, scoped access for agents
🤖 Smart routing, feedback loops, and generation reuse
🔁 Coordination and observability across the entire factory
🧩 Rich traceability for governance and evolution

📊 Visualizing Knowledge Coverage and Trace Completeness¶

As the AI Software Factory scales to generate 3000+ microservices across tenants, projects, and domains, we must:

✅ Ensure all necessary artifacts are generated
🧪 Validate test and span coverage
🔎 Detect gaps or missing edges
🔁 Identify drift, regressions, or forgotten features

The Knowledge Graph enables coverage analysis that goes beyond code — it spans:

Prompts
DSLs
Blueprints
Models
Spans
Tests
Docs
Observability
Feedback

🧩 Trace Completeness Model¶

Each trace is evaluated against a blueprint of expectations:

expected:
  - blueprint
  - emits → span
  - validatedBy → test
  - documentedBy → prompt
  - monitoredBy → dashboard
  - describedBy → DSL

The system then generates a completeness report per trace:

traceId: trace-onboarding
completeness:
  score: 0.67
  missing:
    - span-user.onboard
    - dashboard-onboarding-latency
    - test-UserOnboard.feature

📊 Visual Coverage Views¶

View	Description
Trace Score Heatmap	Color-coded completeness per trace/project
Layered Coverage Chart	% of features with span, test, prompt, etc.
Drilldown Dashboards	Click on low-score trace → see what’s missing
Time-Series View	Feature completeness over sprints/releases
Agent Responsibility Matrix	What each agent has/hasn't completed
Tenant/Project Filters	Compare coverage across clients or modules

🎯 KPIs for Coverage¶

KPI	Metric
Trace Coverage Score	% of traces that meet completeness threshold
Span–Test Ratio	How many spans are covered by tests
DSL Usage Coverage	DSLs linked to actual nodes/traces
Confidence Coverage	% of nodes above a confidence floor
Prompt Reuse %	How often prompts are reused vs new
Missing Edge Trends	What relations are often skipped
Trace Repair Velocity	How fast missing elements are resolved

📈 Example: Coverage Radar Chart¶

radar
  title Feature Trace: Onboarding Flow
  "Blueprint" : 1.0
  "Span" : 0.6
  "Test" : 0.5
  "Dashboard" : 0.2
  "Prompt" : 1.0
  "DSL" : 0.8

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

Gaps shown visually → feed into repair flow.

🧠 Agent Support for Coverage¶

Agent	Coverage Role
`TraceAuditorAgent`	Computes trace completeness and emits missing edges
`CoordinatorAgent`	Routes missing pieces to relevant agents
`FeedbackAgent`	Flags gaps based on dev feedback
`GraphJanitorAgent`	Marks stale or incomplete traces for pruning
`TraceRepairAgent`	Regenerates missing test/span/doc

🗂️ Project and Tenant Dashboards¶

Each project/repo/tenant gets:

📈 Trace completeness chart
✅ Coverage badge (e.g., 85% complete)
⏳ Incomplete trace queue
🔁 Auto-regeneration toggles
🧠 Agent status + responsibility breakdown

🧠 DSL + Template Coverage¶

Coverage dashboards also track:

📚 Templates not yet used
🧬 DSLs referenced vs unused
🧠 Prompt–DSL pairing effectiveness
❗ Conflicting or outdated DSL versions

✅ Summary¶

Coverage and completeness metrics allow:

🧩 Early detection of missing artifacts
📊 Progress tracking across projects
🤖 Proactive agent coordination
🔁 Continuous improvement of output quality
📡 Full observability of the factory’s knowledge state

🔁 Agent Training, Feedback, and Confidence Reinforcement Loops¶

The ConnectSoft Knowledge Graph is not just for lookup — it’s the memory and learning substrate for every agent.

To keep generation accurate and aligned, we must:

📥 Collect structured feedback from users, tests, and agents
📈 Adjust confidence dynamically
🧠 Reinforce good outputs and decay bad ones
🔁 Regenerate or replan when quality drops
🤖 Adapt agent behavior based on performance history

The graph becomes the long-term memory and reward signal for an autonomous AI software engineering system.

🔍 Feedback Sources¶

Source	Format	Example
Agent votes	`👍`, `👎` or scores	TestAgent gives `blueprint-login` a 0.7
User validation	UI feedback	"Prompt irrelevant" → -20% confidence
Runtime metrics	Test fails, span not emitted	Drop `span-user.invite` to 0.4
Observability	Alert triggers or usage drops	Dashboard confidence reduced
Coordinator QA	Manual or automatic audits	Incomplete trace gets flagged

📐 Feedback → Confidence Update¶

nodeId: blueprint-user-registration
previousConfidence: 0.88
feedback:
  - agent: TestAgent
    score: -0.5
  - metric: failed 3 of last 5 tests
newConfidence: 0.51

Confidence decay triggers:

Trace reassessment
Optional regeneration
Edge decay or pruning
Metric logging

🧠 Confidence Reinforcement Loop¶

flowchart TD
    A[Agent Generates Node]
    B[Confidence = 0.8]
    C[Used in Test → Passed]
    D[Used in Prod → Telemetry OK]
    E[Feedback: 👍 by QA Agent]
    F[New Confidence = 0.94]
    G[Frozen as Stable Knowledge]

    A --> B --> C --> D --> E --> F --> G

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

Good behavior → confidence increase → candidate for reuse and generalization.

🤖 Agent Adaptation¶

Agents are not static — they learn from the graph.

Examples:¶

Agent	Adaptation
`PromptAgent`	Avoids templates that failed recently
`BlueprintAgent`	Prioritizes DSLs with high success
`RegeneratorAgent`	Triggers prompt retry on confidence < 0.5
`QAAgent`	Focuses test coverage on low-confidence paths
`CoordinatorAgent`	Reallocates tasks from struggling agents

📊 Feedback Metrics¶

Track:

Avg. confidence per agent
Confidence drift velocity
Feedback coverage (how many nodes were evaluated)
Confidence–reuse correlation
Prompt reuse success rate
Agent regeneration impact

🧠 Memory Stability vs Volatility¶

Type	Policy
`Stable` (confidence > 0.9, used in prod)	Write-protected unless regression
`Volatile` (confidence < 0.4, low usage)	Eligible for pruning, regeneration
`Pending` (just generated)	Needs at least 2 validations

🔂 Multi-Agent Feedback Chaining¶

Blueprint generated →
  QAAgent: Test added
    ↓
  Test failed →
    ↓
  Confidence dropped →
    ↓
  Regeneration triggered →
    ↓
  QAAgent re-evaluates →
    ↓
  Confidence adjusted →
    ↓
  Coordinator confirms completion

This is the factory's immune system.

✅ Summary¶

Closing the feedback loop allows:

🧠 Confidence to evolve with usage
🤖 Agents to adapt, prioritize, and retry intelligently
🔁 Continuous quality reinforcement
📊 Transparent, traceable performance evolution

📘 Conclusion and Integration Summary¶

The ConnectSoft Knowledge Graph is not just a datastore — it is the semantic backbone of the entire AI Software Factory. It links artifacts, agents, and memory across time, environments, and projects, powering traceable, intelligent software generation.

🎯 Summary Table¶

Capability	Enabled Outcome
Semantic Linking	Multi-modal reasoning and trace navigation
Temporal Indexing	Drift detection, snapshot diffs, changelog
Confidence + Feedback	Auto-healing and intelligent regeneration
Partitioning	Multi-tenant, project-scoped, secure generation
Traceability	End-to-end visibility from vision → span → test
Planning & Routing	Graph as a planning surface for agents

🔗 Integration with Factory Subsystems¶

Subsystem	Graph Role
`Knowledge & Memory`	Vectorized, embedded memory per node
`Prompt Planning`	Provides context, constraints, reuse
`DSL & Template Registry`	Connects artifacts to structure
`Observability Mesh`	Links telemetry → blueprint → response
`CI/CD Agents`	Annotate build, deploy, and test outcomes
`Documentation System`	Enriches MkDocs with traceable lineage
`Graph Indexing Services`	Feed fast access to agent clusters
`Security + Compliance`	Scoped access via node-level policies

✅ Final Thought¶

The Knowledge Graph transforms ConnectSoft from a code generator into a continuous reasoning system — where memory, validation, and intelligence converge to enable autonomous, trusted software delivery at scale.

🧠 Knowledge Graph¶

🧠 Purpose of the Knowledge Graph¶

📘 What It Is Not¶

🧩 High-Level Structure¶

🌐 Graph Roles in the Factory¶

🎯 Goals of the Graph¶

📌 Summary¶

🧠 Core Concepts: Nodes, Edges, and Embeddings¶

🔹 What Are Graph Nodes?¶

🔗 What Are Graph Edges?¶

📐 Graph Representation (Mermaid)¶

📊 What Are Embeddings?¶

🧬 Composite and Hybrid Nodes¶

📦 Metadata Fields on Nodes¶

✅ Summary¶

🗂️ Node Types Overview¶

📚 Node Type Categories¶

1. Planning & Specification Nodes¶

2. Code & Generation Nodes¶

3. Prompt & Skill Nodes¶

4. Quality & Validation Nodes¶

5. Runtime & Observability Nodes¶

6. Documentation & Memory Nodes¶

🔁 Composite Node Examples¶

🧩 Agent Usage of Node Types¶

✅ Summary¶

🔗 Edge Types and Semantics¶

🔁 Core Edge Types¶

📐 Edge Metadata¶

🕸️ Sample Edge Chain¶

🧠 Agent Use of Edges¶

🧪 Edge Creation Methods¶

✅ Summary¶

🧭 Graph Navigation by Agents¶

🚦 Agent Navigation Modes¶

🧭 Example: Multi-Hop Traversal by QA Agent¶

🔧 Agent Navigation Skills¶

🧩 Use Case Examples¶

📦 Subgraph Templates¶

🧠 Memory-Aware Navigation¶

✅ Summary¶

🔍 Retrieval Patterns from Graph¶

📂 Categories of Retrieval Patterns¶

🔎 1. Direct Lookup¶

🔮 2. Semantic Similarity Search¶

🔗 3. Edge-Based Traversal (Relational)¶

🔁 4. Trace-Based Retrieval¶

🔍 5. Scoped Queries¶

⏱️ 6. Time-Based Queries¶

🏷️ 7. Tag-Based or Property-Based Filters¶

🧪 Sample Combined Query¶

📦 Agent API Support¶

✅ Summary¶

🕸️ Example Subgraph: “User Registered”¶

🗂️ Nodes Involved¶

🔗 Edges and Relationships¶

🧬 Visual Representation (Mermaid)¶

🧠 Embedded Knowledge¶

🧩 Subgraph Utility for Agents¶

🔁 Real-Time Update Flow¶

✅ Summary¶

🔄 Real-Time Graph Updates¶

🔁 Real-Time Update Triggers¶

🧠 Update Flow Overview¶

🧩 Node Update Actions¶

🔗 Edge Update Behavior¶

🛠️ Agent Collaboration on Updates¶

📂 Example Update Chain¶

🔒 Governance and Constraints¶

🧠 Auto-Healing and Edge Rewriting¶

✅ Summary¶

🔄 Graph and Index Synchronization¶

🧩 What Are Knowledge Indexes?¶

🔁 Synchronization Flow¶

🔄 Index-to-Graph Mapping¶

🔄 Graph-to-Index Feedback¶

🧠 Agent Roles in Sync¶

🔧 Change Detection and Delta Update¶

🧩 Sample Use Case¶

✅ Summary¶

🔗 Example: Trace `trace-user-registration`¶