Intent and specification files

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+# INTENT
+
+## Purpose
+
+This repository exists to provide an **AI-first, headless knowledge and content engine** for managing, transforming, and operating structured information across heterogeneous data sources.
+
+It enables persistent, service-based knowledge systems that support **efficient research, composition, and reuse of information**.
+
+---
+
+## Primary Utility
+
+The repository provides a **runtime system and service layer** that:
+
+* Manages knowledge as persistent, structured collections across projects and domains
+* Integrates and normalizes data from multiple formats (markdown, documents, datasets, files)
+* Orchestrates transformation workflows, including templating, generation, and analysis
+* Provides APIs and service endpoints for accessing and operating on knowledge
+* Supports AI-driven interaction, automation, and augmentation of knowledge processes
+
+It transforms static content into a **living, operable knowledge system**.
+
+---
+
+## Intended Users
+
+* Developers building knowledge-driven applications and services
+* Infrastructure operators (`adm`) managing knowledge systems and deployments
+* Automation systems (`atm`) orchestrating workflows and transformations
+* LLM agents (`agt`) interacting with and evolving structured knowledge environments
+
+---
+
+## Strategic Role in the System
+This repository is part of a layered knowledge system with clearly separated responsibilities:
+
+- markitect-tool     → makes markdown structured and manipulable
+- **kontextual-engine**  → makes knowledge persistent and operable
+- infospace-bench    → makes knowledge concrete and meaningful
+
+These layers correspond to a deliberate separation of concerns:
+
+* **Syntax layer** — structuring and transforming semi-structured data (markdown)
+* **System layer** — operating, persisting, and orchestrating knowledge
+* **Application layer** — applying knowledge systems to real-world contexts
+
+This repository occupies the **system layer** and should maintain **clear boundaries** to the others.
+
+This repository acts as the **headless knowledge engine layer**:
+
+* It sits above tool-level primitives (e.g. `markitect-tool`)
+* It provides **persistence, orchestration, and access** to knowledge systems
+* It enables **AI-native workflows** over structured and semi-structured data
+* It supports multiple interaction modes: API, service, and agent-driven
+
+It is the **runtime substrate for knowledge systems**, not the tooling layer.
+
+---
+
+## Strategic Boundaries
+
+This repository is **not** intended to:
+
+* Replace low-level tooling for markdown or structured content manipulation
+* Be constrained to markdown as a primary format
+* Define end-user projects, experiments, or domain-specific knowledge spaces
+* Act as a simple CLI toolkit
+
+Such concerns belong to:
+
+* `markitect-tool` (tooling layer)
+* `infospace-bench` (project/workspace layer)
+
+---
+
+## Design Principles
+
+* **AI-first operation**
+  The system is designed for interaction and orchestration by LLM agents
+
+* **Format-agnostic knowledge handling**
+  All data types are supported; markdown may serve as an interaction layer, not a constraint
+
+* **Separation of concerns**
+  Tooling, runtime, and project layers are explicitly decoupled
+
+* **Persistent knowledge state**
+  Knowledge is stored, versioned, and evolved over time
+
+* **Operational composability**
+  Workflows are built from reusable, orchestratable primitives
+
+---
+
+## Maturity Target
+
+A mature version of this repository should:
+
+* Provide a **robust, scalable runtime for knowledge systems**
+* Support **multi-format ingestion, transformation, and retrieval**
+* Enable **fully automated and agent-driven knowledge workflows**
+* Expose stable APIs for integration with external systems
+* Act as the **default engine for AI-driven knowledge management**
+
+---
+
+## Stability Note
+
+Changes to this file represent a **deliberate shift in the system’s role as a knowledge engine and runtime layer**.
+
+Such changes should be made with explicit architectural intent, as they affect all dependent systems and projects.
+
--- a/wiki/FunctionalRequirementsSpecification.md
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+# Kontextual Engine Functional Requirements Specification V0.1
+
+## kontextual-engine
+
+---
+
+## 1. System Overview
+
+kontextual-engine is a **headless knowledge system** that enables persistent storage, transformation, retrieval, and AI-driven operation of structured and semi-structured knowledge across heterogeneous data sources.
+
+This FRS defines the **externally observable functional behavior** of the system.
+
+---
+
+## 2. Actors and Interfaces
+
+### 2.1 Primary Actors
+
+* **User (Human Operator)** via API or service interface
+* **Automation System (`atm`)** executing workflows
+* **LLM Agent (`agt`)** interacting with knowledge and workflows
+* **External Systems** integrating via APIs
+
+---
+
+### 2.2 System Interfaces
+
+* Service API (HTTP, RPC, or equivalent)
+* Programmatic API (SDK/library interface)
+* Storage interface (abstracted from implementation)
+
+---
+
+## 3. Functional Requirements
+
+---
+
+## 3.1 Knowledge Persistence
+
+### FR-001: Store Knowledge Artifacts
+
+**Description:**
+The system must allow storage of knowledge artifacts.
+
+**Input:**
+
+* Structured or semi-structured data
+
+**Output:**
+
+* Persisted knowledge artifact with identifier
+
+---
+
+### FR-002: Retrieve Knowledge Artifacts
+
+The system must allow retrieval of stored knowledge artifacts by identifier or query.
+
+---
+
+### FR-003: Update Knowledge Artifacts
+
+The system must allow modification of existing knowledge artifacts.
+
+---
+
+### FR-004: Delete Knowledge Artifacts
+
+The system must allow removal of stored knowledge artifacts.
+
+---
+
+## 3.2 Knowledge Organization
+
+### FR-010: Group Knowledge into Collections
+
+The system must allow grouping knowledge artifacts into collections or domains.
+
+---
+
+### FR-011: Maintain Relationships
+
+The system must allow defining and retrieving relationships between knowledge artifacts.
+
+---
+
+## 3.3 Ingestion and Normalization
+
+### FR-020: Ingest Multi-Format Data
+
+The system must accept input from multiple data formats (e.g. markdown, documents, files).
+
+---
+
+### FR-021: Normalize Data
+
+The system must convert ingested data into a structured representation usable by the system.
+
+---
+
+## 3.4 Query and Retrieval
+
+### FR-030: Query Knowledge
+
+The system must allow querying knowledge artifacts based on:
+
+* Content
+* Metadata
+* Relationships
+
+---
+
+### FR-031: Return Query Results
+
+The system must return matching knowledge artifacts and associated data.
+
+---
+
+## 3.5 Transformation and Composition
+
+### FR-040: Transform Knowledge Artifacts
+
+The system must allow applying transformations to knowledge artifacts.
+
+---
+
+### FR-041: Compose Knowledge
+
+The system must allow combining multiple knowledge artifacts into derived outputs.
+
+---
+
+## 3.6 Workflow Orchestration
+
+### FR-050: Execute Workflows
+
+The system must allow execution of multi-step workflows on knowledge artifacts.
+
+---
+
+### FR-051: Manage Workflow Dependencies
+
+The system must handle dependencies between workflow steps.
+
+---
+
+### FR-052: Provide Workflow Results
+
+The system must return results of workflow execution.
+
+---
+
+## 3.7 AI Interaction
+
+### FR-060: Support AI-Driven Operations
+
+The system must allow AI agents to:
+
+* Access knowledge
+* Trigger transformations
+* Participate in workflows
+
+---
+
+### FR-061: Maintain Context for AI Interaction
+
+The system must provide contextual information to support AI-driven operations.
+
+---
+
+## 3.8 Integration with External Tools
+
+### FR-070: Integrate with Tooling
+
+The system must allow integration with external tooling (e.g. markitect-tool).
+
+---
+
+### FR-071: Accept External Processing Results
+
+The system must accept outputs from external tools and incorporate them into knowledge.
+
+---
+
+## 3.9 API Interaction
+
+### FR-080: Provide API Access
+
+The system must expose its capabilities through a programmatic interface.
+
+---
+
+### FR-081: Support External Invocation
+
+The system must allow external systems to invoke operations on knowledge.
+
+---
+
+## 3.10 Error Handling
+
+### FR-090: Provide Structured Errors
+
+The system must return structured error information for invalid operations.
+
+---
+
+### FR-091: Avoid Silent Failures
+
+The system must not silently ignore errors affecting correctness.
+
+---
+
+## 4. Functional Constraints
+
+* Functions must be accessible through service interfaces
+* System must support heterogeneous data formats
+* AI-related functions must operate independently of specific providers
+* System must not require CLI-based interaction
+
+---
+
+## 5. Traceability
+
+| PRD Concept                  | FRS Coverage  |
+| ---------------------------- | ------------- |
+| Knowledge persistence        | FR-001–FR-004 |
+| Organization & relationships | FR-010–FR-011 |
+| Ingestion & normalization    | FR-020–FR-021 |
+| Query & retrieval            | FR-030–FR-031 |
+| Transformation & composition | FR-040–FR-041 |
+| Workflow orchestration       | FR-050–FR-052 |
+| AI interaction               | FR-060–FR-061 |
+| Integration                  | FR-070–FR-071 |
+| API access                   | FR-080–FR-081 |
+
+---
+
+## 6. Acceptance Perspective
+
+The system satisfies this FRS when:
+
+* Knowledge can be stored, retrieved, and manipulated via API
+* Queries return expected results
+* Workflows execute and produce observable outputs
+* AI agents can interact with knowledge meaningfully
+* Errors are explicit and traceable
+
--- a/wiki/ProductRequirementsDocument.md
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+# Kontextual Engine Product Requirements Document V0.1
+
+## kontextual-engine
+
+---
+
+## 1. Product Overview
+
+### 1.1 Product Name
+
+**kontextual-engine**
+
+---
+
+### 1.2 Product Definition
+
+kontextual-engine is an **AI-first, headless knowledge and content engine** that manages, transforms, and operates structured information across heterogeneous data sources.
+
+It provides a **persistent, service-oriented runtime for knowledge systems**, enabling automated and agent-driven workflows over structured and semi-structured data.
+
+---
+
+## 2. Product Intent
+
+### 2.1 Problem Statement
+
+Modern knowledge systems face several limitations:
+
+* Content is fragmented across formats, tools, and storage systems
+* Automation and orchestration of knowledge workflows are ad-hoc
+* AI interaction lacks stable, persistent context
+* Systems either focus on tooling (too low-level) or platforms (too rigid)
+
+This results in inefficient knowledge reuse, poor traceability, and limited scalability.
+
+---
+
+### 2.2 Intended Outcomes
+
+kontextual-engine enables:
+
+* Persistent, structured **knowledge environments** across domains
+* Unified handling of **multi-format data and files**
+* AI-driven **interaction, transformation, and orchestration** of knowledge
+* Efficient **retrieval, composition, and reuse of information**
+* Stable APIs for integrating knowledge into applications and systems
+
+---
+
+### 2.3 Success Criteria
+
+The product is successful when:
+
+* Knowledge can be **persisted, queried, and transformed across formats**
+* AI agents can operate on knowledge with **context continuity and efficiency**
+* Workflows can be **automated and orchestrated reliably**
+* Systems can integrate with the engine via **clear, stable interfaces**
+* markitect-tool and other primitives can be used seamlessly within the engine
+
+---
+
+## 3. Scope Definition
+
+### 3.1 In Scope
+
+* Persistent storage and management of structured knowledge
+* Multi-format data handling (markdown, documents, files, datasets)
+* Knowledge ingestion, normalization, and indexing
+* Workflow orchestration for transformation, generation, and analysis
+* API and service interfaces for knowledge access and operations
+* AI/LLM-driven interaction and automation
+* Integration with lower-layer tooling (e.g. markitect-tool)
+
+---
+
+### 3.2 Out of Scope
+
+* Low-level markdown parsing or transformation primitives
+* CLI-first tooling or standalone document manipulation
+* Domain-specific knowledge models or project-level content
+* Visual UI applications (headless system only)
+* Direct ownership of LLM provider integrations (delegated to libraries like `llm-connect`)
+
+---
+
+### 3.3 Boundary Clarification
+
+kontextual-engine provides a **runtime system**, not primitives or projects:
+
+* Tooling primitives → `markitect-tool`
+* Project/application usage → `infospace-bench`
+
+---
+
+## 4. Functional Expectations
+
+### 4.1 Core Capabilities
+
+The product must support:
+
+* **Knowledge Persistence**
+  Store and manage structured knowledge across collections and domains
+
+* **Ingestion & Normalization**
+  Convert heterogeneous data into structured representations
+
+* **Transformation & Composition**
+  Apply workflows to generate, modify, and combine knowledge artifacts
+
+* **Query & Retrieval**
+  Provide efficient access to knowledge via APIs
+
+* **Workflow Orchestration**
+  Coordinate multi-step operations and dependencies
+
+* **AI Interaction Layer**
+  Enable LLM-driven interaction, reasoning, and automation
+
+---
+
+### 4.2 Interaction Modes
+
+* API-first (service endpoints)
+* Agent-driven execution
+* Programmatic integration
+
+---
+
+## 5. Non-Functional Expectations
+
+### 5.1 Performance
+
+* Scalable handling of large and heterogeneous data sets
+* Efficient retrieval and transformation operations
+
+---
+
+### 5.2 Reliability
+
+* Consistent and deterministic system behavior where applicable
+* Robust handling of failures in workflows and external dependencies
+
+---
+
+### 5.3 Extensibility
+
+* Modular architecture supporting plugins and adapters
+* Ability to integrate new data sources and workflows
+
+---
+
+### 5.4 Usability
+
+* Clear API surface for integration
+* Predictable behavior across operations
+* Minimal friction for common workflows
+
+---
+
+## 6. Assumptions and Dependencies
+
+### 6.1 Assumptions
+
+* Knowledge systems benefit from persistent, structured representation
+* AI agents are primary consumers and operators of knowledge workflows
+* Multiple data formats must be supported
+
+---
+
+### 6.2 Dependencies
+
+* markitect-tool for markdown-native operations
+* llm-connect (or equivalent) for LLM integration
+* Underlying storage systems (filesystem, databases, object storage)
+
+---
+
+## 7. Constraints
+
+* Must remain **format-agnostic at the system level**
+* Must maintain **clear separation from tooling and project layers**
+* Must avoid vendor lock-in and provider-specific coupling
+* Must support both deterministic and AI-driven operations
+
+---
+
+## 8. Risks
+
+* Scope creep toward full application/platform ownership
+* Over-complex orchestration reducing usability
+* Tight coupling to specific data formats or tools
+* AI-driven behavior reducing predictability
+
+---
+
+## 9. Related Systems
+
+* **markitect-tool** – syntax layer (markdown primitives)
+* **infospace-bench** – application layer (knowledge projects)
+* **llm-connect** – LLM abstraction layer
+
+---
+
+## 10. Ecosystem Context
+
+This product is part of a layered knowledge system:
+
+```text id="m2k9s4"
+markitect-tool     → makes markdown structured and manipulable
+kontextual-engine  → makes knowledge persistent and operable
+infospace-bench    → makes knowledge concrete and meaningful
+```
+
+Layers:
+
+* **Syntax layer** → markitect-tool
+* **System layer** → kontextual-engine
+* **Application layer** → infospace-bench
+
+---
+
+## 11. PRD Type
+
+**Hybrid / Boundary PRD**
+
+This PRD defines system-level intent and constraints while allowing architectural flexibility and iterative development.
+
+---
+
+# 🧠 Final insight (important)
+
+If markitect-tool was about:
+
+> **“making knowledge manipulable”**
+
+Then kontextual-engine is about:
+
+> **“making knowledge operational”**
+
+That distinction will keep this repo from turning into an unbounded platform.
+