Intent and specification files

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.
+