Architecture

1. Architecture at a Glance

Cubyts is an AI-native platform built around a continuously evolving Context Graph. The platform connects to enterprise data and software development systems through pre-built and custom connectors, transforms fragmented information into relationship-aware and dependency-aware context, and exposes that context securely through APIs and MCPs to AI agents, applications, and experiences.

The architecture separates enterprise context from the AI models and experiences that consume it. This allows Cubyts to support multiple foundation models, purpose-built agents, and domain-specific applications while maintaining a consistent, governed source of enterprise context.

2. Architectural Flow

The architecture can be understood as a five-stage flow:

• Connect — collect and synchronize data from enterprise systems through connectors.

• Understand — transform fragmented data into a continuously evolving Context Graph using domain-specific semantics, relationships, dependencies, and reasoning.

• Expose — make governed context and platform capabilities available through APIs and MCPs.

• Reason — apply foundation models, deterministic rules, and probabilistic reasoning.

• Experience — deliver intelligence through diagnostics, agents, search, conversation, and purpose-built applications.

3. AI Models & Fine-Tuning

The AI model layer provides the intelligence used by Cubyts agents and experiences. Cubyts is designed to remain model-provider agnostic, allowing different foundation models to be used according to the task, enterprise requirements, performance, cost, and security needs.

3.1 Model Support

• OpenAI / GPT

• Claude / Anthropic

• Enterprise fine-tuned models (as a service).

3.2 Enterprise Context and Fine-Tuning

Where required, enterprise context can be used to adapt or fine-tune models for specific domains and outcomes. The architecture keeps the model layer decoupled from the Context Graph, so the enterprise context remains reusable even as models evolve.

4. Connectors & Data Sources

The connector layer provides the mechanism for bringing enterprise information into the Cubyts platform. Cubyts supports pre-built connectors as well as custom connectors built on demand.

4.1 Data Sources

• Engineering and source-code systems

• Project and work management systems

• Knowledge bases and documentation

• Collaboration and communication systems

• And more based on customer’s requirements

4.2 Connector Responsibilities

• Secure authentication and authorization to source systems

• Data acquisition and synchronization

• Incremental change detection where supported

• Normalization and metadata capture

• Maintaining data freshness and source provenance

• Supporting enterprise-specific or proprietary systems through custom connectors.

• Connectors are responsible for collecting and synchronizing information; the Context Graph is responsible for turning that information into connected, usable context.

5. Context Graph

The Context Graph is the architectural core of Cubyts. It transforms fragmented enterprise data into a continuously evolving semantic representation of the organization, its entities, activities, relationships, and dependencies.

5.1 Industry-Specific DSL

The Context Graph is structured using an industry- or domain-specific language (DSL) that defines the entities, relationships, semantics, and dependencies relevant to a particular business context. For software development and governance, this can include requirements, features, designs, work items, repositories, code, pull requests, defects, releases, teams, policies, and other SDLC entities.

5.2 Characteristics of the Context Graph

• Live: continuously updated as source systems change

• Relationship-aware: understands how entities are connected

• Dependency-driven: captures dependencies and their implications

• Semantic: represents meaning rather than only raw records

• Contextual: combines information across multiple enterprise systems

5.3 Reasoning

Cubyts combines deterministic and probabilistic reasoning to derive intelligence from the Context Graph.

Deterministic reasoning can be used for:

• Rules and policies

• Compliance conditions

• Thresholds and validations

• Known relationships and dependencies

Probabilistic reasoning can be used for:

• Semantic interpretation

• Classification and inference

• Summarization

• Recommendations

• LLM-based reasoning

Combining both approaches enables Cubyts to use the precision and auditability of deterministic logic together with the flexibility of probabilistic AI.

6. Access Layer

The access layer provides governed interfaces through which applications, agents, and external AI environments can consume Cubyts context and capabilities.

6.1 APIs

Cubyts exposes platform capabilities and context through APIs, enabling applications and services to integrate with the platform without directly accessing underlying source systems.

6.2 MCPs

Model Context Protocol (MCP) interfaces make Cubyts context and capabilities accessible to compatible AI agents and agentic development environments. This enables Cubyts to act as a governed context and capability layer within broader agentic workflows.

6.3 Security, Scalability and Governance

• Authentication and authorization

• Tenant and data isolation

• Access controls and permissions

• Secure interfaces

• Auditability and governance

• Scalable platform services

A core architectural principle is that enterprise context is exposed through controlled interfaces rather than requiring consuming applications or agents to connect directly to underlying source systems.

7. AI Agents & Experiences

The top layer delivers purpose-built intelligence to users and applications. These experiences share the same underlying Context Graph and access layer rather than maintaining separate, disconnected sources of enterprise context.

• Diagnostics: Purpose-built intelligence for identifying conditions, issues, risks, and opportunities.

• Agents: Autonomous or semi-autonomous workflows that reason over enterprise context and perform defined tasks.

• Search: Context-aware retrieval across connected enterprise information.

• Conversation: Natural-language interaction with enterprise context, intelligence, and capabilities.

8. Extensibility

The architecture is designed not only to deliver packaged Cubyts applications, but also to enable organizations to extend the platform for their own domains and use cases.

• Build Agents & Assistants: Design and build purpose-specific AI agents and assistants using governed enterprise context.

• Build Context Graphs: Define industry- or domain-specific DSLs and context models.

• Build Connectors: Extend the platform to proprietary or unsupported enterprise systems.

• Fine-Tune Models: Adapt models using enterprise context for domain-specific accuracy and outcomes.

9. Architectural Principles

10. Cubyts as a Context Platform

The architecture establishes Cubyts as more than an application layer over enterprise systems. The Context Graph acts as a reusable context foundation that can support multiple applications, agents, AI models, and user experiences.

Enterprise systems generate data. Cubyts connects and contextualizes that data. The Context Graph creates a unified understanding of the enterprise. APIs and MCPs make that context available to applications and agents. Models and reasoning capabilities turn context into intelligence, while Cubyts experiences deliver that intelligence to users.

11. Relationship to Cubyts Applications

Cubyts applications such as SDLC Governance, Code Intelligence, Delivery Intelligence, and other agentic experiences are consumers of the underlying Context Engine. They use shared context, relationships, dependencies, reasoning capabilities, and governed access rather than independently reconstructing enterprise context.