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Key Features for AI-Generated Architecture Diagrams

We want to use an LLM to generate our AI drawings in a declarative way.
By declarative, we want the drawing to specify what should be drawn, how the elements are connected but not focus on the details of drawing the diagrams such as pixel-by-pixel placement or what colors and font types to use. These parameters should be part of the overall site design specification.

We want to store the drawings in a JSON format that is independent of a specific drawing tool.

JSON-Based Diagram Representation

Structured Data Format JSON provides a machine-readable format that AI can easily generate, modify, and version. The schema should include nodes (architectural components), edges (relationships), layout information, and metadata for interactive features.

Standardized Schema Design Develop a consistent JSON schema that captures: - Node properties (type, position, styling, metadata) - Edge definitions (source, target, relationship type, styling) - Layout configurations (clustering rules, positioning constraints) - Interactive elements (hover content, click actions, documentation links) - Versioning metadata (creation date, AI model used, validation status)

AI Generation Compatibility Structure the JSON format to align with how large language models naturally represent architectural concepts, making it easier for AI to generate syntactically correct and semantically meaningful diagrams.

Visual Diff Capabilities for JSON Files

Semantic Change Detection Beyond standard text-based diffs, implement visual diff tools that understand architectural semantics - detecting when services are added/removed, relationships change, or component types are modified.

Side-by-Side Diagram Comparison Render two versions of architecture diagrams simultaneously, highlighting differences through color coding, annotations, and change indicators directly on the visual elements.

Change Impact Analysis AI-powered analysis of JSON diffs to identify cascading effects of architectural changes, such as when a database modification impacts multiple dependent services.

Version History Visualization Timeline-based interface showing how architecture has evolved over time, with the ability to compare any two versions and understand the rationale behind changes.

Smart Clustering Capabilities

Automatic Grouping Logic AI algorithms that analyze node relationships, data flow patterns, and functional domains to automatically cluster related components into logical groupings.

Hierarchical Organization Multi-level clustering that can represent architecture at different abstraction levels - from high-level business domains down to individual microservices and their dependencies.

Dynamic Reclustering Ability to reorganize clusters based on different criteria (technology stack, team ownership, security zones, deployment environments) without losing the underlying architectural relationships.

Cluster Metadata Management Rich information about each cluster including ownership, SLA requirements, security boundaries, and compliance considerations that can be leveraged for automated documentation and governance.

Extended Interactivity Features

Multi-Layer Information Architecture Hover interactions that reveal progressively detailed information - from basic component descriptions to detailed technical specifications, performance metrics, and operational status.

Contextual Documentation Links Smart linking that connects diagram elements to relevant sections of technical documentation, runbooks, API specifications, and architectural decision records (ADRs).

Interactive Filtering and Exploration Dynamic filtering capabilities that allow users to focus on specific technology stacks, teams, environments, or architectural concerns while maintaining context of the broader system.

Guided Navigation Workflows AI-generated pathways through complex architectures that guide users through common exploration patterns like data flow analysis, security review, or impact assessment.

Comprehensive Node Type Library

Infrastructure Components Extensive library of database nodes (relational, NoSQL, graph, time-series), messaging systems (queues, event streams, pub/sub), and storage solutions (object stores, file systems, CDNs).

Application Architecture Elements Microservice nodes, API gateways, load balancers, caching layers, and authentication services with appropriate visual styling and metadata schemas.

Network and Security Boundaries Specialized nodes for firewalls, VPNs, security zones, network segments, and compliance boundaries that can be used to overlay security architecture onto application diagrams.

Monitoring and Observability Status indicator nodes that can display real-time health metrics, alert states, and performance indicators, with the ability to drill down into detailed monitoring dashboards.

Integration Pattern Nodes Visual representations of common integration patterns like circuit breakers, retry mechanisms, saga patterns, and event sourcing components.

Additional Critical Features

Template and Pattern Libraries Pre-built architectural patterns (microservices, event-driven, layered architecture) that AI can use as starting points and customize based on specific requirements.

Validation and Consistency Checking AI-powered validation that ensures architectural diagrams follow organizational standards, detect potential anti-patterns, and verify that all required documentation links are present and valid.

Export and Integration Capabilities Support for exporting diagrams in multiple formats (SVG, PNG, PDF) while preserving interactive elements where possible, and integration with popular documentation platforms and architectural tools.

Collaborative Annotation System Features for multiple stakeholders to add comments, suggestions, and approvals directly to diagram elements, with workflow support for architectural review processes.

Automated Synchronization Capability to automatically update diagrams based on changes detected in source code repositories, infrastructure as code definitions, or service discovery systems.

These features create a comprehensive platform that leverages AI to transform how enterprise architects create, maintain, and interact with architectural documentation, making it both more accurate and more useful for stakeholders across the organization.