Architecture Decision Document

Project Context Analysis

Requirements Overview

Functional Requirements:

The UI Simple APIs Visual Pipeline Builder enables API producers to create integration pipelines through a visual interface rather than writing Camel YAML directly. The tool targets medium-to-complex integrations where visual pipeline building adds value; simple pass-through proxies will continue using existing mechanisms.

Trigger Types:

Integrations can be triggered by:

HTTP Routes (GET, POST, PUT, DELETE, etc.) - defined by OAS paths
Events - user selects an event type they want to react to; triggered by platform events

The UI presents these trigger types alongside each other (e.g., GET, POST, EVENT) when defining pipelines.

Pipeline Building Blocks:

The visual pipeline supports these step types:

Call API - Invoke backend API from platform catalogue (abstracts Kamelets, handles auth)
Content Transformation - Field renaming, mapping, data restructuring
Choice - Conditional logic (if/else) with simple operators
For Loop - Iterate over arrays/lists
Parallel Execution - Fork to multiple branches (sync/async)
Script - Groovy code for complex transformations
Publish Event - Publish an event to platform event system (can be used in both HTTP-triggered and Event-triggered flows)

These blocks work identically regardless of trigger type (HTTP or Event).

1. Visual Pipeline Construction

Drag-and-drop canvas for building integration flows (top-to-bottom flowchart)
Support for all pipeline blocks listed above
Inline step configuration with expand/collapse capability
Real-time validation as pipelines are built

2. Trigger Definition

HTTP Routes: Upload OpenAPI specification → discover routes from OAS paths → create pipeline per route
Events: User selects from available event types → create pipeline triggered by that event
Both trigger types use same pipeline building blocks and logic

3. Context-Aware Field Mapping

Visual selection of data sources (original request/event payload, previous step outputs)
Dropdown UI for selecting fields from current context using dot notation (e.g., submission.basicInformation.submittedBy.name)
Type awareness with safe conversion inference
Support for nested JSON structures and exchange property management

4. Backend API Integration

Discover available APIs from platform catalogue
“Call API” block abstracts backend calls (hiding Kamelet complexity)
Smart search for API selection with external catalogue linking
Request/response field mapping with dropdown selection per input parameter

5. Event Integration

Event Trigger: User selects event type from available events (input to pipeline)
Publish Event Step: Publish events during pipeline execution (output from pipeline)
Event discovery from platform event registry/catalogue
Event payload structure available in context for field mapping
Works in both HTTP-triggered and Event-triggered pipelines

6. Orchestration Pattern Support

Sequential backend calls: Extract data from one response to use in next call (Domain API pattern)
Single backend enrichment: Call backend, transform, return (common pattern)
Parallel execution: Fork to multiple pipelines (sync/async) with result aggregation
Conditional logic: Simple operators (equals, not equals, greater than) with static values or data field selection
Loop iteration: Iterate over arrays/lists with current item available in context
Error handling: Try/catch blocks or error routing
Content transformation: Field renaming, mapping, formatting
Event publishing: Publish events to platform (trigger downstream systems, fire-and-forget or with acknowledgment)

7. Code Generation

Generate valid Camel YAML matching established patterns (direct routes, Kamelets with unique routeIds, subroutes)
Follow HIP platform best practices (learned from Domain APIs and existing integrations)
Ensure unique Kamelet routeIds per invocation (critical for Camel routing correctness)
Support reusable pattern library (direct subroutes for common operations)
Generate both REST DSL (HTTP) and Event consumer DSL (Events)

8. Testing & Deployment

Test pipeline before deployment (mock data or sandbox environment)
Integration with MR Worker for deployment via ClickOps
Support versioning and deployment through environments

Use Case Spectrum:

Complexity	Example	Tool Used	Pattern
Simple	Pass-through proxy with auth	Existing mechanism (not this tool)	Kong config only
Medium	Field mapping, single backend call, publish event	Visual Pipeline Builder	Request → Transform → Backend → Publish Event → Response
Complex	Multi-backend orchestration (Domain APIs), event-driven workflows	Visual Pipeline Builder	Event → Backend A → Extract → Backend B → Publish Event → Response

The Visual Pipeline Builder targets medium-to-complex integrations where:

Content transformation is needed beyond simple pass-through
Multiple backend calls require orchestration
Event-driven processing is required (consume events, publish events, or both)
Conditional logic and loops are needed

Example Flows:

HTTP → API → Publish Event: POST endpoint receives submission → stores in backend → publishes “submission.received” event
Event → API → Response: “submission.approved” event → enriches from customer API → stores result → publishes “notification.required” event
HTTP → Parallel (API + Publish Event): POST endpoint → parallel branches: (1) store in backend, (2) publish event for async processing → merge responses

Non-Functional Requirements:

Correctness
- Generated Camel YAML must be syntactically valid and semantically correct
- Must handle Camel-specific constraints (unique route IDs, explicit CamelHttpMethod between calls, header management)
- Exchange property flow must be correct for sequential orchestration
- Support both HTTP and Event trigger mechanisms
- Correctly generate event consumer and producer DSL
Usability
- Clear value proposition: Medium-complexity integrations should be significantly easier to build visually than writing YAML
- Intuitive UI for non-Camel experts
- Clear visualization of data flow and context availability
- Helpful error messages and validation feedback
- Low learning curve for API producers familiar with REST APIs
- Event selection and publishing feels natural and integrated
Maintainability
- Generated YAML should follow established patterns for consistency
- Encourage reuse patterns (subroutes) over inline duplication
- Support for updating pipelines without breaking existing integrations
Security & Compliance
- Part of Critical National Infrastructure platform
- Audit trail for pipeline changes
- Integration with existing HIP security model (Keycloak, network policies)
Performance
- Real-time validation without blocking UI
- Fast pipeline rendering for complex flows
- Efficient code generation

Scale & Complexity:

Primary domain: Web frontend prototype (Loveable) + Camel YAML code generation engine
Complexity level: High - Must handle medium-to-complex use cases elegantly with proper abstractions
Estimated architectural components:
- Frontend: Canvas renderer, pipeline builder UI, field selector, validation engine, trigger type selector, event selector
- Code generation: Camel YAML template engine, Kamelet abstraction layer, routeId uniqueness manager, trigger adapter (HTTP vs Event), event DSL generator
- Integration: API catalogue connector, OAS parser, event registry/catalogue connector, MR Worker client
- Testing: Mock data provider, sandbox execution environment, event simulator

Open Questions & Future Considerations

The following items require further exploration and decision-making after initial prototype validation:

1. Field Mapping UX Enhancement

Current: Two-level dropdown (source → field)
Consider: Autocomplete with dot notation for faster field selection
Tradeoff: Discoverability vs. speed for experienced users

2. Error Handling Patterns

Current: CHOICE blocks with IF branches for conditional error routing
Consider: Explicit “On Error” branch visualization or try/catch patterns
Question: Does CHOICE pattern sufficiently cover error handling needs, or should error handling be more visually distinct?

3. Parallel Execution Visualization

Current: Parallel block concept exists
Detail needed: How to show sync vs. async? How to visualize branch merging?
UX consideration: Branching visualization for parallel paths

4. Complex Field Transformations

Current: Transform block with field mapping
Question: How to handle complex transformations (arrays, nested objects, computed fields)?
Consider: Groovy script templates and examples, visual array manipulation tools (filter, map, reduce)

5. Additional Step Types

Review if additional blocks needed beyond current set: Call API, Transform, Choice, Loop, Parallel, Script, Publish Event, Response
Consider: Delay/Wait, Aggregate, Split, Filter, etc.
Question: What patterns emerge from user feedback that aren’t covered?
Question: Do we allow them to provide camel yaml as a block as a fallback?
Question: How/do we allow producers to create and share blocks themselves?

6. Non-Functional Requirements (NFRs)

Should pipelines define NFRs (max latency, min throughput, error rate thresholds)?
Where in UI would this be specified? Pipeline-level configuration?
How would NFRs be validated/enforced in production?

7. API Catalogue Integration (Production)

Must APIs exist in Hub catalogue before use?
Allow inline OAS/WSDL upload for new APIs not yet catalogued?
Auto-publish uploaded specs to catalogue?
Discovery workflow for production environment

Technical Constraints & Dependencies

Platform Constraints:

HIP Platform Integration
- Must integrate with existing Gateway and Camel orchestration layer via Simple API deployment APIs
- Target medium-to-complex integrations (simple pass-through continues using existing approach)
Event-Driven Architecture Support
- Event Consumption (Trigger): Integrate with event catalogue system to allow definition of pipelines that consume events
- Event Publishing (Step): Support “Publish Event” as pipeline step block (available in all pipelines regardless of trigger type)
- Event payloads become pipeline input context (similar to HTTP request body)
- Same pipeline blocks and logic for both HTTP and Event triggers
- Event schema discovery for both consumption and publishing
- Event acknowledgment and error handling patterns
Camel YAML DSL Requirements

These were observed during Domain APIs and platform experience, and should be reviewed.

Unique route IDs across all route files
Explicit CamelHttpMethod header between sequential HTTP calls
removeHeaders: pattern: "CamelHttp*" between backend calls
Groovy scripts require specific dependencies: camel-groovy, groovy-json:4.0.29
Query parameters arrive as HTTP headers in Camel REST DSL
Kamelet invocations MUST include unique routeId per call (format: kamelet:<name>/<unique-route-id>?params)
Support REST DSL (HTTP), Event consumer DSL (event triggers), and Event producer DSL (Publish Event step)

Integration Pattern Support
- Single backend enrichment (call, transform, return)
- Multi-backend orchestration (sequential or parallel)
- Exchange property management for passing data between steps
- XML-to-JSON transformation support (for legacy backends)
- Response combination using Groovy
- Error handling and fallback patterns
- Event publishing (fire-and-forget or with acknowledgment)

External Dependencies:

API Catalogue: Platform service for discovering available backend APIs (OAS Discovery Service)
Event Registry/Catalogue: Discovery of event types and schemas (for both consumption and publishing)
MR Worker: Deployment service for Simple API YAML configurations
Keycloak: Authentication for API producers
Kong Gateway: Runtime execution of HTTP-triggered integrations
Event Platform: Runtime execution of event-triggered integrations and event publishing (part of HIP EDA)
Apache Camel: Underlying orchestration engine (Camel YAML DSL on JBang)

Technology Constraints (TBC):

Camel YAML DSL: Must generate valid Camel 4.x YAML (not Java DSL)
Groovy 4.0.29: For transformation scripts within generated YAML
OpenAPI 3.0+: For OAS parsing and HTTP route discovery
Event Schemas: Schema format for event payload structure (JSON Schema, Avro, AsyncAPI, or platform-specific)

Cross-Cutting Concerns Identified

Trigger Abstraction
- Unified pipeline model: Same blocks and logic work for HTTP and Event triggers
- Trigger-specific input: HTTP requests vs Event payloads as initial context
- Trigger selector UI: Clear presentation of GET/POST/EVENT options when defining pipelines
- Schema discovery: OAS for HTTP, event schemas for Events (both consumption and publishing)
Event Integration (Bidirectional)
- Event Consumption: Selecting event type as trigger, schema-driven context
- Event Publishing: “Publish Event” block with event type selection, field mapping from current context to event payload
- Event discovery: Unified interface for browsing available events (similar to API catalogue for backend APIs)
- Schema-driven mapping: Both consumed and published events use schemas for field selection
Context Flow Management
- Track available data at each pipeline step
- Manage exchange properties for sequential orchestration
- Provide clear visualization of what fields are accessible where
- Handle both structured (HTTP request/event payload) and dynamic (step outputs) context
- Support “Publish Event” step accessing current context for payload construction
- Critical for correct field mapping and preventing runtime errors
Validation & Error Prevention
- Real-time YAML syntax validation
- Semantic validation (routeId uniqueness, property references, field paths)
- Type checking for field mappings
- Prevent common Camel mistakes (missing CamelHttpMethod, header conflicts)
- Trigger-specific validation (HTTP status codes vs event acknowledgment)
- Event payload validation against schemas (both consumption and publishing)
Code Generation Quality
- Consistency with HIP platform best practices (informed by Domain APIs lessons)
- Encourage reuse patterns (subroutes) over duplication
- Readable generated YAML with comments
- Support REST DSL (HTTP), Event consumer DSL (triggers), and Event producer DSL (Publish Event step)
- Version tagging for tracking changes
API Catalogue Integration
- Discover available backend APIs for “Call API” blocks
- Fetch API schemas for field mapping
- Handle authentication configuration (abstract away from user)
- Link to external catalogue for API documentation
Event Catalogue Integration
- Discover available event types (for both consumption and publishing)
- Fetch event schemas for field mapping (payload structure)
- Present event fields similarly to HTTP request fields in context dropdown
- Support searching/filtering events by domain or category
- Link to event documentation (if available)
Testing & Debugging
- Pre-deployment testing with mock data (HTTP requests, event payloads, or both)
- Sandbox environment for safe execution
- Clear error messages mapping back to visual pipeline
- Test case management for regression testing
- Support testing HTTP-triggered, Event-triggered, and hybrid pipelines
- Simulate event publishing without actually sending events to platform
Versioning & Lifecycle
- Pipeline version control (Git integration via MR Worker)
- Deployment through environments (dev → qa → prod)
- OAS and event schema versioning handling
- Backward compatibility for existing integrations
- Handle schema evolution for events (forward/backward compatibility)
Security & Audit
- Audit trail for pipeline creation/modification
- Integration with platform authentication (Keycloak)
- Secret management for backend credentials (handled by egress gateways, not in UI)
- Event publishing authorization (what events can this integration publish?)
- Event consumption authorization (what events can this integration consume?)
- Compliance with CNI requirements
User Experience
- Progressive disclosure (hide Camel complexity, expose when needed)
- Context-sensitive help and documentation
- Error recovery (save draft pipelines, undo/redo)
- Copy/paste pipeline blocks for reuse
- Clear trigger type selection: Visually distinguish HTTP routes from Event triggers
- Intuitive event selection: Searchable event picker for both consumption and publishing
- Template library: Pre-built patterns for common scenarios:
  - HTTP → API → Response
  - HTTP → API → Publish Event → Response
  - Event → API → Publish Event
  - Event → Parallel (API + Publish Event)
  - API → Transform → Publish Event

Prototype Scope & Objectives

Purpose

This is a clickable prototype to validate UX concepts and visual pipeline building patterns. The focus is on demonstrating the user experience and gathering stakeholder feedback, not building production-ready infrastructure.

Primary Goal

Validate that visual pipeline building is:

Intuitive for API producers (non-Camel experts)
Capable of representing complex orchestration patterns (Domain API use case)
Clearer than writing Camel YAML directly
Worth investing in full production implementation

Scope Boundaries

In Scope (Prototype Demonstrates):

Visual pipeline builder UI with drag-and-drop or add/remove capabilities
Route selection (GET, POST, EVENT) and configuration
Pipeline step manipulation (add, remove, reorder steps)
Step types: Call API, Transform, Choice, Loop, Parallel, Script, Publish Event, Response
Context-aware field selection (dropdowns showing available data at each step)
Mock data for API catalogue, events, and field selection
Complex orchestration example (VPD Domain API with sequential backend calls)
Event trigger selection and event publishing in pipelines
Branching and nesting for conditional logic
Simulated pipeline testing/execution visualization

Out of Scope (Deferred to Production):

Backend code generation service
Actual Camel YAML generation and validation
Integration with HIP platform (API Catalogue, MR Worker, Event Registry)
Pipeline execution or testing in real environment
Persistent storage or state management beyond browser session
User authentication or authorization
Multi-user collaboration
Deployment workflows
Versioning and deployment workflows (handled by MR Worker in production)
Monitoring and tracing integration (production runtime concern)
Test case management and regression testing
Production credential management

Success Criteria

Prototype validates UX if:

✅ Stakeholders can understand pipeline flow without Camel knowledge
✅ Complex orchestrations (like Domain API) are visibly clearer than YAML
✅ Field mapping and context flow are intuitive
✅ Event integration feels natural alongside HTTP routes
✅ API producers express confidence they could build integrations with this tool
✅ Team agrees to invest in production implementation
✅ Branching/nesting for conditionals is clear and manageable

Prototype fails validation if:

❌ Visual representation is more confusing than YAML
❌ Critical pipeline patterns can’t be expressed visually
❌ Context flow is unclear or confusing
❌ Too much cognitive overhead to use the tool
❌ Feedback indicates writing YAML would be easier
❌ Nested branches become unmanageable or confusing

Visual Reference

UI Mocks: .ai/projects/hip/ui-simple-apis/v1.0-mocks/

Core UI Screenshots:

new-route.png - Empty pipeline canvas with route selection
route-selected.png - POST route with incoming request body shown
steps.png - Complete pipeline with multiple steps and step type menu
link-to-expand.png - CHOICE step with nested branch (collapsed view)
expanded-detail.png - Inside a branch with breadcrumb navigation
select-field.png - Field mapping dropdown showing context sources

Key UI Elements:

Left Panel: Route selector showing POST, GET, EVENT with descriptions
Main Canvas: Top-to-bottom pipeline flow
Collapsible Sections: Incoming request, individual steps, response
Step Type Menu: “Add step” button reveals available step types
Step Cards: Numbered steps with type badge, title, description, expand/collapse
Breadcrumb Navigation: For navigating into/out of branches (e.g., “Pipeline > Validation Failed”)
Context Dropdowns: Show available data sources (Incoming Request, Previous Steps, Static Value, Script)

Key UX Patterns from Mocks

Branching and Nesting Pattern

Evidence from Mocks: link-to-expand.png, expanded-detail.png

The prototype demonstrates a nested branching pattern for CHOICE steps:

Collapsed State (link-to-expand.png):

Step 2: “CHOICE - Check Validation”
Shows: “Branch based on validation result”
Nested branch shown as: “IF - Validation Failed”
Link indicator: “1 step in this branch >”
Condition displayed inline: Validate & Calculate (Step 1).valid equals false

Expanded Branch State (expanded-detail.png):

Breadcrumb navigation: “Pipeline > Validation Failed”
Shows badge: ”■ Validation Failed”
Step 1 inside branch: “TRANSFORM - Build Error Response”
“Add step” button within branch
”■ End of branch” marker

Key UX Insights:

Inline Condition Configuration
- Condition shown directly in collapsed CHOICE step
- Three-part condition: [Source Field] [Operator] [Value]
- Example: Validate & Calculate (Step 1).valid | equals | false
Branch Navigation
- Click “1 step in this branch >” to navigate into branch
- Breadcrumb shows context: “Pipeline > Validation Failed”
- Shows you’re inside a conditional branch
Branch Isolation
- Branch has own step numbering (Step 1, Step 2… within branch)
- “Add step” works within branch context
- Clear “End of branch” marker
Context Awareness
- Fields from main pipeline available in branch
- Example: Step 1’s valid field used in condition
- Branch steps can access parent context

This pattern answers several architectural decisions:

✅ Decision 3 (Step Configuration): Inline expansion confirmed - conditions shown directly in collapsed state
✅ Decision 7 (Step Ordering): Not shown in mocks - defer or implement simply
✅ Decision 9 (Error Handling): CHOICE block handles it - “IF Validation Failed” branch pattern

Architectural Implication:

The nesting pattern requires:

State management for current navigation context (main pipeline vs inside branch)
Breadcrumb rendering
Context tracking across nested levels
Branch step isolation (separate numbering, separate “Add step” context)

Prototype Design

Visual Pipeline Builder Interface

The prototype presents a three-panel layout for building integration pipelines:

Left Panel: Route Selector

Displays available routes from uploaded OAS specification
Shows GET, POST, and EVENT triggers in a unified list
Each route shows HTTP method badge, path, and description
Selecting a route loads its pipeline configuration in the main canvas

Main Canvas: Pipeline Flow

Top-to-bottom flowchart visualization
Collapsible “Incoming Request” section showing request structure (parameters, body, headers)
Sequential pipeline steps displayed as numbered cards
“Add step” button between sections to insert new steps
Collapsible “Response” section at bottom showing response structure

Step Type Menu

Appears when “Add step” is clicked
Available step types:
- CALL API - Invoke backend API from catalogue
- TRANSFORM - Rename or map fields
- CHOICE - Conditional branching (if/else)
- SCRIPT - Custom Groovy script
- PARALLEL - Parallel execution branches
- LOOP - Iterate over a list (array/list field from context; current item available as context source in each iteration)
- PUBLISH EVENT - Publish event to platform
- RESPONSE - Map fields to API response

Call API Step Configuration

When configuring a Call API step:

API Selection:

APIs are selected from the platform API catalogue
For prototype: Mock catalogue with predefined APIs (excise, customer, tax-platform)
Open question (production): Must APIs exist in catalogue before use? Or allow inline OAS/WSDL upload for new APIs?

API Details Display:

API name and description
Link to full API documentation (external catalogue)
SLA Information (if available):
- Expected response time
- P50/P99 latency percentiles
- Max throughput supported
- Current availability status
Filtering Requirements: Some APIs may have filtering requirements (security scopes, permissions). Prototype shows where this information would appear.

Input Mapping:

List of required parameters from API schema
Field mapping dropdown for each parameter (from context)
Support for both simple fields and nested paths

Step Configuration Pattern

Steps use inline expansion for configuration:

Click step card → expands to show configuration fields
All configuration visible in main canvas (no modals or drawers)
Collapse/expand to manage visual complexity
Keeps pipeline flow visible at all times

For branching structures (CHOICE steps), the interface uses breadcrumb navigation:

Collapsed view shows branch condition inline (e.g., Validate & Calculate (Step 1).valid equals false)
Link indicator shows “1 step in this branch >”
Clicking navigates into branch with breadcrumb: “Pipeline > Validation Failed”
Branch has isolated step numbering and “Add step” context
“End of branch” marker indicates return to main flow

Context-Aware Field Selection

Field mapping uses a dynamic dropdown system showing available data sources:

Available Sources:

Incoming Request (or Event payload for event-triggered flows)
Previous Steps (e.g., “Validate & Calculate (Step 1)”)
Static Value - for hardcoded values
Script (Groovy) - for computed values

As pipeline steps are added, the context grows dynamically. Each field mapping dropdown shows:

First dropdown: Select source (Incoming Request, Step 1, Step 2, Static Value, Script)
Second dropdown: Select field from that source (supports dot notation for nested fields)

This pattern enables users to visually trace where data comes from and how it flows through the pipeline.

Array and List Handling:

The field selection system supports working with arrays in multiple ways:

Direct array field selection: Select entire array field for passing to another step
Array element access: Use Loop step type to iterate over array elements
Array item fields: Within Loop context, access fields of current array item using dot notation
Future consideration: Visual array transformation tools (filter, map, reduce operations)

Event Integration

Events are treated alongside HTTP routes in the route selector:

Mixed list showing GET, POST, EVENT triggers
Visual distinction via badges (GET, POST, EVENT)
Same pipeline builder for both HTTP and Event triggers
“Publish Event” step available in all pipelines (HTTP or Event-triggered)

Publishing events follows the same pattern as calling APIs:

Select event type from event catalogue
Map context fields to event payload schema
Events are fire-and-forget but return basic status code
Status can be used in subsequent CHOICE conditions

Pipeline Manipulation

Reordering Steps:

Drag-and-drop handles on step cards
Visual feedback during drag operation
Drop indicators show valid placement positions

Step Management:

Delete button on each step card (trash icon)
Expand/collapse toggle on each step
Inline editing of step configuration

Mock Data Structure

The prototype uses structured JSON files for realistic demonstration:

routes.json - OAS routes (GET, POST, EVENT triggers)
api-catalogue.json - Available backend APIs (excise, customer, tax-platform)
events-catalogue.json - Available events to consume/publish
example-pipelines.json - Pre-built examples (VPD Domain API, event-driven flows)

This structure allows easy updates to examples and maintains separation between UI logic and demonstration data.

Example Pipelines

The prototype demonstrates three complexity levels:

1. Simple GET (Blank Template)

Empty pipeline showing basic structure
Demonstrates route selection and adding first step

2. Complex Orchestration (VPD Domain API - POST)

Multi-backend sequential calls
Conditional branching (validation check)
Field extraction and mapping between steps
Response assembly from multiple sources
Demonstrates: Call API, Choice, Transform, Response

3. Event-Driven Flow (To be added)

Event trigger (e.g., “submission.approved”)
Enrich data from backend API
Publish new event (e.g., “notification.required”)
Demonstrates: Event integration and publishing

Testing Simulation

The “Test pipeline” button triggers simulated execution visualization:

Test Execution Flow:

User provides mock request data (or uses example from OAS)
Pipeline executes in simulation mode
Each step highlights as it “executes”
Step outputs displayed in-context

Visibility During Test:

Step-by-step execution: Visual highlighting of active step
Input/Output inspection: Expand any step to see its input data and output result
Context evolution: See how context grows as pipeline executes
HTTP request visibility (future): Show actual HTTP requests made to backend APIs (mock or real)
Final response: Display complete response structure

Authentication (Production Consideration): For production testing with real backends:

Users would need credentials with appropriate scopes
Could create/update test applications for isolated testing
Prototype uses mock data, no real authentication required

Test Result Management (Future):

Save test scenarios for regression testing
Compare results across runs
Out of scope for prototype

This demonstrates the value of testing before deployment without requiring actual backend integration.

Appendix: Loveable Implementation

Current Prototype Implementation: Loveable Platform

Platform: Loveable (https://lovable.dev)
Live Prototype: https://cle-mock-5.lovable.app/

Technology Stack

Loveable provides:

React 18+ with TypeScript
Tailwind CSS for styling
Vite build tooling
Hot module replacement
Built-in deployment and hosting

Implementation Notes

State Management:

React Context API or useState for prototype state
No persistence needed (browser session only)

Data Mocking:

Mock data in JSON files or components
No backend API calls required
All logic runs in browser

Component Organization:

Standard Loveable project structure
Components, pages, hooks, utils folders
Tailwind for styling

Techcle Wiki

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