AI Native Development toolkit for Microsoft Dynamics 365 Business Central implementing the AI-Native Instructions Architecture framework with 37 specialized Agent Primitives across 3 systematic layers. Features AL Orchestra multi-agent TDD system that transforms feature requests into code following BC best practices.
v2.8.0 - Workflow Simplification: Direct workflow (User → al-architect → al-conductor)
⚡ 5-Minute Setup → Complete Quick Start Guide
Option 1: VS Code Extension (Recommended)
1. Install from Marketplace: Search "AL Development Collection"
2. Open Command Palette (Ctrl+Shift+P)
3. Run: AL Collection: Install Toolkit to Workspace
Option 2: NPM Package
# Install via npm
npm install github:javiarmesto/AL-Development-Collection-for-GitHub-Copilot
npx al-collection install
# Or use directly with npx
npx al-development-collection installCLI Commands:
# Install to current or specified directory
npx al-development-collection install [path]
# Update existing installation (merge mode)
npx al-development-collection update
# Validate installation
npx al-development-collection validate
# Get help
npx al-development-collection --helpFeatures:
- ✅ Auto-detects AL projects - Searches for app.json automatically
- ✅ Interactive selection - Choose from multiple projects if found
- ✅ Smart merging - Preserves existing files, only adds new ones
- ✅ Validation - Built-in installation verification
Option 3: Clone & Install
For development or customization:
git clone https://github.com/javiarmesto/AL-Development-Collection-for-GitHub-Copilot.git
cd AL-Development-Collection-for-GitHub-Copilot
node install.js install [your-al-project]Then: Reload VS Code (Ctrl+Shift+P → Developer: Reload Window)
Recommendation: Use VS Code Extension for easiest setup. Use npm CLI for automation. Use Clone for development.
📖 Follow the complete example: Customer Loyalty Points System
Or start directly:
New project?
@workspace use al-initializeHave a feature to build?
Use al-architect mode
[Describe your requirements]Need debugging?
Use al-debugger mode
[Describe the issue]This collection implements the AI Native-Instructions Architecture framework with 3 systematic layers that transform ad-hoc AI usage into reliable, repeatable engineering:
graph TB
Dev[👤 Developer] --> Layer1[Layer 1: Markdown Prompt Engineering]
Layer1 --> |Structured Instructions| Layer2[Layer 2: Agent Primitives]
Layer2 --> |Context Optimization| Layer3[Layer 3: Context Engineering]
Layer2 --> Instructions["📋 Instructions Files (Auto-applied guidelines)"]
Layer2 --> Agents["💬 Agents (Role-based specialists)"]
Layer2 --> Prompts["🎯 Prompts-Agentic Workflows (Task execution)"]
Layer3 --> Modular["Modular Loading (applyTo patterns)"]
Layer3 --> AGENTSMD["AGENTS.md Standard (Universal portability)"]
Instructions --> Copilot[🤖 GitHub Copilot]
Agents --> Copilot
Prompts --> Copilot
style Layer1 fill:#9C27B0
style Layer2 fill:#2196F3
style Layer3 fill:#4CAF50
style Copilot fill:#FF9800
| Layer | Focus | Purpose | AL Implementation |
|---|---|---|---|
| Layer 1 | Markdown Prompt Engineering | Structured instructions using semantic markdown | Headers, lists, links for AI reasoning |
| Layer 2 | Agent Primitives | Configurable tools (Instructions, Modes, Prompts) | 7 Instructions + 11 Agents + 18 Workflows + 1 Guide |
| Layer 3 | Context Engineering | Strategic context management via applyTo patterns |
Modular loading, AGENTS.md compilation ready |
| Primitive Type | Count | Activation | Purpose |
|---|---|---|---|
| Instructions | 9 files | Auto via applyTo patterns |
Persistent coding rules & standards |
| Agents | 7 files | Use [mode-name] |
Role-based strategic/tactical specialists |
| Orchestra System | 4 files | Use al-conductor |
Multi-agent TDD orchestration |
| Agentic Workflows | 18 files | @workspace use [name] |
Complete task execution processes |
| Total Primitives | 37 tools | — | Complete AI Native toolkit with TDD |
Markdown Prompt Engineering implemented as modular .instructions.md files with applyTo patterns:
Always Active (apply to **/*.al):
- al-guidelines - Master hub referencing all patterns
- al-code-style - Code formatting & feature-based structure
- al-naming-conventions - PascalCase standards & 26-char limits
- al-performance - SetLoadFields, early filtering, temporary tables
Context-Activated (apply based on context):
- al-error-handling - TryFunctions, error labels, telemetry (
applyTo: **/*.al) - al-events - Event subscribers, integration events (
applyTo: **/*.al) - al-testing - AL-Go structure, test generation (
applyTo: **/test/**/*.al)
Integration & Coordination:
- copilot-instructions - Master coordination document (auto-loaded as
.github/copilot-instructions.md) - index - Complete instructions catalog and usage guide
💡 Context Engineering: Instructions use
applyTofrontmatter for selective loading, optimizing context window usage
Systematic processes as .prompt.md files with validation gates and tool access:
General Workflows - Invoke with @workspace use [name]:
- al-initialize - Complete environment and workspace setup (consolidated)
- al-diagnose - Runtime debugging and configuration troubleshooting (consolidated)
- al-build - Build, package, publish workflows
- al-events - Event subscriber/publisher implementation
- al-performance - Deep performance analysis with CPU profiling
- al-performance.triage - Quick performance diagnosis and static analysis
- al-permissions - Permission set generation
- al-migrate - BC version upgrade workflows
- al-pages - Page Designer integration
- al-spec.create - Functional-technical specifications
- al-pr-prepare - Pull request preparation (streamlined template)
- al-translate - XLF translation file management
Context & Memory Workflows - AI assistant continuity:
- al-context.create - Generate project context.md file for AI assistants
- al-memory.create - Generate/update memory.md for session continuity
Copilot Workflows - Specialized for AI-powered features:
- al-copilot-capability - Register new Copilot capability with enum extension and setup
- al-copilot-promptdialog - Create complete PromptDialog page with all areas
- al-copilot-test - Comprehensive testing with AI Test Toolkit
- al-copilot-generate - Generate Copilot code from natural language
💡 Agentic Workflows: Prompts orchestrate primitives into complete processes with human validation checkpoints.
Role-based specialists with MCP tool boundaries (like professional licensing):
Strategic Specialists (design & analyze):
- al-architect 🏗️ - Solution architecture, cannot execute builds (START HERE)
- al-debugger 🐛 - Deep diagnosis, systematic troubleshooting
- al-tester ✅ - Testing strategy, TDD methodology
- al-api 🌐 - RESTful API design & implementation
- al-copilot 🤖 - AI-powered Copilot features development
Tactical Specialist (implement & execute):
- al-developer 💻 - Code implementation with full build tool access
💡 Tool Boundaries: Each mode has explicit CAN/CANNOT lists preventing cross-domain security breaches
Multi-Agent TDD Orchestration adapted from GitHub Copilot Orchestra with AL-specific enhancements:
Main Agent:
- al-conductor 🎯 - Orchestrates Planning → Implementation → Review → Commit cycle
Specialized Subagents (called automatically by conductor):
- al-planning-subagent 🔍 - AL-aware research and context gathering
- al-implement-subagent ⚙️ - TDD-focused implementation (RED → GREEN → REFACTOR)
- al-review-subagent ✅ - Code review against AL best practices
Key Features:
- 🧪 TDD Enforcement: Failing tests first, then minimal code, then refactor
- 🎨 Event-Driven Validation: Prevents base BC object modifications
- 📋 Auto-Documentation: Generates plan files in
.github/plans/ - 🚦 Quality Gates: Automated review before each commit
- 💰 Cost-Optimized: Uses Haiku for implementation, Sonnet for strategic work
Usage:
Use al-conductor mode
Add email validation to Customer table with these requirements:
- Use regex pattern matching
- Allow empty emails (optional in BC)
- Show user-friendly error messagesResult: Multi-phase plan → TDD implementation → Quality validation → Documentation trail
📖 Complete Orchestra Documentation
💡 When to Use: Complex features (3+ phases), production code requiring quality gates, bug fixes with TDD proof, or learning AL through guided implementation
Centralized documentation system ensuring all agents share context and maintain consistency:
Documentation Location: .github/plans/
Core Documents:
architecture.md- System architecture decisions and patternsspec.md- Functional and technical specificationstest-plan.md- Test strategy and coverage requirementsmemory.md- Session history and decision rationale
Agent Integration:
Orchestra Agents (context-aware collaboration):
- al-conductor: Reads architecture/spec upfront, passes context to subagents
- al-planning-subagent: Documents research findings for conductor's plan creation
- al-implement-subagent: Aligns implementation with architecture/spec/test-plan
- al-review-subagent: Validates compliance against all context documents
Specialist Agents (context-aware design):
- al-developer: Reads all context docs before coding, ensures consistency
- al-api: Produces
<endpoint>-api-design.mddocumenting contracts and integration - al-copilot: Generates
<feature>-copilot-ux-design.mdcovering AI prompts and UX
Key Benefits:
- ✅ Consistency: All agents reference the same architectural decisions
- 📚 Knowledge Transfer: New agents inherit project context automatically
- 🔄 Session Continuity: Memory system preserves decisions across interactions
- 🎯 Quality Assurance: Review validates against documented requirements
- 📖 Auto-Documentation: Design decisions captured for team reference
Workflow Example:
1. Use al-architect mode → Creates architecture.md
2. @workspace use al-spec.create → Generates spec.md
3. Use al-conductor mode → Reads docs, implements with subagents
4. al-review-subagent → Validates against architecture.md + spec.md
5. Result: Consistent implementation aligned with designDocument Templates Available:
- API Design:
docs/templates/api-design-template.md - Copilot UX:
docs/templates/copilot-ux-design-template.md - Architecture: Generated by al-architect during planning
- Specifications: Generated by al-spec.create workflow
💡 Best Practice: Always create architecture.md and spec.md before using al-conductor for medium/high complexity features
NEW: Visual guide for choosing the right workflow based on task complexity:
📖 Complete Development Flow Documentation
This comprehensive guide includes:
- Decision Tree - Automated routing based on complexity level (Simple/Moderate/Complex/Specialized)
- 3 Development Patterns :
- Pattern 1: Simple Features (direct implementation)
- Pattern 2: Moderate Features (TDD with multi-phase planning)
- Pattern 3: Complex Features (architecture + specialized design)
- Specialized Flows for specific scenarios:
- Flow A: API Development (REST/OData)
- Flow B: AI/Copilot Features (prompt engineering)
- Flow C: Performance Optimization (profiling + refactoring)
- Real Examples from Customer Loyalty Points test case
💡 Quick Start: Unsure which agent or workflow to use? Check the decision tree in the complete flow guide!
- copilot-instructions.md - Master document coordinating all 37 primitives with usage guidance
📖 New to the collection? Check the Complete Development Flow Guide for visual decision trees and pattern selection!
Have a feature to implement? The collection uses automatic complexity classification with validation gates:
- Describe your requirements (document, specs, or plain description)
- System analyzes and infers complexity: 🟢 LOW / 🟡 MEDIUM / 🔴 HIGH
- You confirm the classification (🚦 VALIDATION GATE - mandatory)
- Auto-routes to the appropriate agent/workflow
⚠️ Experimental & Customizable Classification: This routing system is an experimental approach that you can customize by editing framework files (README.md,agents/index.md,instructions/copilot-instructions.md) in your repository. Complexity is assessed by scope, integration depth, and architectural impact—not by counting AL objects. Modify the criteria, thresholds, and routing paths to match your team's expertise and project requirements.
| Complexity | Domain | Scenario Description | Agent Route | Why This Path |
|---|---|---|---|---|
| 🟢 LOW | 🎯 Standard | Simple field addition, basic validation, single UI change | al-developer |
Direct implementation - scope is clear, no design needed |
| 🟢 LOW | 🐛 Bug Fix | Known issue with clear reproduction steps | al-debugger → al-developer |
Diagnose root cause first, then implement fix with tests |
| 🟢 LOW | ✅ Test Addition | Adding tests to existing well-structured code | al-tester → al-developer |
Design test strategy, then implement test cases |
| 🟡 MEDIUM | 🏗️ Feature | Business logic with data flow, internal integrations | al-conductor |
TDD orchestration ensures quality across 2-3 phases |
| 🟡 MEDIUM | 🌐 API | RESTful endpoints, OData pages, internal API exposure | al-api → al-conductor |
Design API contract first, then implement with TDD |
| 🟡 MEDIUM | 🤖 Copilot Feature | Copilot capability, PromptDialog, basic AI integration | al-copilot → al-conductor |
Design AI UX and prompts, then build with quality gates |
| 🟡 MEDIUM | 🐛 Complex Bug | Intermittent issue requiring systematic investigation | al-debugger → al-conductor |
Diagnose with profiling, then fix with comprehensive tests |
| 🔴 HIGH | 🏛️ Architecture | Multi-module feature, new patterns, broad impact | al-architect → al-conductor |
Design architecture first, then orchestrate TDD implementation |
| 🔴 HIGH | 🌐 Integration | External APIs, OAuth, Azure services, webhooks | al-api → al-architect → al-conductor |
API design → System architecture → Secure TDD implementation |
| 🔴 HIGH | ⚡ Performance | System-wide optimization, architectural bottlenecks | al-architect → al-conductor |
Analyze architecture impact, design optimization strategy |
Quick examples:
- 🟢 LOW: "Add Priority field to Sales Header" →
al-developer - 🟡 MEDIUM: "Customer loyalty points with calculation" →
al-conductor - 🔴 HIGH: "External API integration with OAuth and retry logic" →
al-api→al-architect→al-conductor
Start here if unsure:
Use al-architect mode
I need to [describe your requirement]al-architect will analyze requirements, design the solution architecture, and recommend the appropriate workflow (al-developer for simple features, al-conductor for complex TDD implementation).
📊 See complete routing matrix and decision tree
1. @workspace use al-diagnose → Diagnose and debug
2. @workspace use al-performance → Profile (if needed)
3. Fix (auto-guidelines active)
4. Use al-tester mode → Test strategy
1. Use al-architect mode → Design API contract
2. Use al-api mode → Implement REST/OData
3. @workspace use al-permissions → Security
4. Use al-tester mode → API test suite
5. @workspace use al-build → Deploy
📖 Complete API Flow with examples
1. Use al-copilot mode → Design AI capability
2. @workspace use al-copilot-capability → Register
3. @workspace use al-copilot-promptdialog → UI
4. @workspace use al-copilot-test → Validate
📖 Complete Copilot Flow with examples
- AI Native-Instructions Architecture Implementation - Framework compliance details
- AI Native-Instructions Framework Guide - Complete framework reference
- AGENTS.md Standard - Universal context format
- Collection Overview - User-facing guide
- Instructions Index - Complete guide to all instruction files
- Prompts Index - Complete guide to all agentic workflows
- Agents Index - Complete guide to all agents
- Integration Guide - Master coordination document
- Visual Studio Code Insiders
- AL Language extension for Business Central
- GitHub Copilot extension (active subscription)
- Business Central development environment (sandbox recommended)
- Node.js 14+ (for validation script)
This collection leverages Model Context Protocol (MCP) servers to enhance GitHub Copilot's capabilities with specialized tools:
| Server | Purpose | Key Tools |
|---|---|---|
| al-symbols-mcp | AL object analysis from compiled .app packages (by Stefan Maron) | al_search_objects, al_get_object_definition, al_find_references, al_search_object_members, al_get_object_summary, al_packages |
| context7 | Up-to-date library documentation retrieval | resolve-library-id, get-library-docs |
| microsoft-docs | Official Microsoft/Azure documentation search | microsoft_docs_search, microsoft_code_sample_search, microsoft_docs_fetch |
MCP servers are configured in .vscode/mcp.json:
{
"servers": {
"al-symbols-mcp": {
"type": "stdio",
"command": "npx",
"args": ["al-mcp-server"]
}
}
}The MCP tools are integrated into specific agents based on their specialization:
al-symbols-mcp tools assigned to:
- al-planning-subagent - All 6 tools for comprehensive AL research
- al-developer - All tools via wildcard for implementation
- al-architect - Search, definition, references, summary for design analysis
- al-debugger - References, definition, members for troubleshooting
Prompts with al-symbols-mcp:
- al-events - Object search and references for event implementation
- al-diagnose - References and definitions for debugging
- al-spec.create - Object search and summary for specifications
💡 Note: al-symbols-mcp requires compiled
.appfiles in.alpackagesdirectory. Ensure you have downloaded symbols (al_download_symbols) before using these tools.
Validate the collection before contributing:
# Install dependencies
npm install
# Run validation
npm run validateExpected output:
✅ Collection is fully compliant and ready for contribution!
User: "Add email validation to Customer table"
Classification: 🟢 LOW
- Scope: Limited (single table)
- Integration: None
- Route: al-developer mode
Result: Field + validation implemented in ~5 minutesUser: "Build customer loyalty points system"
Classification: 🟡 MEDIUM
- Scope: Moderate (Customer + Sales + Loyalty)
- Integration: Internal (event subscribers)
- Route: al-architect → al-conductor (TDD Orchestra)
Workflow:
1. al-architect designs architecture (20 min)
2. al-conductor orchestrates implementation:
- Planning subagent researches BC objects
- Implement subagent executes 7 TDD phases
- Review subagent validates quality gates
3. Result: 10 AL objects, 63 tests, 100% passing (90 min)User: "Integrate with external payment gateway (OAuth + webhooks)"
Classification: 🔴 HIGH
- Scope: Extensive (Sales + Finance + Security + API)
- Integration: External (REST API, OAuth 2.0)
- Route: al-api → al-architect → al-conductor
Workflow:
1. al-api designs API contract (1 hour)
2. al-architect plans system architecture (2 hours)
3. al-conductor implements with TDD (1-2 days)
4. Security review + performance profilingFound an issue or have a suggestion?
-
Report Issues
- Use GitHub Issues
- Provide specific examples
- Include error messages
-
Suggest Improvements
- New prompts for common tasks
- New modes for specialized scenarios
- Better patterns and examples
-
Contribution Guidelines
- Follow file naming conventions
- Include proper frontmatter
- Update manifest file
- Run validation before submitting
See CONTRIBUTING.md for detailed guidelines.
AI Native-Instructions Architecture with 37 Agent Primitives across 3 layers:
AL-Development-Collection-for-GitHub-Copilot/
├── .github/ # GitHub integration
│ ├── ISSUE_TEMPLATE/ # Issue templates
│ ├── PULL_REQUEST_TEMPLATE.md # PR template
│ └── copilot-instructions.md # Master coordination (symlink)
├── instructions/ # 📋 Layer 1: Instructions (9 files)
│ ├── index.md # Complete guide
│ ├── copilot-instructions.md # Master integration guide
│ ├── al-guidelines.instructions.md # Master hub (applyTo: **/*.{al,json})
│ ├── al-code-style.instructions.md # Code style (applyTo: **/*.al)
│ ├── al-naming-conventions.instructions.md # Naming rules
│ ├── al-performance.instructions.md # Performance patterns
│ ├── al-error-handling.instructions.md # Error handling
│ ├── al-events.instructions.md # Event-driven patterns
│ └── al-testing.instructions.md # Testing (applyTo: **/test/**/*.al)
├── prompts/ # 🎯 Layer 2: Agentic Workflows (18 files)
│ ├── index.md # Complete workflows guide
│ ├── al-initialize.prompt.md # Environment & workspace setup
│ ├── al-diagnose.prompt.md # Debug & troubleshoot
│ ├── al-build.prompt.md # Build & deployment
│ ├── al-events.prompt.md # Event implementation
│ ├── al-performance.prompt.md # Deep profiling
│ ├── al-performance.triage.prompt.md # Quick analysis
│ ├── al-permissions.prompt.md # Permission generation
│ ├── al-migrate.prompt.md # Version migration
│ ├── al-pages.prompt.md # Page Designer
│ ├── al-spec.create.prompt.md # Specifications
│ ├── al-pr-prepare.prompt.md # Pull request prep
│ ├── al-translate.prompt.md # XLF translation
│ ├── al-context.create.prompt.md # Context file generation
│ ├── al-memory.create.prompt.md # Memory file generation
│ ├── al-copilot-capability.prompt.md # Copilot capability registration
│ ├── al-copilot-promptdialog.prompt.md # PromptDialog creation
│ ├── al-copilot-test.prompt.md # AI Test Toolkit integration
│ └── al-copilot-generate.prompt.md # Natural language to code
├── agents/ # 💬 Layer 2: Agents (6 + 4 files)
│ ├── index.md # Complete agent guide
│ ├── al-architect.agent.md # 🏗️ Solution architecture (START HERE)
│ ├── al-developer.agent.md # 💻 Tactical implementation
│ ├── al-debugger.agent.md # 🐛 Deep debugging
│ ├── al-tester.agent.md # ✅ Testing strategy
│ ├── al-api.agent.md # 🌐 API development
│ ├── al-copilot.agent.md # 🤖 AI features
│ └── orchestration/ # 🎭 Orchestra System (4 files)
│ ├── README.md # Complete Orchestra docs (23k chars)
│ ├── index.md # Quick reference
│ ├── al-conductor.agent.md # Main orchestration agent
│ ├── al-planning-subagent.agent.md # AL-aware research
│ ├── al-implement-subagent.agent.md # TDD implementation (Haiku 4.5)
│ └── al-review-subagent.agent.md # Quality validation
├── docs/ # Documentation mirrors
│ ├── agents/ # Agent documentation
│ ├── instructions/ # Instructions documentation
│ ├── prompts/ # Prompts documentation
│ └── workflows/
│ └── complete-development-flow.md # Visual workflow guide
├── collections/ # Collection manifests
│ └── al-development.collection.yml # Main collection (37 primitives)
├── references/ # Framework documentation
│ ├── AI Native-INSTRUCTIONS-ARCHITECTURE.md # Framework compliance
│ ├── ai native-concepts.md # Core concepts
│ └── ai native structure.md # Structure guide
├── archive/ # Historical files
│ ├── README.md # Archive documentation
│ ├── MIGRATION-FROM-ORCHESTRATOR.md # Migration guide (v2.8.0)
│ └── al-orchestrator.agent.md # Archived: Smart router (deprecated v2.8.0)
├── al-development.md # Framework overview
├── CONTRIBUTING.md # Contribution guidelines
├── CHANGELOG.md # Version history
├── LICENSE # MIT License
├── SECURITY.md # Security policy
├── validate-al-collection.js # Compliance validation
├── package.json # Dependencies
├── QUICK-START.md # Quick start guide with complete example
├── REPRODUCIBLE-EXAMPLE.md # Step-by-step reproducible example
└── README.md # This file
# Total: 37 Agent Primitives (9 instructions + 18 workflows + 6 agents + 4 orchestra)
# AGENTS.md compilation ready (Layer 3: Context Engineering)
# All primitives use applyTo patterns for modular context loading
- 🎯 Simplified Workflow - Reduced from 3-4 steps to 2 steps
- Removed al-orchestrator routing layer (archived)
- Direct workflow: User → al-architect (design) → al-conductor (implement)
- 40% reduction in decision fatigue
- 📚 Example-Based Learning - Single validated reproducible example
- QUICK-START.md with complete Customer Loyalty Points walkthrough
- REPRODUCIBLE-EXAMPLE.md with step-by-step instructions
- Replaced multiple incomplete examples with one comprehensive case
- 📖 Migration Guide - MIGRATION-FROM-ORCHESTRATOR.md for existing users
- Before/after workflow comparison tables
- Decision tree without orchestrator
- FAQ for common migration questions
- 📊 Total Primitives - 37 tools (9 + 18 + 6 + 4) - orchestrator archived
- 🎓 Better Onboarding - 5-minute setup → working example in <2 hours
- ✅ Validation - Customer Loyalty Points test case (24/24 validations passed)
- 📋 Agent Context System - Centralized documentation in
.github/plans/- Core documents: architecture.md, spec.md, test-plan.md, memory.md
- All orchestra agents read and reference shared context
- Specialist agents (developer, API, Copilot) produce design documents
- 🔄 Session Continuity - Memory system preserves decisions across interactions
- 📚 Knowledge Transfer - Context automatically shared between agents
- 🎯 Quality Consistency - Review validates against documented requirements
- 📖 Auto-Documentation - Design decisions captured in structured templates
- 🏗️ Enhanced Collaboration - Conductor delegates with full context awareness
- ✅ Phase 2 Complete - All orchestration subagents and specialists context-aware
- 📊 Total Primitives - 38 tools unchanged (9 + 18 + 7 + 4)
- 🎓 Production Ready - Context system validated across orchestra workflow
- 🎯 Routing Matrix Optimization - Streamlined from 14 to 10 essential paths
- Removed edge cases (AI System, Migration) for clarity
- Enhanced with 5-column format: Domain | Scenario | Route | Rationale
- Added domain icons for visual identification (🎯🐛✅🏗️🌐🤖⚡)
- 📊 Complexity Classification System - Scope-based assessment framework
- 5-question framework (Scope, Integration, Logic, Phases, Impact)
- Removed all object count references (conceptual purity)
- 🚦 Validation Gate Protocol - Mandatory user confirmation before routing
- Experimental & customizable by teams (documented adaptation process)
- 🧪 Test Validation - Complete framework testing with Customer Loyalty Points
- 24/24 validations passed (100% success rate)
- 7-phase TDD workflow validated
- Test execution documented in
.local/test-execution-results-2025-11-09.md
- 📚 Workshop Materials Updated - Complete workshop v2.0 ready
- Added complexity assessment module (30 min)
- Added customization workshop module (30 min)
- 6 practice classification exercises with solutions
- Updated to 7 parts, 4h 45min total duration
- 📊 Total Primitives - 38 tools unchanged (9 + 18 + 7 + 4)
- 🎓 Framework Maturity - Production-ready with test validation
- 🎭 AL Orchestra System - NEW multi-agent TDD orchestration (4 agents)
- al-conductor: Main orchestration agent for Planning → Implementation → Review → Commit cycle
- al-planning-subagent: AL-aware research and context gathering
- al-implement-subagent: TDD-focused implementation with full AL MCP tools (uses Haiku 4.5 for cost optimization)
- al-review-subagent: Code review against AL best practices with CRITICAL/MAJOR/MINOR severity levels
- 🧪 TDD Enforcement - Strict RED (failing tests) → GREEN (minimal code) → REFACTOR cycle
- 🎨 Event-Driven Validation - Prevents base BC object modifications (extension patterns only)
- 📋 Auto-Documentation - Generates plan files in
.github/plans/with complete audit trail - 🚦 Quality Gates - Automated review before each commit with AL-specific checks
- 💰 Cost-Optimized - Uses Haiku for implementation, Sonnet for strategic work (30-40% cost reduction)
- 📊 Total Primitives - 38 tools (9 instructions + 18 workflows + 11 agents)
- 📖 Complete Documentation - 23k-character adaptability analysis, 15k-character Orchestra guide
- 🔄 Backward Compatible - All existing agents work unchanged, Orchestra is additive
- 🎯 Framework Alignment - Implements GitHub Copilot Orchestra pattern adapted for AL development
- 💻 al-developer Mode - NEW tactical implementation specialist
- Full MCP tool access (al_build, al_publish, al_incrementalpublish)
- Executes code changes, builds, and tests
- Bridges gap between strategic modes and implementation
- Delegates architectural decisions to specialized modes
- 📄 al-context.create Workflow - Generate project context.md files
- 16-section comprehensive project documentation
- AI assistant onboarding in <2 minutes
- Architecture, decisions, navigation guide
- 🧠 al-memory.create Workflow - Session continuity tracking
- 12-section development memory log
- Decision tracking with rationale
- Problem/solution patterns documentation
- TODO and learning journal
- 📊 Total Primitives - 32 tools (7 instructions + 18 workflows + 7 modes + 1 guide)
- 🎯 Cognitive Focus - Complete separation: Think (architect) → Do (developer) → Validate (tester)
- 🏗️ Enhanced Workflows - Better integration between strategic and tactical work
- 🤖 AL Copilot Mode - Complete rewrite with 3x more content (1,280 lines)
- Quick Start guide (5-minute first Copilot)
- Complete PromptDialog patterns (all 5 areas)
- Modern patterns (SetManagedResourceAuthorization, AI Test Toolkit)
- Real-world examples from Lab1_3 repository
- Advanced prompt engineering techniques
- Responsible AI implementation patterns
- 🎯 New Copilot Workflows - 3 specialized prompts added (15 total workflows)
- al-copilot-capability: Register Copilot capability
- al-copilot-promptdialog: Create PromptDialog pages
- al-copilot-test: Test with AI Test Toolkit
- 📚 al-orchestrator - Converted to GUIDE/EXAMPLE for agent creation
- 📊 Total Primitives - 29 tools (7 instructions + 15 workflows + 6 modes + 1 guide)
- 🏗️ AI Native Development - Full compliance maintained across all new content
- 🔄 Consolidated Workflows - Reduced from 14 to 11 workflows (-21%)
- 🎯 al-initialize - Merged al-setup + al-workspace into single initialization workflow
- 🐛 al-diagnose - Merged al-debug + al-troubleshoot into unified diagnostics
- ✂️ Streamlined Templates - al-pr-prepare reduced from 509 to ~240 lines
- ❌ Removed Redundancy - Eliminated al-workflow (overlapped with specialized prompts)
- 📊 Total Primitives - 25 tools at v2.3 (reduced from 28 at v2.2)
- 💡 Clearer Purpose - Each workflow now has distinct, non-overlapping functionality
- 📉 Context Efficiency - Reduced total lines by ~30% while maintaining functionality
- 🏗️ Framework Implementation - Full AI Native-Instructions Architecture compliance
- 📋 Layer 1: Markdown Prompt Engineering - Structured semantic markdown throughout
- 🔧 Layer 2: Agent Primitives - 28 configurable tools (Instructions, Modes, Prompts)
- 🎯 Layer 3: Context Engineering -
applyTopatterns for modular context loading - 📖 AGENTS.md Ready - Prepared for universal context compilation
- 🔒 Tool Boundaries - Agents with explicit CAN/CANNOT security lists
- ✅ Validation Passing - All 34 framework compliance checks passing
- ✨ Streamlined agents - Archived 5 duplicate modes
- 🎯 Focused toolkit - 6 core strategic agents
- 📊 Updated to 24 tools - Clearer separation of concerns
- 📚 Enhanced documentation - Practical examples integrated
- ✅ Full compliance with contribution guidelines
- ✅ All files renamed to proper extensions
- ✅ Collection manifest created
- ✅ Validation script added
- 🎉 Initial release
- 📦 7 instructions, 10 prompts, 6 modes
MIT License - see LICENSE file for details.
javiarmesto
- GitHub: @javiarmesto
- Created: 2025-01-15
- Microsoft Business Central team for AL language
- GitHub Copilot team for extensibility
- AL development community for patterns and best practices
- Shep Alderson for the GitHub Copilot Orchestra pattern
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Documentation: al-development.md
Status: ✅ AI Native-Instructions Architecture Compliant
Framework: AI Native-Instructions Architecture
Last Updated: 2025-11-14
Collection ID: al-development
Version: 2.8.0
Total Primitives: 37 (9 instructions + 18 workflows + 6 agents + 4 orchestra)
Installation: npm CLI (recommended) | Clone & Install
Context Standard: AGENTS.md ready
Latest: Workflow Simplification - Direct User → al-architect → al-conductor path