# Alfred Agent Integration Template

## 4-Step Workflow Integration

### Step 1: Intent Understanding (User Requirement Analysis)
```python
# Pattern for Alfred agent to analyze user requests
def analyze_user_intent(request: str) -> TestIntent:
 """
 Analyze user's test request to establish AI test strategy

 Parameters:
 - request: User request ("Test this web app", "Cross-browser testing needed")

 Returns:
 - TestIntent: Analyzed test intent and strategy
 """
 
 intent_patterns = {
 'comprehensive_testing': [' ', ' ', ' '],
 'regression_testing': [' ', ' ', ' '],
 'cross_browser': [' ', ' ', ''],
 'performance_testing': [' ', ' ', ''],
 'visual_regression': ['UI ', ' ', ' ']
 }
 
 # AI 
 analyzed_intent = ai_intent_analyzer.analyze(request, intent_patterns)
 
 return TestIntent(
 primary_goal=analyzed_intent['goal'],
 test_types=analyzed_intent['types'],
 priority=analyzed_intent['priority'],
 context=analyzed_intent['context']
 )
```

### Step 2: Plan Creation (AI )
```python
# Context7 MCP AI 
async def create_ai_test_plan(intent: TestIntent) -> TestPlan:
 """
 Context7 MCP AI 
 
 :
 - Playwright 
 - AI 
-
 """
 
 # Context7 Playwright 
 latest_patterns = await context7_client.get_library_docs(
 context7_library_id="/microsoft/playwright",
 topic="enterprise testing automation patterns 2025",
 tokens=5000
 )
 
 # AI 
 ai_strategy = ai_test_generator.create_strategy(
 intent=intent,
 context7_patterns=latest_patterns,
 best_practices=enterprise_patterns
 )
 
 return TestPlan(
 strategy=ai_strategy,
 context7_integration=True,
 ai_enhancements=True,
 enterprise_ready=True
 )
```

### Step 3: Task Execution (AI )
```python
# AI 
class AITestExecutor:
 """AI """
 
 def __init__(self):
 self.context7_client = Context7Client()
 self.ai_orchestrator = AITestOrchestrator()
 
 async def execute_comprehensive_testing(self, test_plan: TestPlan) -> TestResults:
 """
 Context7 MCP AI 
 
 :
 1. AI 
 2. Context7 
3.
 4. AI 
5.
 """
 
 # Step 1: AI-powered test generation
 smart_tests = await self.ai_orchestrator.generate_smart_tests(test_plan)
 
 # Step 2: Context7 pattern application
 enhanced_tests = self.apply_context7_patterns(smart_tests)
 
 # Step 3: Execute across browsers
 cross_browser_results = await self.execute_cross_browser(enhanced_tests)
 
 # Step 4: Visual regression with AI
 visual_results = await self.ai_visual_regression_test(cross_browser_results)
 
 # Step 5: Performance analysis
 performance_results = await self.ai_performance_analysis(visual_results)
 
 return TestResults(
 functional=cross_browser_results,
 visual=visual_results,
 performance=performance_results,
 ai_insights=self.generate_ai_insights(performance_results)
 )
```

### Step 4: Report & Analysis (AI )
```python
# AI 
async def generate_ai_test_report(results: TestResults) -> AIReport:
 """
 AI Context7 
 
 :
 - AI 
 - Context7 
-
-
 """
 
 # AI 
 ai_analysis = await ai_analyzer.analyze_test_results(results)
 
 # Context7 
 context7_validation = await validate_context7_application(results)
 
 # 
 recommendations = await ai_recommender.generate_recommendations(
 test_results=results,
 ai_analysis=ai_analysis,
 context7_validation=context7_validation
 )
 
 return AIReport(
 summary=create_executive_summary(results),
 detailed_analysis=ai_analysis,
 context7_insights=context7_validation,
 action_items=recommendations,
 next_steps=generate_next_steps(recommendations)
 )
```

## Alfred Multi-Agent Coordination

### 
```python
# Alfred 
class AlfredAgentCoordinator:
 """Alfred """
 
 def __init__(self):
 self.debug_agent = "moai-essentials-debug"
 self.perf_agent = "moai-essentials-perf"
 self.review_agent = "moai-essentials-review"
 self.trust_agent = "moai-foundation-trust"
 
 async def coordinate_with_debug_agent(self, test_failures: List[TestFailure]) -> DebugAnalysis:
 """
 
 
 :
 - AI 
 - Context7 
-
 """
 
 debug_request = {
 'failures': test_failures,
 'context': 'webapp_testing',
 'ai_enhanced': True,
 'context7_patterns': True
 }
 
 # 
 debug_result = await call_agent(self.debug_agent, debug_request)
 
 return DebugAnalysis(
 root_causes=debug_result['root_causes'],
 suggested_fixes=debug_result['fixes'],
 confidence_score=debug_result['confidence']
 )
 
 async def coordinate_with_performance_agent(self, performance_data: Dict) -> PerformanceOptimization:
 """
 
 
 :
-
-
-
 """
 
 perf_request = {
 'performance_data': performance_data,
 'optimization_goals': ['speed', 'efficiency', 'ux'],
 'context7_best_practices': True
 }
 
 optimization_result = await call_agent(self.perf_agent, perf_request)
 
 return PerformanceOptimization(
 identified_bottlenecks=optimization_result['bottlenecks'],
 optimization_strategies=optimization_result['strategies'],
 expected_improvements=optimization_result['improvements']
 )
```

## Perfect Gentleman 

### UX 
```python
class KoreanUXOptimizer:
 """Perfect Gentleman UX """
 
 def __init__(self, conversation_language="ko"):
 self.conversation_language = conversation_language
 self.style_templates = self.load_style_templates()
 
 def generate_korean_response(self, test_results: TestResults) -> KoreanResponse:
 """
 
 
 :
-
-
-
 """
 
 if self.conversation_language == "ko":
 response_template = self.style_templates['korean_formal']
 
 return KoreanResponse(
 greeting=response_template['greeting'],
 summary=self.create_korean_summary(test_results),
 detailed_findings=self.create_korean_findings(test_results),
 recommendations=self.create_korean_recommendations(test_results),
 closing=response_template['closing']
 )
 else:
 return self.generate_english_response(test_results)
 
 def create_korean_summary(self, results: TestResults) -> str:
 """ """
 
 pass_rate = results.calculate_pass_rate()
 status = "" if pass_rate >= 90 else " " if pass_rate >= 70 else ""
 
 summary = f"""
 

 : {pass_rate:.1f}%
 : {status}

 :
• {len(results.tests)} 
• : {len(results.passed_tests)}
• : {len(results.failed_tests)}
• : {len(results.performance_issues)}

AI : {self.get_ai_status_description(results.ai_insights)}
 """
 
 return summary.strip()
```

## TRUST 5 

### 
```python
class TRUST5QualityAssurance:
 """TRUST 5 """
 
 async def validate_test_quality(self, test_results: TestResults) -> QualityReport:
 """
 TRUST 5 
 
 TRUST 5:
 - Test First: 
 - Readable: 
 - Unified: 
 - Secured: 
 - Trackable: 
 """
 
 quality_scores = {
 'test_first': self.validate_test_first_principle(test_results),
 'readable': self.validate_test_readability(test_results),
 'unified': self.validate_test_unification(test_results),
 'secured': self.validate_test_security(test_results),
 'trackable': self.validate_test_traceability(test_results)
 }
 
 overall_score = sum(quality_scores.values()) / len(quality_scores)
 
 return QualityReport(
 individual_scores=quality_scores,
 overall_score=overall_score,
 compliance_level=self.determine_compliance_level(overall_score),
 improvement_recommendations=self.generate_improvement_recommendations(quality_scores)
 )
```

## : Alfred 

```python
# Alfred 
async def alfred_complete_testing_workflow(user_request: str):
 """
 Alfred 4-step AI 
 
 
 """
 
 # Step 1: Intent Understanding
 intent = analyze_user_intent(user_request)
 
 # Step 2: Plan Creation (with Context7 + AI)
 test_plan = await create_ai_test_plan(intent)
 
 # Step 3: Task Execution (AI-orchestrated)
 test_executor = AITestExecutor()
 results = await test_executor.execute_comprehensive_testing(test_plan)
 
 # Step 4: Report & Analysis
 report = await generate_ai_test_report(results)
 
 # Multi-agent coordination
 coordinator = AlfredAgentCoordinator()
 
 if results.has_failures():
 debug_analysis = await coordinator.coordinate_with_debug_agent(results.failures)
 report.debug_insights = debug_analysis
 
 if results.has_performance_issues():
 perf_optimization = await coordinator.coordinate_with_performance_agent(results.performance_data)
 report.performance_optimization = perf_optimization
 
 # Quality assurance
 qa_validator = TRUST5QualityAssurance()
 quality_report = await qa_validator.validate_test_quality(results)
 report.quality_assurance = quality_report
 
 # Korean UX optimization
 ux_optimizer = KoreanUXOptimizer()
 korean_report = ux_optimizer.generate_korean_response(results)
 
 return {
 'technical_report': report,
 'user_friendly_report': korean_report,
 'next_actions': generate_next_actions(report),
 'alfred_workflow_completed': True
 }

# 
if __name__ == "__main__":
 # 
 user_input = " "
 
 # Alfred 
 result = await alfred_complete_testing_workflow(user_input)
 
 # 
 print(" Alfred AI ")
 print(result['user_friendly_report'].summary)
```
