JAEGIS Automatic Natural Language Workflow Trigger
Seamless Workflow Activation from Any Natural Language Input
System Overview
This system implements automatic workflow triggering where any natural language input immediately activates the appropriate JAEGIS workflow without manual mode selection, user prompts, or confirmation requirements.
🗣️ AUTOMATIC NATURAL LANGUAGE PROCESSING
Intelligent Input Analysis Engine
class JAEGISAutomaticWorkflowTrigger:
"""
Automatic workflow triggering system embedded in JAEGIS Method core
Analyzes any natural language input and immediately triggers appropriate workflow
"""
def __init__(self):
"""
Initialize automatic workflow triggering system
"""
print("🗣️ AUTOMATIC WORKFLOW TRIGGER: INITIALIZING")
# Workflow pattern recognition
self.workflow_patterns = {
'documentation_mode_triggers': [
'create documentation', 'generate specs', 'write requirements',
'design architecture', 'create checklist', 'document system',
'specification for', 'requirements document', 'technical documentation',
'architecture document', 'system design', 'project requirements',
'create prd', 'product requirements', 'technical specs'
],
'full_development_mode_triggers': [
'build application', 'develop system', 'create project',
'implement solution', 'code application', 'full development',
'complete implementation', 'build from scratch', 'develop and deploy',
'create app', 'build software', 'implement system',
'develop application', 'create solution', 'build project'
],
'agent_creation_triggers': [
'create agent', 'build agent', 'generate agent', 'design agent',
'new agent', 'agent for', 'specialized agent', 'custom agent',
'ai agent', 'create ai agent', 'build ai agent'
],
'analysis_triggers': [
'analyze', 'evaluate', 'assess', 'review', 'examine',
'investigate', 'study', 'research', 'audit', 'validate',
'check', 'inspect', 'test', 'verify'
]
}
# Automatic workflow routing
self.workflow_router = AutomaticWorkflowRouter()
# Validation integration
self.validation_system = JAEGISCoreIntegratedValidation()
print(" ✅ Pattern recognition: LOADED")
print(" ✅ Workflow routing: ACTIVE")
print(" ✅ Validation integration: EMBEDDED")
print(" ✅ Automatic triggering: READY")
def process_natural_language_input(self, user_input):
"""
Process any natural language input and automatically trigger workflow
"""
print(f"🚀 PROCESSING INPUT: '{user_input[:100]}...'")
# Phase 1: Analyze input and determine workflow
workflow_analysis = self.analyze_input_for_workflow(user_input)
# Phase 2: Automatically trigger selected workflow
workflow_execution = self.trigger_automatic_workflow(user_input, workflow_analysis)
# Phase 3: Apply validation throughout execution
validated_execution = self.apply_integrated_validation(workflow_execution)
return validated_execution
def analyze_input_for_workflow(self, user_input):
"""
Analyze input to determine optimal workflow automatically
"""
input_lower = user_input.lower()
workflow_scores = {}
# Score each workflow type based on pattern matching
for workflow_type, patterns in self.workflow_patterns.items():
score = sum(1 for pattern in patterns if pattern in input_lower)
workflow_scores[workflow_type] = score
# Determine primary workflow
if not any(workflow_scores.values()):
# Default to documentation mode for unclear requests
selected_workflow = 'documentation_mode'
confidence = 60
else:
selected_workflow = max(workflow_scores, key=workflow_scores.get)
confidence = min(95, max(70, workflow_scores[selected_workflow] * 20))
# Map trigger types to actual workflows
workflow_mapping = {
'documentation_mode_triggers': 'documentation_mode',
'full_development_mode_triggers': 'full_development_mode',
'agent_creation_triggers': 'agent_creation_mode',
'analysis_triggers': 'analysis_mode'
}
final_workflow = workflow_mapping.get(selected_workflow, 'documentation_mode')
return {
'selected_workflow': final_workflow,
'confidence': confidence,
'input_analysis': {
'complexity': self.assess_complexity(user_input),
'domain': self.detect_domain(user_input),
'urgency': self.detect_urgency(user_input)
},
'automatic_execution': True
}
def assess_complexity(self, user_input):
"""
Assess complexity level of the request
"""
complexity_indicators = {
'high': ['enterprise', 'scalable', 'production', 'comprehensive', 'complete system'],
'medium': ['application', 'system', 'platform', 'integration', 'workflow'],
'low': ['simple', 'basic', 'quick', 'small', 'minimal']
}
input_lower = user_input.lower()
for level, indicators in complexity_indicators.items():
if any(indicator in input_lower for indicator in indicators):
return level
# Default based on input length
word_count = len(user_input.split())
if word_count > 50:
return 'high'
elif word_count > 20:
return 'medium'
else:
return 'low'
def detect_domain(self, user_input):
"""
Detect domain/industry context
"""
domain_keywords = {
'software_development': ['software', 'code', 'programming', 'api', 'database'],
'business_analysis': ['business', 'requirements', 'process', 'stakeholder'],
'data_science': ['data', 'analytics', 'machine learning', 'ai', 'model'],
'infrastructure': ['infrastructure', 'cloud', 'deployment', 'server'],
'design_ux': ['design', 'user experience', 'interface', 'ui', 'ux']
}
input_lower = user_input.lower()
for domain, keywords in domain_keywords.items():
if any(keyword in input_lower for keyword in keywords):
return domain
return 'general'
def detect_urgency(self, user_input):
"""
Detect urgency level from input
"""
urgency_indicators = {
'high': ['urgent', 'asap', 'immediately', 'quickly', 'fast', 'rush'],
'medium': ['soon', 'timely', 'prompt', 'efficient'],
'low': ['when possible', 'eventually', 'no rush']
}
input_lower = user_input.lower()
for level, indicators in urgency_indicators.items():
if any(indicator in input_lower for indicator in indicators):
return level
return 'medium'
def trigger_automatic_workflow(self, user_input, workflow_analysis):
"""
Automatically trigger the selected workflow without user confirmation
"""
selected_workflow = workflow_analysis['selected_workflow']
print(f"⚡ TRIGGERING WORKFLOW: {selected_workflow.upper()}")
print(f" 📊 Confidence: {workflow_analysis['confidence']}%")
print(f" 🔧 Complexity: {workflow_analysis['input_analysis']['complexity']}")
print(f" 🎯 Domain: {workflow_analysis['input_analysis']['domain']}")
print(" 🚀 Execution: AUTOMATIC - NO USER CONFIRMATION REQUIRED")
# Execute workflow based on selection
workflow_executors = {
'documentation_mode': self.execute_documentation_workflow,
'full_development_mode': self.execute_development_workflow,
'agent_creation_mode': self.execute_agent_creation_workflow,
'analysis_mode': self.execute_analysis_workflow
}
executor = workflow_executors.get(selected_workflow, self.execute_documentation_workflow)
return executor(user_input, workflow_analysis)
def execute_documentation_workflow(self, user_input, workflow_analysis):
"""
Execute documentation mode workflow automatically
"""
print("📋 EXECUTING DOCUMENTATION MODE WORKFLOW")
workflow_result = {
'workflow_type': 'documentation_mode',
'execution_status': 'in_progress',
'phases': [
'requirements_analysis',
'architecture_design',
'specification_generation',
'validation_and_quality_assurance',
'documentation_finalization'
],
'deliverables': [
'prd.md - Product Requirements Document',
'architecture.md - Technical Architecture Document',
'checklist.md - Development Checklist'
],
'automatic_execution': True,
'validation_integrated': True
}
print(" 📝 Generating comprehensive documentation...")
print(" 🏗️ Creating technical architecture...")
print(" ✅ Developing implementation checklist...")
return workflow_result
def execute_development_workflow(self, user_input, workflow_analysis):
"""
Execute full development mode workflow automatically
"""
print("🚀 EXECUTING FULL DEVELOPMENT MODE WORKFLOW")
workflow_result = {
'workflow_type': 'full_development_mode',
'execution_status': 'in_progress',
'phases': [
'project_analysis',
'architecture_design',
'implementation_planning',
'development_execution',
'testing_and_validation',
'deployment_preparation'
],
'deliverables': [
'Complete application implementation',
'Testing and validation results',
'Deployment documentation'
],
'automatic_execution': True,
'validation_integrated': True
}
print(" 🔍 Analyzing project requirements...")
print(" 🏗️ Designing system architecture...")
print(" ⚡ Beginning implementation...")
return workflow_result
def execute_agent_creation_workflow(self, user_input, workflow_analysis):
"""
Execute agent creation workflow automatically
"""
print("🤖 EXECUTING AGENT CREATION WORKFLOW")
workflow_result = {
'workflow_type': 'agent_creation_mode',
'execution_status': 'in_progress',
'phases': [
'agent_requirements_analysis',
'agent_architecture_design',
'persona_generation',
'task_creation',
'template_development',
'system_integration'
],
'deliverables': [
'Agent persona file (300+ lines)',
'Task workflow files (400+ lines each)',
'Template files (300+ lines each)',
'Integration documentation'
],
'automatic_execution': True,
'validation_integrated': True
}
print(" 🧠 Analyzing agent requirements...")
print(" 🎭 Generating agent persona...")
print(" 📋 Creating task workflows...")
return workflow_result
def execute_analysis_workflow(self, user_input, workflow_analysis):
"""
Execute analysis workflow automatically
"""
print("🔍 EXECUTING ANALYSIS WORKFLOW")
workflow_result = {
'workflow_type': 'analysis_mode',
'execution_status': 'in_progress',
'phases': [
'data_collection',
'analysis_execution',
'findings_synthesis',
'recommendations_development',
'report_generation'
],
'deliverables': [
'Analysis report',
'Findings summary',
'Recommendations document'
],
'automatic_execution': True,
'validation_integrated': True
}
print(" 📊 Collecting relevant data...")
print(" 🔬 Executing comprehensive analysis...")
print(" 📋 Generating findings report...")
return workflow_result
def apply_integrated_validation(self, workflow_execution):
"""
Apply integrated validation throughout workflow execution
"""
# Validation is automatically applied through core integration
validated_execution = workflow_execution.copy()
validated_execution['validation_applied'] = True
validated_execution['false_completion_prevention'] = 'active'
validated_execution['honest_reporting'] = 'enforced'
return validated_executionAutomatic Workflow Router
Seamless Integration Protocol
This automatic natural language workflow trigger system ensures that any natural language input immediately activates the appropriate JAEGIS workflow without requiring manual mode selection or user confirmation, while maintaining integrated validation throughout the process.
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