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AI Agentic Workflows API Documentation

Advanced Multi-Step Workflow Execution REST API

Complete API reference for the AI Agentic Workflows module, providing intelligent multi-step execution, dynamic decision making, real-time monitoring, and comprehensive AI provider integration.

Base URL​

https://your-site.com/ai-workflows/api

Authentication​

All API endpoints support standard Drupal authentication:

  • Session Cookie: Web interface authentication
  • Basic Auth: Username/password for integrations
  • OAuth2: Third-party application access
  • API Keys: Token-based authentication
Authorization: Bearer YOUR_API_TOKEN
Content-Type: application/json
Accept: application/json

Core Workflow Execution APIs​

Execute Multi-Step Workflow​

Execute a comprehensive workflow with advanced AI integration and real-time monitoring.

POST /execute-flow
Content-Type: application/json

{
  "flow_id": "advanced_content_processing",
  "context": {
    "content": "Content to process through the workflow",
    "user_context": {
      "role": "editor",
      "department": "marketing",
      "permissions": ["edit", "publish"]
    },
    "processing_options": {
      "priority": "high",
      "quality_level": "premium",
      "target_audience": "developers",
      "seo_requirements": true
    },
    "metadata": {
      "source": "cms",
      "content_type": "blog_post",
      "language": "en",
      "deadline": "2025-01-25T10:00:00Z"
    }
  },
  "options": {
    "execution_mode": "synchronous",
    "monitoring": {
      "enable_detailed_logging": true,
      "track_intermediate_results": true,
      "send_progress_updates": true,
      "capture_agent_decisions": true
    },
    "ai_options": {
      "preferred_providers": ["ollama", "openai"],
      "fallback_enabled": true,
      "cost_limit": 0.50,
      "quality_threshold": 0.8
    },
    "error_handling": {
      "retry_attempts": 3,
      "retry_delay": 5,
      "fallback_strategy": "continue_without_failed_step",
      "notification_on_failure": true
    },
    "callback_config": {
      "webhook_url": "https://mysite.com/webhook/workflow-complete",
      "notification_email": "admin@example.com",
      "progress_callbacks": true
    }
  }
}

Response Schema:

{
  "success": true,
  "execution_id": "flow_advanced_content_processing_xyz789",
  "status": "completed",
  "execution_summary": {
    "flow_id": "advanced_content_processing",
    "steps_executed": 5,
    "steps_skipped": 1,
    "execution_time": 127.8,
    "total_cost": 0.156,
    "success_rate": 100.0,
    "agent_decisions_made": 3
  },
  "step_results": {
    "content_analysis": {
      "step_type": "ai_prompt",
      "execution_time": 18.5,
      "tokens_used": 567,
      "cost": 0.028,
      "success": true,
      "results": {
        "quality_score": 8.4,
        "readability": 7.8,
        "sentiment": 0.87,
        "technical_depth": "intermediate",
        "seo_potential": 85,
        "improvement_areas": ["structure", "keywords"]
      },
      "ai_provider": "ollama",
      "model_used": "llama3.2:latest",
      "confidence": 0.94
    },
    "conditional_routing": {
      "step_type": "conditional",
      "execution_time": 2.1,
      "success": true,
      "conditions_evaluated": [
        {
          "condition": "quality_score > 8.0",
          "result": true,
          "action_taken": "proceed_to_advanced_optimization"
        }
      ],
      "routing_decision": "advanced_optimization_path"
    },
    "seo_optimization": {
      "step_type": "ai_prompt",
      "execution_time": 25.3,
      "tokens_used": 789,
      "cost": 0.039,
      "success": true,
      "results": {
        "optimized_title": "Advanced AI Development: A Comprehensive Guide for Developers",
        "meta_description": "Explore cutting-edge AI development techniques...",
        "keyword_density": 2.8,
        "internal_links_suggested": 4,
        "header_structure_improved": true
      },
      "optimization_improvements": [
        "Enhanced semantic structure",
        "Improved keyword distribution", 
        "Added relevant internal links"
      ]
    },
    "external_validation": {
      "step_type": "external_api",
      "execution_time": 12.7,
      "api_response_time": 8.9,
      "success": true,
      "api_endpoint": "https://api.grammarly.com/check",
      "results": {
        "grammar_score": 98,
        "issues_found": 2,
        "issues_fixed": 2,
        "overall_improvement": 15.2
      }
    },
    "data_transformation": {
      "step_type": "data_transform",
      "execution_time": 1.8,
      "success": true,
      "transformations_applied": 3,
      "results": {
        "consolidated_analysis": {
          "final_quality_score": 9.2,
          "optimization_score": 88,
          "readiness_level": "publish_ready",
          "confidence": 0.96
        }
      }
    }
  },
  "shared_memory": {
    "content_analysis": {...},
    "seo_optimization": {...},
    "final_analysis": {...}
  },
  "agent_decisions": [
    {
      "step": "content_analysis",
      "agent": "content_quality_analyzer",
      "decision": "high_quality_content_detected",
      "confidence": 0.94,
      "reasoning": "Content exceeds quality thresholds across all metrics",
      "context_used": ["readability_score", "technical_accuracy", "structure"],
      "alternative_paths_considered": ["basic_optimization", "quality_improvement"],
      "timestamp": 1640995320
    },
    {
      "step": "conditional_routing",
      "decision": "advanced_optimization_path",
      "confidence": 0.91,
      "reasoning": "Quality score qualifies for advanced processing",
      "workflow_modifications": {
        "steps_added": ["advanced_seo_optimization"],
        "steps_skipped": ["basic_content_fixes"]
      }
    },
    {
      "step": "seo_optimization",
      "agent": "seo_specialist",
      "decision": "comprehensive_optimization",
      "confidence": 0.88,
      "reasoning": "High potential for significant SEO improvements detected"
    }
  ],
  "performance_metrics": {
    "total_execution_time": 127.8,
    "ai_processing_time": 95.4,
    "api_call_time": 18.2,
    "data_processing_time": 14.2,
    "total_ai_calls": 4,
    "total_tokens_used": 2145,
    "total_cost": 0.156,
    "cache_hits": 2,
    "provider_distribution": {
      "ollama": 3,
      "openai": 1
    }
  },
  "resource_utilization": {
    "peak_memory_usage": "89MB",
    "cpu_time": "23.7s",
    "network_requests": 6,
    "cache_operations": 8
  },
  "quality_metrics": {
    "overall_success_rate": 100.0,
    "step_success_rate": 100.0,
    "agent_confidence_avg": 0.91,
    "output_quality_score": 9.2
  },
  "execution_timeline": [
    {
      "step": "content_analysis",
      "started_at": 1640995200,
      "completed_at": 1640995218,
      "duration": 18.5
    },
    {
      "step": "conditional_routing", 
      "started_at": 1640995219,
      "completed_at": 1640995221,
      "duration": 2.1
    }
  ],
  "warnings": [],
  "recommendations": [
    "Consider caching content analysis results for similar content",
    "SEO optimization showed high impact - consider similar workflows"
  ],
  "timestamp": 1640995800
}

Get Flow Execution Status​

Monitor real-time execution progress with comprehensive status information.

GET /execution-status/{execution_id}?include_details={boolean}&include_predictions={boolean}

Path Parameters:

  • execution_id (string): Unique execution identifier

Query Parameters:

  • include_details (boolean): Include detailed step information
  • include_predictions (boolean): Include completion time predictions
  • include_memory (boolean): Include shared memory state

Response Schema:

{
  "execution_id": "flow_advanced_content_processing_xyz789",
  "flow_id": "advanced_content_processing",
  "status": "running",
  "current_phase": "execution",
  "progress": {
    "overall_percentage": 68.0,
    "current_step": "seo_optimization",
    "current_step_progress": 75.0,
    "steps_completed": 3,
    "steps_remaining": 2,
    "steps_total": 5
  },
  "timing": {
    "started_at": 1640995200,
    "current_time": 1640995287,
    "elapsed_time": 87.3,
    "estimated_completion": 1640995345,
    "estimated_remaining": 58.2,
    "last_activity": 1640995285
  },
  "current_step_details": {
    "step_id": "seo_optimization",
    "step_type": "ai_prompt",
    "status": "processing",
    "started_at": 1640995245,
    "elapsed_time": 42.1,
    "ai_provider": "ollama",
    "model": "llama3.2:latest",
    "progress_indicators": {
      "prompt_processed": true,
      "ai_response_streaming": true,
      "response_processing": false,
      "validation": false
    },
    "intermediate_output": {
      "partial_results": "Optimizing title structure...",
      "confidence_building": 0.78,
      "tokens_processed": 456
    }
  },
  "completed_steps": [
    {
      "step_id": "content_analysis",
      "status": "completed",
      "duration": 18.5,
      "success": true,
      "key_outputs": {
        "quality_score": 8.4,
        "readability": 7.8,
        "sentiment": 0.87
      }
    },
    {
      "step_id": "conditional_routing",
      "status": "completed", 
      "duration": 2.1,
      "success": true,
      "routing_decision": "advanced_optimization_path"
    },
    {
      "step_id": "quality_validation",
      "status": "completed",
      "duration": 15.2,
      "success": true,
      "validation_score": 9.1
    }
  ],
  "upcoming_steps": [
    {
      "step_id": "final_review",
      "step_type": "ai_prompt",
      "estimated_duration": 12.0,
      "dependencies": ["seo_optimization"]
    },
    {
      "step_id": "data_export",
      "step_type": "data_transform",
      "estimated_duration": 3.5,
      "dependencies": ["final_review"]
    }
  ],
  "shared_memory_state": {
    "content_analysis": {
      "quality_score": 8.4,
      "technical_depth": "intermediate",
      "target_keywords": ["ai", "development", "machine learning"]
    },
    "optimization_context": {
      "seo_focus": "technical_content",
      "audience": "developers",
      "content_length": 2450
    }
  },
  "agent_activity": [
    {
      "step": "seo_optimization",
      "agent": "seo_specialist",
      "activity": "analyzing_keyword_density",
      "progress": 75.0,
      "last_update": 1640995285,
      "processing_tokens": 234
    }
  ],
  "resource_usage": {
    "current_memory": "67MB",
    "peak_memory": "89MB",
    "cpu_usage": 45.2,
    "active_connections": 2,
    "queue_position": null
  },
  "cost_tracking": {
    "current_cost": 0.089,
    "estimated_total_cost": 0.156,
    "cost_by_provider": {
      "ollama": 0.000,
      "openai": 0.089
    },
    "budget_remaining": 0.411
  },
  "performance_indicators": {
    "execution_health": "excellent",
    "step_success_rate": 100.0,
    "average_step_time": 16.8,
    "provider_response_times": {
      "ollama": 1.2,
      "openai": 2.8
    }
  },
  "predictions": {
    "completion_confidence": 0.94,
    "estimated_final_cost": 0.156,
    "potential_issues": [],
    "optimization_opportunities": [
      "Step 'final_review' could benefit from caching"
    ]
  },
  "real_time_logs": [
    {
      "timestamp": 1640995285,
      "level": "info",
      "message": "SEO optimization 75% complete",
      "step": "seo_optimization",
      "details": {"keywords_processed": 15, "suggestions_generated": 8}
    },
    {
      "timestamp": 1640995280,
      "level": "debug",
      "message": "AI provider response received",
      "step": "seo_optimization",
      "response_time": 2.1
    }
  ],
  "warnings": [
    {
      "type": "performance",
      "message": "Current step taking longer than average",
      "threshold": 35.0,
      "current": 42.1,
      "impact": "low"
    }
  ]
}

Execute Flow Asynchronously​

Execute workflows in background with comprehensive monitoring and callback support.

POST /execute-flow-async
Content-Type: application/json

{
  "flow_id": "large_content_processing",
  "context": {
    "content_batch": ["article1", "article2", "article3"],
    "processing_rules": {
      "batch_size": 10,
      "parallel_processing": true,
      "quality_threshold": 0.8
    }
  },
  "options": {
    "priority": "high",
    "max_execution_time": 1800,
    "resource_limits": {
      "max_memory": "512MB",
      "max_concurrent_ai_calls": 3
    },
    "notifications": {
      "progress_interval": 300,
      "webhook_url": "https://mysite.com/webhook/progress",
      "email_on_completion": "admin@example.com",
      "slack_channel": "#ai-workflows"
    },
    "retry_config": {
      "max_attempts": 3,
      "exponential_backoff": true,
      "retry_on_provider_error": true
    }
  }
}

Response Schema:

{
  "success": true,
  "execution_id": "async_large_content_processing_def456",
  "status": "queued",
  "queue_position": 3,
  "estimated_start_time": 1640995890,
  "estimated_completion_time": 1640997690,
  "monitoring": {
    "status_url": "/execution-status/async_large_content_processing_def456",
    "webhook_configured": true,
    "progress_notifications": true
  },
  "execution_config": {
    "priority": "high",
    "estimated_cost": 2.45,
    "resource_allocation": {
      "memory_reserved": "512MB",
      "cpu_priority": "high",
      "ai_call_slots": 3
    }
  },
  "callback_info": {
    "webhook_url": "https://mysite.com/webhook/progress",
    "webhook_secret": "wh_secret_abc123",
    "supported_events": [
      "execution.started",
      "execution.progress",
      "execution.completed",
      "execution.failed"
    ]
  },
  "timestamp": 1640995800
}

Get Flow Recommendations​

AI-powered workflow recommendations based on context analysis and historical performance.

POST /recommend-flows
Content-Type: application/json

{
  "context": {
    "content_type": "technical_documentation",
    "content_characteristics": {
      "length": 5000,
      "complexity": "high",
      "technical_level": "expert",
      "target_audience": "developers",
      "language": "en",
      "domain": "machine_learning"
    },
    "current_quality_metrics": {
      "readability_score": 6.2,
      "seo_score": 45,
      "technical_accuracy": 8.9,
      "structure_score": 7.1
    },
    "business_requirements": {
      "target_seo_score": 85,
      "required_quality_level": "premium",
      "compliance_requirements": ["gdpr", "accessibility"],
      "publication_deadline": "2025-01-30T10:00:00Z"
    },
    "user_context": {
      "role": "technical_writer",
      "experience_level": "expert", 
      "department": "engineering",
      "previous_workflows": ["technical_content_enhancement", "api_documentation"]
    }
  },
  "preferences": {
    "max_cost": 1.00,
    "max_duration": 300,
    "min_confidence": 0.85,
    "preferred_providers": ["ollama", "openai"],
    "exclude_experimental": true,
    "prioritize": "quality_over_speed",
    "explain_recommendations": true
  },
  "constraints": {
    "available_time_window": "2025-01-25T09:00:00Z/2025-01-25T17:00:00Z",
    "resource_limits": {
      "max_memory": "1GB",
      "max_concurrent_flows": 2
    },
    "compliance_requirements": ["data_retention", "audit_logging"]
  }
}

Response Schema:

{
  "recommendations": [
    {
      "flow_id": "expert_technical_content_enhancement",
      "confidence": 0.96,
      "match_score": 9.4,
      "recommendation_strength": "highly_recommended",
      "reasoning": {
        "primary_factors": [
          "Perfect match for technical documentation processing",
          "Excellent track record with similar content complexity",
          "Optimal provider selection for technical content",
          "Strong historical performance for ML domain content"
        ],
        "context_alignment": {
          "content_type_match": 0.98,
          "complexity_handling": 0.94,
          "domain_expertise": 0.92,
          "audience_targeting": 0.89
        },
        "performance_indicators": {
          "success_rate_similar_content": 0.96,
          "avg_quality_improvement": 2.8,
          "user_satisfaction": 4.7
        }
      },
      "estimated_outcomes": {
        "quality_improvement": {
          "readability_score": 8.5,
          "seo_score": 87,
          "technical_accuracy": 9.4,
          "structure_score": 9.1,
          "overall_improvement": 35.2
        },
        "execution_estimates": {
          "duration": 185,
          "cost": 0.78,
          "success_probability": 0.96,
          "completion_confidence": 0.94
        }
      },
      "flow_composition": {
        "total_steps": 7,
        "ai_steps": 5,
        "conditional_steps": 2,
        "api_steps": 1,
        "transform_steps": 2,
        "critical_steps": ["technical_analysis", "domain_validation", "expert_review"]
      },
      "provider_strategy": {
        "primary_provider": "ollama",
        "fallback_provider": "openai",
        "provider_distribution": {
          "ollama": ["technical_analysis", "structure_optimization"],
          "openai": ["domain_validation", "expert_review"]
        },
        "cost_optimization": "balanced"
      },
      "risk_assessment": {
        "risk_level": "low",
        "potential_issues": [
          {
            "risk": "high_complexity_timeout",
            "probability": 0.05,
            "mitigation": "Extended timeout configuration available"
          }
        ],
        "mitigation_strategies": [
          "Automatic step retry on timeout",
          "Progressive complexity handling",
          "Fallback to simplified processing"
        ]
      },
      "customization_options": [
        {
          "variant": "speed_optimized",
          "duration": 120,
          "cost": 0.65,
          "quality_tradeoff": -0.3,
          "description": "Faster execution with slightly reduced analysis depth"
        },
        {
          "variant": "premium_quality",
          "duration": 245,
          "cost": 1.25,
          "quality_improvement": +0.5,
          "description": "Maximum quality with additional validation steps"
        }
      ]
    },
    {
      "flow_id": "comprehensive_documentation_processor",
      "confidence": 0.89,
      "match_score": 8.7,
      "recommendation_strength": "recommended",
      "reasoning": {
        "primary_factors": [
          "Good fit for comprehensive documentation processing",
          "Strong SEO optimization capabilities",
          "Effective compliance handling"
        ]
      },
      "estimated_outcomes": {
        "quality_improvement": {
          "readability_score": 8.2,
          "seo_score": 92,
          "technical_accuracy": 9.1,
          "structure_score": 8.8
        },
        "execution_estimates": {
          "duration": 215,
          "cost": 0.92,
          "success_probability": 0.91
        }
      }
    }
  ],
  "context_analysis": {
    "complexity_assessment": {
      "content_complexity": "high",
      "processing_difficulty": "moderate",
      "domain_specialization_required": true,
      "estimated_processing_time": 180
    },
    "optimization_opportunities": [
      {
        "area": "seo_improvement",
        "current_score": 45,
        "potential_score": 87,
        "improvement_potential": 93.3,
        "priority": "high"
      },
      {
        "area": "readability_enhancement", 
        "current_score": 6.2,
        "potential_score": 8.5,
        "improvement_potential": 37.1,
        "priority": "medium"
      }
    ],
    "domain_insights": {
      "domain": "machine_learning",
      "specialized_processing_required": true,
      "domain_specific_validators": ["ml_terminology", "technical_accuracy", "code_examples"],
      "expert_review_recommended": true
    }
  },
  "workflow_comparison": {
    "comparison_matrix": [
      {
        "criterion": "quality_potential",
        "expert_technical_content_enhancement": 9.4,
        "comprehensive_documentation_processor": 8.7,
        "basic_content_processor": 6.2
      },
      {
        "criterion": "cost_efficiency",
        "expert_technical_content_enhancement": 8.9,
        "comprehensive_documentation_processor": 7.8,
        "basic_content_processor": 9.5
      },
      {
        "criterion": "speed",
        "expert_technical_content_enhancement": 7.8,
        "comprehensive_documentation_processor": 7.2,
        "basic_content_processor": 9.1
      }
    ],
    "recommendation_summary": "expert_technical_content_enhancement offers the best balance of quality and efficiency for your specific requirements"
  },
  "alternative_strategies": [
    {
      "strategy": "sequential_processing",
      "description": "Process in multiple stages for better quality control",
      "flows": ["basic_content_analysis", "expert_technical_enhancement", "final_review"],
      "total_cost": 1.15,
      "total_duration": 285,
      "quality_potential": 9.6
    },
    {
      "strategy": "parallel_processing",
      "description": "Process different aspects simultaneously for speed", 
      "flows": ["technical_analysis", "seo_optimization", "structure_enhancement"],
      "total_cost": 0.95,
      "total_duration": 145,
      "coordination_complexity": "moderate"
    }
  ],
  "success_predictors": {
    "high_success_indicators": [
      "Content complexity matches flow capabilities",
      "Strong historical performance in ML domain",
      "User expertise level aligns with flow sophistication"
    ],
    "potential_challenges": [
      "High technical complexity may require extended processing",
      "Domain-specific validation needed for accuracy"
    ],
    "success_optimization_tips": [
      "Provide comprehensive context for better analysis",
      "Consider premium quality variant for critical content",
      "Enable expert review for technical accuracy"
    ]
  },
  "cost_breakdown": {
    "base_processing": 0.45,
    "ai_provider_costs": 0.28,
    "premium_features": 0.05,
    "estimated_total": 0.78,
    "cost_comparison": {
      "vs_manual_processing": "-85%",
      "vs_basic_automation": "+23%",
      "vs_external_service": "-62%"
    }
  },
  "recommendation_metadata": {
    "analysis_version": "2.1.0",
    "model_confidence": 0.94,
    "recommendations_generated": 2,
    "analysis_duration": 3.2,
    "data_sources": [
      "historical_performance",
      "user_preferences", 
      "content_analysis",
      "domain_expertise"
    ],
    "last_updated": 1640995800
  }
}

Advanced Flow Configuration APIs​

Create Custom Flow Definition​

Define complex multi-step workflows with AI integration and dynamic routing.

POST /flows
Content-Type: application/json

{
  "flow_definition": {
    "id": "custom_research_workflow",
    "label": "Custom Research & Analysis Workflow",
    "description": "Advanced research workflow with AI-powered analysis and dynamic routing",
    "version": "1.0.0",
    "category": "research",
    "tags": ["research", "analysis", "ai", "dynamic"],
    "steps": [
      {
        "id": "initial_research",
        "type": "ai_prompt",
        "label": "Initial Research Phase",
        "prompt": "Conduct comprehensive research on: {{research_topic}}. Focus on {{focus_areas}} and provide detailed analysis including current trends, key players, and future outlook.",
        "ai_options": {
          "model": "llama3.2:latest",
          "temperature": 0.4,
          "max_tokens": 2000,
          "system_prompt": "You are an expert researcher with deep knowledge across multiple domains. Provide comprehensive, factual, and well-structured research."
        },
        "output_to_memory": "initial_research_results",
        "validation": {
          "min_length": 500,
          "required_sections": ["overview", "trends", "key_players"],
          "quality_threshold": 0.8
        },
        "retry_config": {
          "max_attempts": 3,
          "retry_on_validation_failure": true
        }
      },
      {
        "id": "quality_assessment",
        "type": "conditional",
        "label": "Research Quality Assessment",
        "conditions": [
          {
            "field": "initial_research_results.quality_score",
            "operator": ">=",
            "value": 0.8,
            "action": {
              "type": "continue",
              "next_step": "competitive_analysis"
            }
          },
          {
            "field": "initial_research_results.quality_score", 
            "operator": "<",
            "value": 0.8,
            "action": {
              "type": "jump_to_step",
              "target_step": "research_enhancement",
              "reason": "Initial research quality below threshold"
            }
          }
        ],
        "default_action": {
          "type": "continue",
          "next_step": "competitive_analysis"
        }
      },
      {
        "id": "competitive_analysis",
        "type": "ai_prompt",
        "label": "Competitive Analysis",
        "prompt": "Based on the initial research: {{initial_research_results}}, conduct a detailed competitive analysis. Identify main competitors, their strengths/weaknesses, market positioning, and opportunities.",
        "ai_options": {
          "model": "gpt-4",
          "temperature": 0.3,
          "max_tokens": 1500
        },
        "dependencies": ["initial_research"],
        "output_to_memory": "competitive_analysis_results",
        "parallel_execution": false
      },
      {
        "id": "market_data_fetch",
        "type": "external_api",
        "label": "Market Data Collection",
        "api": {
          "url": "https://api.marketdata.com/v1/research",
          "method": "POST",
          "headers": {
            "Authorization": "Bearer {{market_data_api_key}}",
            "Content-Type": "application/json"
          },
          "body": {
            "query": "{{research_topic}}",
            "data_types": ["market_size", "growth_rate", "trends"],
            "time_period": "5_years",
            "regions": "{{target_regions}}"
          },
          "timeout": 45,
          "retry_attempts": 2
        },
        "output_to_memory": "market_data",
        "error_handling": {
          "on_failure": "continue_without_data",
          "fallback_message": "Market data unavailable - continuing with available research"
        }
      },
      {
        "id": "data_synthesis",
        "type": "data_transform",
        "label": "Data Synthesis & Integration",
        "dependencies": ["competitive_analysis", "market_data_fetch"],
        "transformations": [
          {
            "operation": "merge",
            "sources": [
              "initial_research_results",
              "competitive_analysis_results", 
              "market_data"
            ],
            "merge_strategy": "comprehensive"
          },
          {
            "operation": "calculate_metrics",
            "fields": [
              "market_opportunity_score",
              "competitive_threat_level",
              "research_confidence_score"
            ],
            "calculation_method": "weighted_average"
          },
          {
            "operation": "generate_summary",
            "template": "research_report_template",
            "include_visualizations": true
          }
        ],
        "output_to_memory": "synthesized_report",
        "validation": {
          "required_fields": ["market_opportunity_score", "executive_summary"],
          "min_confidence": 0.7
        }
      },
      {
        "id": "ai_insights_generation",
        "type": "ai_prompt",
        "label": "AI-Powered Insights Generation",
        "prompt": "Based on the comprehensive research data: {{synthesized_report}}, generate strategic insights, recommendations, and actionable next steps. Include risk assessment and opportunity prioritization.",
        "ai_options": {
          "model": "gpt-4-turbo",
          "temperature": 0.6,
          "max_tokens": 2500,
          "system_prompt": "You are a strategic business analyst. Provide actionable insights with specific recommendations and clear reasoning."
        },
        "dependencies": ["data_synthesis"],
        "output_to_memory": "strategic_insights",
        "agent_routing": true,
        "routing_prompt": "Should this analysis include detailed financial projections? Consider the research depth and available market data.",
        "routing_options": {
          "include_financial_analysis": {
            "add_steps": ["financial_projections", "roi_analysis"],
            "condition": "market_data_quality >= 0.8"
          },
          "skip_financial_analysis": {
            "continue_to": "final_report_generation",
            "reason": "Insufficient financial data for accurate projections"
          }
        }
      },
      {
        "id": "final_report_generation",
        "type": "ai_prompt",
        "label": "Comprehensive Report Generation",
        "prompt": "Create a comprehensive research report combining all findings: {{initial_research_results}}, {{competitive_analysis_results}}, {{market_data}}, and {{strategic_insights}}. Format as a professional business report with executive summary, detailed findings, and recommendations.",
        "ai_options": {
          "model": "gpt-4",
          "temperature": 0.2,
          "max_tokens": 4000,
          "system_prompt": "You are a professional report writer. Create well-structured, executive-level reports with clear sections and actionable recommendations."
        },
        "dependencies": ["ai_insights_generation"],
        "output_to_memory": "final_report",
        "post_processing": [
          {
            "operation": "format_document",
            "format": "markdown",
            "include_toc": true,
            "add_charts": true
          },
          {
            "operation": "quality_check",
            "checks": ["grammar", "structure", "completeness"],
            "min_quality_score": 0.9
          }
        ]
      }
    ],
    "global_configuration": {
      "agent_routing_enabled": true,
      "shared_memory_persistent": true,
      "error_handling": {
        "global_retry_attempts": 2,
        "fallback_strategy": "graceful_degradation",
        "notification_on_failure": true
      },
      "monitoring": {
        "enable_detailed_logging": true,  
        "track_performance_metrics": true,
        "capture_intermediate_results": true
      },
      "resource_limits": {
        "max_execution_time": 1800,
        "max_memory_usage": "1GB",
        "max_concurrent_ai_calls": 3
      },
      "cost_controls": {
        "max_total_cost": 5.00,
        "cost_alerts": [1.00, 2.50, 4.00],
        "auto_abort_on_budget_exceeded": false
      }
    }
  },
  "metadata": {
    "created_by": "research_team",
    "department": "business_intelligence",
    "use_cases": ["market_research", "competitive_analysis", "strategic_planning"],
    "estimated_duration": 450,
    "estimated_cost": 2.50,
    "complexity_level": "high",
    "approval_required": false
  }
}

Response Schema:

{
  "success": true,
  "flow_id": "custom_research_workflow",
  "status": "created",
  "validation_results": {
    "is_valid": true,
    "steps_validated": 7,
    "dependencies_resolved": true,
    "resource_requirements_feasible": true,
    "estimated_performance": {
      "avg_execution_time": 425,
      "success_probability": 0.94,
      "estimated_cost": 2.35
    }
  },
  "flow_summary": {
    "total_steps": 7,
    "ai_steps": 4,
    "conditional_steps": 1,
    "api_steps": 1,
    "transform_steps": 1,
    "dynamic_routing_enabled": true,
    "parallel_execution_steps": 0
  },
  "deployment_info": {
    "environment": "production",
    "version": "1.0.0",
    "deployment_timestamp": 1640995800,
    "testing_recommended": true,
    "approval_status": "auto_approved"
  },
  "next_steps": [
    "Test flow with sample data",
    "Configure monitoring alerts",
    "Set up user permissions",
    "Schedule regular reviews"
  ]
}

Monitoring & Analytics APIs​

Get Flow Analytics​

Comprehensive analytics for flow performance, costs, and optimization opportunities.

GET /analytics/flows?timeframe={timeframe}&flow_ids={flow_ids}&include_predictions={boolean}

Query Parameters:

  • timeframe (string): Analysis period (1h, 24h, 7d, 30d)
  • flow_ids (array): Specific flows to analyze
  • include_predictions (boolean): Include performance predictions
  • group_by (string): Group results (flow, step_type, provider)
  • include_cost_breakdown (boolean): Detailed cost analysis

Response Schema:

{
  "analytics_summary": {
    "timeframe": "24h",
    "flows_analyzed": 8,
    "total_executions": 234,
    "success_rate": 94.3,
    "avg_execution_time": 87.5,
    "total_cost": 15.67,
    "unique_users": 23,
    "data_freshness": "real-time"
  },
  "flow_performance": {
    "advanced_content_processing": {
      "executions": 89,
      "success_rate": 96.6,
      "avg_duration": 127.8,
      "avg_cost": 0.156,
      "user_satisfaction": 4.8,
      "performance_trend": "+12%",
      "step_breakdown": {
        "content_analysis": {"avg_time": 18.5, "success_rate": 98.9},
        "seo_optimization": {"avg_time": 25.3, "success_rate": 94.4},
        "final_review": {"avg_time": 12.7, "success_rate": 97.8}
      },
      "cost_breakdown": {
        "ai_processing": 0.089,
        "api_calls": 0.023,
        "data_processing": 0.044,
        "total": 0.156
      },
      "optimization_opportunities": [
        {
          "area": "step_caching",
          "potential_savings": "15% cost reduction",
          "implementation_effort": "low"
        },
        {
          "area": "provider_optimization",
          "potential_savings": "8% speed improvement",
          "implementation_effort": "medium"
        }
      ]
    }
  },
  "step_type_analysis": {
    "ai_prompt": {
      "total_executions": 445,
      "avg_duration": 22.3,
      "success_rate": 95.8,
      "cost_per_execution": 0.045,
      "token_efficiency": 0.78,
      "top_models": ["llama3.2:latest", "gpt-4", "claude-3"]
    },
    "external_api": {
      "total_executions": 123,
      "avg_duration": 8.7,
      "success_rate": 92.7,
      "timeout_rate": 4.1,
      "retry_rate": 7.3
    },
    "data_transform": {
      "total_executions": 234,
      "avg_duration": 2.1,
      "success_rate": 99.1,
      "memory_efficiency": 0.91
    }
  },
  "provider_analysis": {
    "ollama": {
      "usage_percentage": 67.2,
      "avg_response_time": 1.8,
      "success_rate": 97.2,
      "cost_per_request": 0.000,
      "availability": "99.8%",
      "models_used": ["llama3.2:latest", "nomic-embed-text"]
    },
    "openai": {
      "usage_percentage": 28.1,
      "avg_response_time": 2.9,
      "success_rate": 94.6,
      "cost_per_request": 0.023,
      "rate_limit_hits": 3,
      "models_used": ["gpt-4", "gpt-3.5-turbo"]
    },
    "anthropic": {
      "usage_percentage": 4.7,
      "avg_response_time": "N/A",
      "success_rate": 0.0,
      "status": "offline",
      "last_successful_request": "2025-01-23T14:30:00Z"
    }
  },
  "user_behavior_insights": {
    "most_popular_flows": [
      {"flow_id": "content_processing", "usage": 89},
      {"flow_id": "seo_optimization", "usage": 67},
      {"flow_id": "data_analysis", "usage": 45}
    ],
    "peak_usage_times": ["09:00-11:00", "14:00-16:00"],
    "avg_session_length": 245,
    "user_retention_rate": 0.87,
    "feature_adoption": {
      "async_execution": 34.2,
      "custom_workflows": 12.8,
      "ai_routing": 67.3
    }
  },
  "cost_analysis": {
    "total_cost_24h": 15.67,
    "cost_per_execution": 0.067,
    "cost_trends": {
      "vs_yesterday": "-8.2%",
      "vs_week_ago": "+15.3%",
      "vs_month_ago": "-23.1%"
    },
    "cost_distribution": {
      "ai_processing": 67.2,
      "api_calls": 18.4,
      "data_storage": 8.9,
      "compute_resources": 5.5
    },
    "cost_optimization_recommendations": [
      {
        "strategy": "increase_caching",
        "potential_savings": "$2.34/day",
        "implementation": "Enable result caching for repeated queries"
      },
      {
        "strategy": "provider_rebalancing", 
        "potential_savings": "$1.89/day",
        "implementation": "Route more requests to Ollama for cost efficiency"
      }
    ]
  },
  "quality_metrics": {
    "output_quality_avg": 8.7,
    "user_satisfaction_avg": 4.6,
    "error_rate_by_category": {
      "validation_errors": 2.1,
      "timeout_errors": 1.8,
      "provider_errors": 1.4,
      "system_errors": 0.3
    },
    "quality_trends": {
      "improvement_rate": "+5.2% per week",
      "consistency_score": 0.91,
      "reliability_index": 0.94
    }
  },
  "predictions": {
    "next_24h": {
      "expected_executions": 267,
      "predicted_success_rate": 0.95,
      "estimated_cost": 17.89,
      "confidence": 0.88
    },
    "weekly_forecast": {
      "execution_trend": "+18%",
      "cost_trend": "+12%",
      "performance_trend": "+3%",
      "capacity_alerts": [
        {
          "metric": "concurrent_executions",
          "predicted_peak": "2025-01-26T15:00:00Z",
          "threshold_risk": "medium"
        }
      ]
    },
    "optimization_impact": {
      "cache_implementation": {
        "cost_reduction": "12-18%",
        "performance_improvement": "8-15%",
        "implementation_timeline": "1-2 weeks"
      },
      "provider_optimization": {
        "cost_reduction": "5-12%",
        "performance_improvement": "3-8%",
        "implementation_timeline": "3-5 days"
      }
    }
  },
  "benchmark_comparisons": {
    "industry_averages": {
      "success_rate": 0.89,
      "avg_cost_per_execution": 0.089,
      "user_satisfaction": 4.2
    },
    "performance_vs_benchmark": {
      "success_rate": "+5.3%",
      "cost_efficiency": "+24.7%",
      "user_satisfaction": "+9.5%"
    }
  },
  "alert_recommendations": [
    {
      "type": "performance",
      "threshold": "success_rate < 90%",
      "current_value": 94.3,
      "recommended_action": "Monitor for degradation trends"
    },
    {
      "type": "cost",
      "threshold": "daily_cost > $20.00",
      "current_value": 15.67,
      "recommended_action": "Consider cost optimization strategies"
    }
  ]
}

Integration & Webhook APIs​

Configure Webhooks​

Set up webhooks for real-time workflow event notifications.

POST /webhooks
Content-Type: application/json

{
  "webhook_config": {
    "url": "https://your-app.com/webhooks/ai-workflows",
    "secret": "your-webhook-secret-key",
    "events": [
      "flow.execution.started",
      "flow.execution.completed", 
      "flow.execution.failed",
      "flow.step.completed",
      "agent.decision.made",
      "cost.threshold.exceeded",
      "performance.degradation.detected"
    ],
    "filters": {
      "flow_ids": ["advanced_content_processing", "research_workflow"],
      "min_cost": 0.10,
      "priority": ["high", "critical"]
    },
    "retry_policy": {
      "max_attempts": 5,
      "retry_delay": 30,
      "exponential_backoff": true,
      "timeout": 10
    },
    "format": {
      "content_type": "application/json",
      "include_full_context": false,
      "include_intermediate_results": true,
      "compression": "gzip"
    }
  }
}

Response Schema:

{
  "success": true,
  "webhook_id": "wh_ai_workflows_abc123",
  "webhook_url": "https://your-app.com/webhooks/ai-workflows",
  "events_subscribed": 7,
  "verification": {
    "verification_required": true,
    "verification_token": "verify_abc123def456",
    "verification_url": "https://your-app.com/webhooks/ai-workflows?verify=verify_abc123def456",
    "expires_at": 1640999400
  },
  "status": "pending_verification",
  "created_at": 1640995800
}

Webhook Event Examples​

Flow Execution Started:

{
  "event": "flow.execution.started",
  "timestamp": 1640995800,
  "webhook_id": "wh_ai_workflows_abc123",
  "data": {
    "execution_id": "flow_content_processing_xyz789",
    "flow_id": "advanced_content_processing",
    "initiated_by": "user_123",
    "estimated_duration": 127,
    "estimated_cost": 0.156,
    "priority": "high",
    "context_summary": {
      "content_type": "blog_post",
      "content_length": 2450,
      "target_quality": "premium"
    }
  },
  "signature": "sha256=abc123def456789..."
}

Agent Decision Made:

{
  "event": "agent.decision.made",
  "timestamp": 1640995850,
  "data": {
    "execution_id": "flow_content_processing_xyz789",
    "step_id": "quality_assessment",
    "agent_id": "content_quality_analyzer",
    "decision": {
      "action": "proceed_to_advanced_optimization",
      "confidence": 0.94,
      "reasoning": "Content quality score (8.4) exceeds threshold for advanced processing",
      "alternatives_considered": ["basic_optimization", "quality_improvement"],
      "workflow_modifications": {
        "steps_added": ["advanced_seo_optimization"],
        "estimated_additional_cost": 0.023,
        "estimated_additional_time": 18
      }
    }
  }
}

Flow Execution Completed:

{
  "event": "flow.execution.completed",
  "timestamp": 1640995927,
  "data": {
    "execution_id": "flow_content_processing_xyz789",
    "flow_id": "advanced_content_processing",
    "status": "completed",
    "execution_summary": {
      "duration": 127.8,
      "steps_executed": 5,
      "success_rate": 100.0,
      "total_cost": 0.156,
      "agent_decisions": 3
    },
    "results": {
      "final_quality_score": 9.2,
      "optimization_improvements": 35.2,
      "seo_score": 87,
      "readiness_level": "publish_ready"
    },
    "performance_metrics": {
      "avg_step_time": 25.6,
      "cache_hit_rate": 0.4,
      "provider_distribution": {"ollama": 3, "openai": 2}
    }
  }
}

Error Handling and Status Codes​

HTTP Status Codes​

  • 200 OK: Request successful
  • 201 Created: Resource created successfully
  • 202 Accepted: Request accepted for async processing
  • 400 Bad Request: Invalid request data
  • 401 Unauthorized: Authentication required
  • 403 Forbidden: Insufficient permissions
  • 404 Not Found: Resource not found
  • 409 Conflict: Resource conflict (e.g., duplicate flow ID)
  • 422 Unprocessable Entity: Valid request but logical errors
  • 429 Too Many Requests: Rate limit exceeded
  • 500 Internal Server Error: Server error
  • 502 Bad Gateway: AI provider unavailable
  • 503 Service Unavailable: System overloaded
  • 504 Gateway Timeout: Request timeout

Error Response Format​

{
  "error": {
    "code": "FLOW_EXECUTION_FAILED",
    "message": "Workflow execution failed due to AI provider timeout",
    "details": {
      "execution_id": "flow_content_processing_xyz789",
      "failed_step": "seo_optimization",
      "provider": "openai",
      "timeout_duration": 30,
      "retry_attempts": 3,
      "partial_results": {
        "completed_steps": ["content_analysis", "quality_assessment"],
        "available_data": {...}
      }
    },
    "recovery_options": [
      {
        "action": "retry_with_different_provider",
        "description": "Retry the failed step with Ollama provider",
        "estimated_cost": 0.000,
        "estimated_time": 15
      },
      {
        "action": "continue_without_failed_step",
        "description": "Skip SEO optimization and proceed to final review",
        "impact": "Reduced optimization quality"
      },
      {
        "action": "restart_from_checkpoint",
        "description": "Restart from the last successful checkpoint",
        "estimated_cost": 0.067,
        "estimated_time": 45
      }
    ],
    "timestamp": 1640995800,
    "request_id": "req_abc123def456",
    "support_reference": "sr_2025_0124_001",
    "documentation_url": "https://docs.example.com/errors/flow-execution-failed"
  }
}

Common Error Codes​

CodeDescriptionRecovery Actions
INVALID_FLOW_DEFINITIONFlow configuration is invalidValidate and fix flow definition
FLOW_NOT_FOUNDSpecified flow doesn't existCheck flow ID, create flow if needed
EXECUTION_TIMEOUTWorkflow execution timed outIncrease timeout, optimize flow steps
PROVIDER_UNAVAILABLEAI provider is offlineUse fallback provider, retry later
INSUFFICIENT_CREDITSAccount credit limit reachedAdd credits, upgrade plan
VALIDATION_FAILEDInput validation failedFix input data, check requirements
CONCURRENT_LIMIT_EXCEEDEDToo many concurrent executionsWait for completion, upgrade limits
STEP_DEPENDENCY_FAILEDRequired step dependency failedCheck dependencies, fix prerequisites
AGENT_DECISION_ERRORAI agent decision logic failedReview agent configuration
MEMORY_LIMIT_EXCEEDEDWorkflow exceeded memory limitsOptimize data processing, increase limits

SDKs and Client Libraries​

PHP/Drupal SDK​

<?php

use Drupal\ai_agentic_workflows\Client\WorkflowClient;

// Initialize client
$client = new WorkflowClient([
  'base_url' => 'https://your-site.com/ai-workflows/api',
  'api_token' => 'your-api-token',
  'timeout' => 300,
  'retry_attempts' => 3
]);

// Execute workflow synchronously
$result = $client->executeFlow('advanced_content_processing', [
  'content' => $content,
  'user_context' => ['role' => 'editor'],
  'options' => ['quality_level' => 'premium']
]);

// Execute asynchronously with monitoring
$execution_id = $client->executeFlowAsync('large_batch_processing', [
  'batch_data' => $batch_items,
  'processing_options' => ['parallel' => true]
], [
  'webhook_url' => 'https://mysite.com/webhook/progress',
  'email_notifications' => true
]);

// Monitor execution
$status = $client->getExecutionStatus($execution_id);
while ($status['status'] === 'running') {
  sleep(10);
  $status = $client->getExecutionStatus($execution_id);
  echo "Progress: {$status['progress']['overall_percentage']}%\n";
}

// Get recommendations
$recommendations = $client->getFlowRecommendations([
  'content_type' => 'technical_article',
  'complexity' => 'high',
  'target_audience' => 'developers'
], [
  'max_cost' => 1.00,
  'min_confidence' => 0.85
]);

foreach ($recommendations['recommendations'] as $rec) {
  echo "Recommended: {$rec['flow_id']} (confidence: {$rec['confidence']})\n";
}

JavaScript/Node.js SDK​

const { WorkflowClient } = require('@ai-workflows/client');

// Initialize client
const client = new WorkflowClient({
  baseUrl: 'https://your-site.com/ai-workflows/api',
  apiToken: process.env.AI_WORKFLOWS_TOKEN,
  timeout: 300000,
  retryAttempts: 3
});

// Execute workflow with comprehensive monitoring
async function processContent(content, options = {}) {
  try {
    // Get recommendations first
    const recommendations = await client.getFlowRecommendations({
      content_type: 'blog_post',
      content_length: content.length,
      target_audience: options.audience || 'general'
    }, {
      max_cost: options.budget || 0.50,
      min_confidence: 0.8
    });

    const bestFlow = recommendations.recommendations[0];
    console.log(`Using recommended flow: ${bestFlow.flow_id}`);
    
    // Execute the recommended workflow
    const execution = await client.executeFlow(bestFlow.flow_id, {
      content: content,
      user_context: options.userContext,
      processing_options: options.processing
    }, {
      monitoring: {
        enable_detailed_logging: true,
        track_intermediate_results: true
      },
      callback_config: {
        webhook_url: options.webhookUrl
      }
    });

    return {
      success: true,
      execution_id: execution.execution_id,
      results: execution.step_results,
      metrics: execution.performance_metrics
    };

  } catch (error) {
    console.error('Workflow execution failed:', error);
    
    // Handle specific error types
    if (error.code === 'PROVIDER_UNAVAILABLE') {
      // Retry with fallback provider
      return await client.executeFlow(bestFlow.flow_id, {
        content: content,
        // ... context
      }, {
        ai_options: {
          preferred_providers: ['ollama'],  // Use local provider
          fallback_enabled: true
        }
      });
    }
    
    throw error;
  }
}

// Real-time monitoring with Server-Sent Events
function monitorExecution(executionId, onUpdate) {
  const eventSource = new EventSource(
    `${client.baseUrl}/execution-stream/${executionId}`,
    {
      headers: {
        'Authorization': `Bearer ${client.apiToken}`
      }
    }
  );

  eventSource.onmessage = (event) => {
    const data = JSON.parse(event.data);
    onUpdate(data);
  };

  eventSource.addEventListener('step_completed', (event) => {
    const stepData = JSON.parse(event.data);
    console.log(`Step completed: ${stepData.step_id}`);
  });

  eventSource.addEventListener('execution_completed', (event) => {
    const results = JSON.parse(event.data);
    console.log('Execution completed:', results);
    eventSource.close();
  });

  return eventSource;
}

// Usage example
(async () => {
  const content = "Your content to process...";
  
  const result = await processContent(content, {
    audience: 'developers',
    budget: 1.00,
    userContext: { role: 'editor', department: 'engineering' },
    webhookUrl: 'https://myapp.com/webhook/workflow-complete'
  });

  if (result.success) {
    console.log('Processing completed successfully');
    console.log('Final quality score:', result.results.final_analysis?.quality_score);
    console.log('Total cost:', result.metrics.total_cost);
  }
})();

Python SDK​

import asyncio
import json
from ai_workflows import WorkflowClient, WebhookHandler
from typing import Dict, List, Optional

class WorkflowManager:
    def __init__(self, api_token: str, base_url: str):
        self.client = WorkflowClient(
            base_url=base_url,
            api_token=api_token,
            timeout=300,
            retry_attempts=3
        )
    
    async def process_content_batch(self, 
                                  content_items: List[Dict], 
                                  flow_preferences: Dict = None) -> List[Dict]:
        """Process multiple content items with optimal flow selection."""
        
        results = []
        
        for item in content_items:
            # Get personalized recommendations
            recommendations = await self.client.get_flow_recommendations(
                context={
                    'content_type': item.get('type', 'article'),
                    'content_length': len(item['content']),
                    'target_audience': item.get('audience', 'general'),
                    'current_quality_metrics': item.get('quality_metrics', {})
                },
                preferences={
                    'max_cost': flow_preferences.get('budget_per_item', 0.50),
                    'max_duration': flow_preferences.get('max_duration', 300),
                    'min_confidence': 0.85,
                    'prioritize': 'quality_over_speed'
                }
            )
            
            if not recommendations['recommendations']:
                results.append({
                    'item_id': item['id'],
                    'success': False,
                    'error': 'No suitable workflows found'
                })
                continue
            
            best_flow = recommendations['recommendations'][0]
            
            try:
                # Execute workflow asynchronously
                execution_id = await self.client.execute_flow_async(
                    flow_id=best_flow['flow_id'],
                    context={
                        'content': item['content'],
                        'user_context': item.get('user_context', {}),
                        'processing_options': {
                            'priority': 'high',
                            'quality_level': 'premium'
                        }
                    },
                    options={
                        'monitoring': {
                            'enable_detailed_logging': True,
                            'send_progress_updates': True
                        },
                        'ai_options': {
                            'preferred_providers': ['ollama', 'openai'],
                            'cost_limit': best_flow['estimated_cost'] * 1.5
                        }
                    }
                )
                
                results.append({
                    'item_id': item['id'],
                    'execution_id': execution_id,
                    'estimated_completion': best_flow['estimated_duration'],
                    'status': 'processing'
                })
                
            except Exception as e:
                results.append({
                    'item_id': item['id'],
                    'success': False,
                    'error': str(e)
                })
        
        return results
    
    async def monitor_executions(self, execution_ids: List[str]) -> Dict[str, Dict]:
        """Monitor multiple workflow executions concurrently."""
        
        async def get_status(execution_id):
            return execution_id, await self.client.get_execution_status(
                execution_id, 
                include_details=True,
                include_predictions=True
            )
        
        # Get status for all executions concurrently
        tasks = [get_status(eid) for eid in execution_ids]
        status_results = await asyncio.gather(*tasks, return_exceptions=True)
        
        statuses = {}
        for result in status_results:
            if isinstance(result, Exception):
                continue
            execution_id, status = result
            statuses[execution_id] = status
        
        return statuses
    
    async def wait_for_completion(self, 
                                execution_ids: List[str],
                                timeout: int = 1800,
                                progress_callback: Optional[callable] = None) -> Dict[str, Dict]:
        """Wait for multiple workflows to complete with progress monitoring."""
        
        completed_results = {}
        start_time = asyncio.get_event_loop().time()
        
        while len(completed_results) < len(execution_ids):
            current_time = asyncio.get_event_loop().time()
            if current_time - start_time > timeout:
                break
            
            # Check status of pending executions
            pending_ids = [eid for eid in execution_ids if eid not in completed_results]
            statuses = await self.monitor_executions(pending_ids)
            
            for execution_id, status in statuses.items():
                if status['status'] in ['completed', 'failed', 'cancelled']:
                    completed_results[execution_id] = status
                elif progress_callback:
                    progress_callback(execution_id, status)
            
            # Wait before next check
            await asyncio.sleep(10)
        
        return completed_results

# Usage example
async def main():
    manager = WorkflowManager(
        api_token="your-api-token",
        base_url="https://your-site.com/ai-workflows/api"
    )
    
    # Content batch for processing
    content_items = [
        {
            'id': 'article_1',
            'content': 'Technical article content...',
            'type': 'technical_documentation',
            'audience': 'developers',
            'quality_metrics': {'readability': 6.2, 'seo_score': 45}
        },
        {
            'id': 'article_2', 
            'content': 'Blog post content...',
            'type': 'blog_post',
            'audience': 'general',
            'quality_metrics': {'readability': 7.8, 'seo_score': 62}
        }
    ]
    
    # Process content batch
    results = await manager.process_content_batch(
        content_items,
        flow_preferences={
            'budget_per_item': 0.75,
            'max_duration': 240
        }
    )
    
    # Extract execution IDs for monitoring
    execution_ids = [r['execution_id'] for r in results if 'execution_id' in r]
    
    # Monitor progress
    def progress_update(execution_id, status):
        print(f"Execution {execution_id}: {status['progress']['overall_percentage']:.1f}% complete")
    
    # Wait for completion
    final_results = await manager.wait_for_completion(
        execution_ids,
        timeout=1800,
        progress_callback=progress_update
    )
    
    # Process results
    for execution_id, result in final_results.items():
        if result['status'] == 'completed':
            print(f"✅ Execution {execution_id} completed successfully")
            print(f"   Quality improvement: {result.get('quality_improvement', 'N/A')}")
            print(f"   Total cost: ${result.get('cost_tracking', {}).get('current_cost', 'N/A')}")
        else:
            print(f"❌ Execution {execution_id} failed: {result.get('error', 'Unknown error')}")

if __name__ == "__main__":
    asyncio.run(main())

Support and Resources​

Developer Tools​

Support Channels​

Service Level Agreements​

  • Uptime: 99.9% availability guarantee
  • Response Time: < 200ms average for API calls
  • Support Response: < 4 hours for technical issues
  • Data Retention: 90 days for execution logs and results

Last Updated: 2025-01-24
API Version: 2.0.0
Documentation Version: 1.0.0