| name | summarize-incidents |
| description | Generates incident summaries and post-mortem reports with advanced AI capabilities including NLP summarization, ML-based root cause analysis, and predictive incident insights. Use when documenting incidents, learning from failures, or implementing intelligent incident management. |
| license | AGPLv3 |
| metadata | {"author":"agentic-reconciliation-engine","version":"2.0","category":"enterprise","risk_level":"medium","autonomy":"conditional","layer":"temporal"} |
| compatibility | Requires Python 3.8+, cloud provider CLI tools (AWS CLI, Azure CLI, gcloud), and access to multi-cloud monitoring systems |
| allowed-tools | Bash Read Write Grep |
Incident Summary — Multi-Cloud Enterprise Automation with Advanced AI
Purpose
Enterprise-grade automation solution for incident summary operations across AWS, Azure, GCP, and on-premise environments with advanced AI capabilities including NLP-powered summarization, machine learning root cause analysis, and predictive incident insights to maximize operational efficiency while maintaining security and compliance standards.
When to Use
- incident summary operations across multi-cloud environments
- Advanced AI analysis including NLP summarization and ML root cause detection
- Automation and optimization of incident summary workflows
- Monitoring and management of incident summary resources
- Compliance and governance for incident summary activities
- Predictive incident prevention using historical pattern analysis
Inputs
- operation: Operation type (required)
- targetResource: Target resource identifier (required)
- cloudProvider: Cloud provider -
aws|azure|gcp|onprem|all (optional, default: all)
- parameters: Operation-specific parameters including AI model configurations (optional)
- environment: Target environment (optional, default:
production)
- dryRun: Dry run mode (optional, default:
true)
- historicalData: Include historical incident data for pattern analysis (optional, default:
false)
Process
- Cloud Provider Detection: Identify target cloud providers and environments
- Input Validation: Comprehensive parameter validation and security checks
- Multi-Cloud Context Analysis: Analyze current state across all providers
- AI-Powered Analysis: Apply NLP summarization, ML clustering for root causes, and predictive modeling
- Operation Planning: Generate optimized execution plan with AI insights
- Safety Assessment: Risk analysis and impact evaluation across providers
- Execution: Perform operations with monitoring and validation
- Results Analysis: Process results and generate AI-enhanced reports
Outputs
- Operation Results: Detailed execution results and status per provider
- AI-Enhanced Summaries: NLP-generated incident summaries and post-mortems
- ML Root Cause Analysis: Clustered and prioritized potential causes
- Predictive Insights: Recommendations for incident prevention
- Compliance Reports: Validation and compliance status across environments
- Performance Metrics: Operation performance and efficiency metrics by provider
- Recommendations: Optimization suggestions and next steps
- Audit Trail: Complete operation history for compliance across all providers
Environment
- AWS: EKS, EC2, Lambda, CloudWatch, IAM, S3
- Azure: AKS, VMs, Functions, Monitor, Azure AD
- GCP: GKE, Compute Engine, Cloud Functions, Cloud Monitoring
- On-Premise: Kubernetes clusters, VMware, OpenStack, Prometheus
- Multi-Cloud Tools: Terraform, Ansible, Crossplane, Cluster API
Dependencies
- Python 3.8+: Core execution environment
- AI/ML Libraries: scikit-learn, nltk, transformers, pandas, numpy
- Cloud SDKs: boto3, azure-sdk, google-cloud
- NLP Tools: spaCy, textblob for natural language processing
- Kubernetes: kubernetes client for cluster operations
- Multi-Cloud Libraries: terraform-python, ansible-python
Scripts
core/scripts/automation/incident-summary.py: Main automation implementation with AI capabilitiescore/scripts/automation/incident-summary_handler.py: Cloud-specific operationscore/scripts/automation/multi_cloud_orchestrator.py: Cross-provider coordination
Trigger Keywords
incident, summary, automation, enterprise, multi-cloud, aws, azure, gcp, onprem, ai, nlp, ml, predictive, root-cause
Human Gate Requirements
- Production changes: Production environment operations require approval
- High-impact operations: Critical operations require review
- Security changes: Security modifications need validation
- AI Model Updates: Changes to AI models require approval
Enterprise Features
- Multi-tenant Support: Isolated operations per tenant
- Role-based Access Control: Enterprise IAM integration
- Audit Logging: Complete audit trail for compliance
- Performance Monitoring: SLA tracking and metrics
- Security Hardening: Encryption and compliance standards
- Dynamic Code Generation: Agents can modify logic dynamically
- Cross-Cloud Orchestration: Coordinated operations across providers
- AI-Powered Insights: Advanced analytics and predictive capabilities
- Automated Learning: Continuous improvement from incident patterns
Best Practices
- Idempotent Operations: Safe retry mechanisms
- Circuit Breaker Patterns: Resilience against failures
- Rate Limiting: Respect API limits and implement backpressure
- Graceful Degradation: Fallback strategies when providers are unavailable
- Comprehensive Testing: Integration tests and compliance validation
- Security First: Zero-trust architecture and principle of least privilege
- AI Model Validation: Regular validation of ML models and NLP accuracy
- Data Privacy: Ensure incident data handling complies with privacy regulations
