AI Ops & Incident Orchestrator
Reduce incident resolution time by 70% through intelligent orchestration. Semawork coordinates incident management workflows across ServiceNow, Slack, monitoring tools, and runbooks.
What It Does
The Challenge
Modern IT operations teams face an overwhelming volume of alerts, incidents, and system events that require rapid response and resolution. When a critical system goes down or performance degrades, every minute counts—but traditional incident management processes are slow, manual, and error-prone. Teams spend hours manually triaging incidents, correlating alerts from multiple monitoring tools, determining which team should handle each issue, and coordinating runbook execution across different systems.
The problem is compounded by the fact that incident data lives in silos: monitoring tools like Datadog or New Relic generate alerts, ticketing systems like ServiceNow track incidents, communication platforms like Slack contain context and discussions, and runbooks exist in separate documentation systems. There's no unified view of what's happening, which means incidents get misrouted, duplicate tickets get created, and resolution takes far longer than necessary. This manual coordination doesn't scale, especially as systems become more complex and distributed.
- •Incidents take hours to triage and route to the correct team
- •Manual correlation of alerts and incidents across multiple tools
- •Runbook execution requires manual coordination between systems
- •No unified visibility across monitoring and ticketing platforms
- •High false positive rates waste engineering time
The Solution
Semawork's AI Ops & Incident Orchestrator transforms incident management from a reactive, manual process into an intelligent, automated workflow. Our agents continuously monitor your incident sources—ServiceNow tickets, monitoring alerts, Slack channels, and runbook systems—and automatically detect, classify, and route incidents based on context and historical patterns. The system understands the relationships between different alerts, can correlate related incidents, and knows which team or individual should handle each type of issue based on your organization's structure and expertise.
When an incident is detected, our agents immediately begin the resolution process: they create tickets in ServiceNow with all relevant context, notify the appropriate team in Slack with a summary and links, execute runbooks automatically where safe to do so, and request human approval for critical actions. Throughout the process, the system maintains a unified view of the incident across all tools, ensuring everyone has the same information and reducing confusion. As incidents are resolved, agents learn from the outcomes, improving classification accuracy and routing decisions over time.
- ✓Automatic incident detection and classification using AI-powered analysis
- ✓Intelligent routing to correct team based on incident type, severity, and team expertise
- ✓Automated runbook execution with human approvals for critical steps
- ✓Unified observability across all incident sources in a single dashboard
- ✓Automatic correlation of related alerts and incidents to reduce noise
Common Use Cases
Infrastructure Incidents
When monitoring tools detect infrastructure issues—server failures, network outages, database performance degradation—our agents automatically create ServiceNow incidents, notify the infrastructure team in Slack, and execute standard recovery runbooks. The system correlates related alerts to prevent duplicate tickets and provides a unified view of the incident across all tools.
For example, when Datadog alerts indicate high CPU usage on multiple servers, the agent recognizes this as a potential infrastructure issue, creates a single incident in ServiceNow, routes it to the infrastructure team, and begins executing diagnostic runbooks while waiting for team response.
Application Errors
Application errors detected through error tracking tools or user reports are automatically classified by severity and type, routed to the appropriate development team, and linked to relevant code repositories and documentation. The system can execute automated remediation steps like restarting services or rolling back deployments when configured.
When an application error rate spikes, the agent analyzes the error patterns, determines if it's a known issue or new problem, routes to the correct team based on the affected service, and provides context from logs and monitoring data to accelerate resolution.
Security Alerts
Security alerts from SIEM tools or security scanners are automatically prioritized, routed to the security team, and trigger appropriate response workflows. The system ensures critical security incidents get immediate attention while filtering out false positives based on historical patterns.
When a security scanner detects a potential vulnerability or suspicious activity, the agent immediately creates a high-priority incident, notifies the security team through multiple channels, and begins executing security response runbooks while maintaining an audit trail of all actions taken.
Performance Degradation
Performance issues detected through APM tools or user reports are automatically analyzed to identify root causes, routed to the team responsible for the affected service, and linked to performance dashboards and historical data. The system can execute performance optimization runbooks automatically.
When response times increase beyond thresholds, the agent analyzes performance metrics, identifies the bottleneck, routes to the appropriate team with context, and begins executing performance optimization procedures while the team investigates the root cause.
How It Works
1. Continuous Monitoring
Our agents continuously monitor all your incident sources—ServiceNow for new tickets, monitoring tools like Datadog and New Relic for alerts, Slack channels for incident reports, and runbook systems for execution status. The agents understand the data structures and APIs of each tool, allowing them to extract relevant information and detect patterns that indicate incidents.
2. Intelligent Detection & Classification
When an incident is detected, our AI-powered agents analyze the available data—alert details, error messages, performance metrics, historical patterns—to classify the incident by type, severity, and affected systems. The system uses machine learning to improve classification accuracy over time, learning from how your team resolves different types of incidents.
3. Automatic Correlation
The system automatically correlates related alerts and incidents to prevent duplicate tickets and provide a unified view. For example, if multiple servers show the same error pattern, the agent recognizes this as a single incident rather than creating separate tickets for each server. This correlation reduces noise and helps teams focus on root causes rather than symptoms.
4. Intelligent Routing
Based on incident classification and your organization's structure, the agent routes the incident to the appropriate team or individual. The routing considers factors like team expertise, current workload, on-call schedules, and historical resolution patterns. The system ensures critical incidents get immediate attention while routine issues follow standard workflows.
5. Automated Response
For incidents with known solutions, the agent automatically executes runbooks—sequences of actions that resolve common issues. Critical actions require human approval, ensuring safety while enabling automation. The system maintains a complete audit trail of all actions taken, providing full visibility and compliance.
6. Continuous Learning
As incidents are resolved, the system learns from outcomes—which routing decisions led to faster resolution, which runbooks were most effective, which alerts were false positives. This learning improves the system's performance over time, reducing resolution times and increasing automation rates without manual configuration changes.
ROI Breakdown
Before Semawork
- • 4 hours average incident resolution time
- • 20% incidents auto-resolved
- • $150 per incident handling cost
- • 60% correct classification rate
- • Manual runbook execution
After Semawork
- • 1.2 hours average resolution (70% reduction)
- • 80% incidents auto-resolved
- • $75 per incident (50% cost reduction)
- • 90% correct classification
- • Automated runbook execution
Technical Architecture
AI-Powered Classification Engine
The classification engine uses natural language processing and machine learning to analyze incident data from multiple sources. It understands context from alert messages, error logs, performance metrics, and historical incident patterns to accurately classify incidents by type, severity, and affected systems. The engine continuously learns from resolution outcomes, improving accuracy over time without manual retraining.
The classification engine processes structured data from APIs as well as unstructured data from logs and messages, using transformer-based models to extract relevant information and make classification decisions. This allows the system to handle incidents even when data formats vary or information is incomplete.
Correlation and Deduplication
The correlation engine identifies relationships between alerts and incidents using temporal analysis, pattern matching, and dependency mapping. It recognizes when multiple alerts represent a single incident, when incidents are related to the same root cause, and when incidents are independent. This reduces noise and prevents duplicate ticket creation.
Correlation algorithms analyze timing patterns, error signatures, affected systems, and historical relationships to determine incident relationships. The system maintains a graph of incident relationships that helps teams understand root causes and dependencies.
Intelligent Routing System
The routing system uses multiple factors to determine the best team or individual to handle each incident. It considers team expertise, current workload, on-call schedules, historical resolution patterns, and incident characteristics. The system can route to individuals, teams, or escalation paths based on severity and type.
Routing decisions are made using a combination of rule-based logic and machine learning models that learn from historical routing outcomes. The system can adapt to organizational changes, team restructuring, and evolving expertise without manual reconfiguration.
Runbook Execution Engine
The runbook execution engine can execute automated remediation workflows across multiple systems. It supports conditional logic, loops, error handling, and human approval points. Runbooks can interact with APIs, execute scripts, update systems, and coordinate actions across tools. Critical actions require human approval, ensuring safety while enabling automation.
Runbooks are defined using a visual workflow designer or YAML configuration, making them easy to create and modify. The execution engine maintains state, handles retries, manages timeouts, and provides detailed execution logs for every step.
Integration Examples
ServiceNow Integration
Semawork integrates with ServiceNow through REST APIs to create, update, and query incidents. When an incident is detected from monitoring tools, Semawork automatically creates a ServiceNow incident with all relevant context including alert details, affected systems, and correlation information. The integration maintains bidirectional sync, ensuring that updates in ServiceNow are reflected in Semawork and vice versa.
The ServiceNow integration supports custom fields, assignment rules, and workflow automation. It can create incidents in specific categories, assign them to appropriate groups, link related incidents, and update status based on resolution progress.
Monitoring Tools Integration
Semawork integrates with monitoring tools like Datadog, New Relic, Prometheus, and custom monitoring systems through their APIs and webhook interfaces. The system subscribes to alert streams, processes alert data in real-time, and correlates alerts with existing incidents. Integration supports both push (webhooks) and pull (API polling) models depending on tool capabilities.
The monitoring integration can filter alerts based on severity, type, or custom rules, reducing noise and focusing on actionable incidents. It also retrieves historical metrics and performance data to provide context for incident resolution.
Slack and Communication Integration
Semawork integrates with Slack, Microsoft Teams, and other communication platforms to notify teams about incidents, provide status updates, and enable interactive incident management. When an incident is detected, Semawork creates a dedicated channel or thread, posts incident details, and provides interactive buttons for acknowledging, escalating, or resolving incidents.
The communication integration supports @mentions for routing, threaded conversations for context, and rich message formatting with links, attachments, and status indicators. Teams can interact with incidents directly from Slack without switching to other tools.
Runbook System Integration
Semawork can execute runbooks stored in various systems including Confluence, Notion, GitHub, or custom runbook repositories. The system retrieves runbook definitions, executes steps programmatically, and tracks execution status. Runbooks can include API calls, script execution, system commands, and human approval points.
The runbook integration supports version control, rollback capabilities, and execution history. Runbooks can be parameterized, allowing the same runbook to handle different incident types with appropriate parameters.
Additional Use Cases
Capacity Planning Incidents
When monitoring indicates capacity thresholds are approaching, Semawork can automatically create incidents, notify infrastructure teams, and trigger capacity planning workflows. The system analyzes trends to predict when capacity issues will occur and proactively initiates scaling or resource allocation processes.
This proactive approach helps prevent outages by addressing capacity issues before they become critical incidents, reducing downtime and improving system reliability.
Deployment Failures
When CI/CD pipelines fail or deployments encounter errors, Semawork automatically detects the failure, analyzes the error type, routes to the appropriate team, and can trigger rollback procedures. The system correlates deployment failures with infrastructure incidents to identify root causes.
This automation reduces the time between deployment failure and resolution, minimizing impact on users and ensuring rapid recovery from deployment issues.
Data Quality Issues
When data quality monitoring detects anomalies, missing data, or data corruption, Semawork creates incidents, routes to data engineering teams, and can trigger data validation and repair workflows. The system understands data pipeline dependencies and can identify upstream causes of data quality issues.
This helps maintain data integrity and ensures that data quality issues are detected and resolved quickly before they impact downstream systems or business operations.
Third-Party Service Outages
When third-party services experience outages or degradation, Semawork detects the impact on your systems, creates incidents, and routes to appropriate teams. The system can check service status pages, monitor API response times, and correlate third-party issues with internal incidents.
This helps teams quickly identify when issues are caused by external dependencies rather than internal systems, reducing investigation time and enabling faster communication with stakeholders.
Tools Orchestrated
Semawork integrates with a wide range of incident management, monitoring, communication, and runbook tools. The system orchestrates workflows across these tools, providing a unified view and automated coordination.
Don't see your tool? Semawork can integrate with any tool that provides REST APIs, webhooks, or other programmatic interfaces. Custom integrations can be developed for tools not yet in our standard library.
Frequently Asked Questions
How does Semawork integrate with our existing incident management tools?
Semawork integrates seamlessly with ServiceNow, Slack, monitoring tools like Datadog and New Relic, and runbook systems through APIs. Our agents connect to your existing tools without requiring any changes to your current infrastructure. The system reads from and writes to your tools, orchestrating workflows across them while maintaining your existing processes and data structures.
What happens if the AI makes an incorrect routing decision?
All routing decisions can be overridden by your team, and the system learns from corrections to improve accuracy over time. For critical incidents, you can configure the system to require human approval before routing. The system also maintains a complete audit trail of all decisions, allowing you to review and refine routing logic based on actual outcomes.
Can Semawork execute runbooks automatically without human approval?
Yes, but you have full control over which runbooks can be executed automatically and which require human approval. You can configure different approval requirements based on incident severity, type, or affected systems. For example, you might allow automatic execution of routine runbooks for low-severity incidents while requiring approval for any actions affecting production systems.
How does the system reduce false positives?
The system uses machine learning to identify patterns in alerts and incidents, learning which alerts are typically false positives based on historical resolution data. It also correlates related alerts to identify when multiple alerts represent a single incident, reducing noise. Over time, the system becomes more accurate at filtering false positives while ensuring genuine incidents are never missed.
What kind of ROI can we expect from implementing this solution?
Typical results include 70% reduction in mean time to resolution, 80% of incidents resolved automatically, 50% reduction in per-incident handling costs, and 90% accuracy in incident classification. The exact ROI depends on your current incident volume and resolution times, but most organizations see significant time and cost savings within the first quarter of implementation.
How long does it take to implement Semawork for incident orchestration?
Implementation typically takes 2-4 weeks, depending on the complexity of your incident management workflows and the number of tools being integrated. The process includes connecting to your tools, configuring routing rules, setting up runbooks, and training the system on your historical incident data. Most organizations see value within the first week as the system begins orchestrating workflows.
Ready to accelerate incident resolution?
Let's discuss how Semawork can orchestrate your incident management workflows and reduce resolution times.