AI Investigation Process

StackPilot's AI investigation engine is the core of our intelligent incident response system. When an incident occurs, our AI agent automatically begins a comprehensive analysis that would typically take engineers hours to complete manually.

How AI Investigation Works

1. Automatic Activation

AI investigation begins immediately when:

• New tickets are created from monitoring alerts
• Anomalies are detected in connected systems
• Manual investigations are requested by team members
• Escalation triggers fire for unresolved incidents

2. Multi-Source Data Gathering

The AI agent simultaneously collects data from:

• Error tracking systems (Sentry, Rollbar) for stack traces and exceptions
• APM tools (Datadog, New Relic) for performance metrics and traces
• Log aggregation (Splunk, ELK Stack) for relevant log entries
• Version control (GitHub, GitLab) for recent code changes
• Deployment systems (Jenkins, GitHub Actions) for pipeline data

3. Intelligent Correlation

StackPilot's AI performs advanced correlation analysis:

• Temporal correlation - Aligns incident timing with deployments and code changes
• Code impact analysis - Identifies which commits might have introduced issues
• Pattern recognition - Compares with historical incidents and resolutions
• Dependency mapping - Understands service relationships and cascading effects

AI Analysis Components

Code-Aware Root Cause Analysis

StackPilot's unique strength is code-level understanding:

• Commit correlation - Links errors to specific code changes with confidence scores
• Stack trace analysis - Identifies problematic code paths and methods
• Dependency analysis - Maps how code changes affect downstream services
• Regression detection - Identifies when new code introduces old bugs

Log Query Autocomplete

AI-powered log analysis includes:

• Intelligent query generation based on error patterns
• Contextual filtering using incident metadata
• Anomaly detection in log patterns and volumes
• Cross-service log correlation for distributed systems

Timeline Generation

Automated incident timeline construction:

• Event sequencing from multiple data sources
• Impact propagation tracking across services
• Human action integration combining AI and manual investigation
• Visual timeline for easy incident comprehension

Pattern Learning

Continuous improvement through:

• Historical incident analysis for pattern recognition
• Resolution outcome tracking to validate AI recommendations
• Team feedback integration to improve future analysis
• Cross-team learning from similar incidents in other projects

AI Investigation Outputs

Root Cause Hypothesis

For each incident, StackPilot generates:

• Primary hypothesis with confidence level
• Supporting evidence from multiple data sources
• Alternative theories for complex or ambiguous cases
• Confidence scoring based on data quality and correlation strength

Automated Recommendations

AI-generated suggestions include:

• Immediate mitigation steps to reduce impact
• Investigation priorities for manual follow-up
• Code fix recommendations with specific line-level changes
• Monitoring improvements to prevent similar incidents

Code Fix Generation

When patterns are clear, StackPilot can:

• Generate specific code fixes for common error patterns
• Create pull requests with proposed changes
• Provide fix explanations detailing why changes resolve the issue
• Include test recommendations to validate fixes

Playbook Creation

Convert investigations into reusable knowledge:

• Runbook generation from successful resolution patterns
• Team-specific procedures based on past incident handling
• Escalation triggers for similar future incidents
• Knowledge base articles for common issue types

Working with AI Findings

Understanding Confidence Levels

StackPilot uses confidence scoring to help you prioritize:

• High Confidence (80-100%) - Strong evidence across multiple data sources
• Medium Confidence (50-79%) - Good evidence but may need validation
• Low Confidence (20-49%) - Initial hypothesis requiring manual investigation
• Exploratory (0-19%) - Potential leads worth investigating

Validating AI Analysis

Best practices for working with AI recommendations:

• Cross-reference findings with your domain knowledge
• Test AI-proposed fixes in non-production environments first
• Validate code correlations by reviewing the actual changes
• Consider alternative explanations for complex incidents

Providing Feedback

Help improve AI accuracy by:

• Rating investigation quality after incident resolution
• Marking correct/incorrect correlations for learning
• Adding manual findings that AI might have missed
• Documenting resolution outcomes for pattern learning

AI Learning and Improvement

Continuous Learning

StackPilot's AI improves through:

• Outcome validation - Learning from actual incident resolutions
• Team feedback - Incorporating human expertise and corrections
• Cross-incident patterns - Building knowledge across similar issues
• Code pattern recognition - Understanding common bug patterns in your codebase

Customization and Tuning

AI behavior can be adapted to your environment:

• Service priority weighting - Focus on critical system components
• Code repository emphasis - Weight repositories by importance
• Alert sensitivity tuning - Adjust to your team's noise tolerance
• Investigation depth controls - Balance thoroughness with speed

Privacy and Security

AI investigation respects your data boundaries:

• On-premises deployment options for sensitive environments
• Data minimization - Only analyzing necessary incident data
• Encryption at rest and in transit for all analysis data
• Audit logging of all AI analysis activities

Advanced Features

Multi-Incident Analysis

For complex scenarios:

• Incident clustering - Grouping related incidents for analysis
• Cross-service impact analysis - Understanding cascading failures
• Timeline merging - Combining multiple incident timelines
• Root cause propagation - Tracking how issues spread across systems

Predictive Analysis

Proactive incident prevention:

• Risk scoring for deployments based on code change analysis
• Anomaly prediction using historical patterns
• Capacity planning insights from performance trends
• Alert fatigue reduction through intelligent alert prioritization

Integration Intelligence

AI-powered tool optimization:

• Connection health monitoring for data source reliability
• Integration recommendations for improved analysis coverage
• Data quality assessment and improvement suggestions
• Custom correlation rules based on your specific tool stack

Best Practices for AI Investigation

Maximizing AI Effectiveness

• Maintain comprehensive integrations for rich data correlation
• Keep deployment information current for accurate code correlation
• Regular feedback provision to improve AI accuracy over time
• Team training on interpreting and acting on AI findings

Balancing AI and Human Intelligence

• Use AI for initial triage and hypothesis generation
• Apply human expertise for complex or novel issues
• Validate AI recommendations before implementing fixes
• Document manual insights to improve future AI analysis

Building AI-Human Collaboration

• Treat AI as an expert team member with specific strengths
• Leverage AI speed for initial analysis while applying human judgment
• Use AI findings as starting points rather than definitive answers
• Build team confidence in AI recommendations through validated outcomes

StackPilot's AI investigation process transforms reactive incident firefighting into proactive, intelligent incident resolution, enabling your team to resolve issues faster while building institutional knowledge for future incidents.