The Challenge
Post-deployment monitoring is reactive, manual, and often too late to prevent user impact:
- Delayed regression detection: Teams discover new bugs hours or days after deployment when user complaints spike or dashboards show anomalies
- Manual comparison overhead: Developers must manually compare error rates, performance metrics, and new error groups between releases across multiple Raygun screens
- Lack of immediate feedback: CI/CD pipelines deploy successfully, but teams lack automated post-deployment health checks to validate actual stability
- Unclear deployment attribution: When errors increase, it’s difficult to quickly determine whether the latest release is responsible or if external factors are at play
The Autohive Solution
Autohive’s Raygun integration enables AI assistants to perform automated deployment impact analysis, providing instant visibility into release health and regression detection.
Automated Post-Deployment Investigation
Immediately after deployment, your AI assistant retrieves the latest deployment record from Raygun and begins analyzing error groups, occurrence rates, and affected user counts specific to that release.
Error Group Comparison
The assistant compares error groups introduced in the new deployment against historical patterns, identifying genuinely new bugs versus pre-existing issues that may be surfacing under different conditions.
Performance Trend Analysis
Using Raygun’s time-series performance metrics, the assistant analyzes load times, response rates, and user experience distributions before and after deployment to detect performance regressions.
Benefits
- Immediate Regression Detection - Identify deployment-related issues within minutes instead of waiting for user reports
- Proactive Rollback Decisions - Get actionable intelligence to decide whether to rollback, hotfix, or continue monitoring
- Deployment Confidence - Validate release stability automatically, building trust in your CI/CD pipeline
- Reduced User Impact - Catch critical issues before they affect large user populations
How It Works
- Deployment Trigger - CI/CD pipeline completes and creates deployment record in Raygun
- Automated Analysis - AI assistant retrieves deployment details and begins analyzing error and performance data
- Comparison & Correlation - Assistant compares new error groups, occurrence rates, and performance metrics against baseline
- Impact Report - Team receives summary of deployment health, new errors introduced, performance changes, and recommended actions
Getting Started
- Sign up at app.autohive.com
- Connect your Raygun integration from the marketplace
- Configure your AI assistant to monitor deployments
- Deploy with confidence knowing regressions will be caught immediately
Learn more about the Raygun integration on the Autohive marketplace.
