
Overviews
How it works?
Run tests on deployment triggers
AI agents execute your Bugbug test suites when new code is deployed or merged, ensuring quality checks happen without manual intervention and catching issues before they reach production.
Monitor test results in real-time
Track the status and outcomes of test runs as they complete, allowing your AI agent to identify failures and initiate appropriate responses based on test results.
Send failure notifications to teams
Alert developers and stakeholders through Slack, email, or project management tools when tests fail, ensuring quick response to potential issues in your application.
Schedule recurring test runs
Execute test suites on predefined schedules to monitor application health continuously, catching regressions or issues that emerge over time without manual test initiation.
Create detailed test reports
Compile test results into comprehensive reports and dashboards, providing visibility into application quality, test coverage, and trending issues for stakeholder review.
Update project management tools
Create or update tickets in Jira, Linear, or other platforms when tests fail, ensuring issues are tracked and assigned for resolution within your development workflow.
Trigger rollback procedures
Initiate deployment rollbacks or prevent production releases when critical tests fail, protecting your users from experiencing bugs and maintaining application stability.
Collect performance metrics
Extract timing and performance data from test runs to monitor application speed and responsiveness, identifying performance degradation before it impacts user experience.

Configure
Build
Continuous testing pipeline
Build an AI-driven workflow that runs Bugbug tests on every code commit, analyzes results, blocks merges when tests fail, and notifies relevant team members with detailed failure information.
“You can’t do this anywhere else.”



















































Your stack,
connected.

