Who should use the Bug Fixing workflow?
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Work
Practical execution plan for bug fixing with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
Confirmation that the fix is stable in production with no adverse effects.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
Confirmation that the fix is stable in production with no adverse effects.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Loom to a documented, repeatable reproduction case that can be shared with the team and used to validate the fix. Then, you pass the output to Kilo Code v7 to a clear, documented root cause statement with supporting evidence (e.g., line of code, configuration key). Then, you pass the output to Factory to a committed code change with passing unit tests that specifically validate the fix. Then, you pass the output to Applitools to a verified fix in a production-like environment with no observed regressions. Then, you pass the output to Jira Software to a closed bug ticket with full traceability and informed stakeholders. Finally, Datadog is used to confirmation that the fix is stable in production with no adverse effects.
Reproduce and Isolate the Bug
A documented, repeatable reproduction case that can be shared with the team and used to validate the fix.
Diagnose Root Cause
A clear, documented root cause statement with supporting evidence (e.g., line of code, configuration key).
Design and Implement the Fix
A committed code change with passing unit tests that specifically validate the fix.
Verify the Fix in a Staging Environment
A verified fix in a production-like environment with no observed regressions.
Document and Communicate the Resolution
A closed bug ticket with full traceability and informed stakeholders.
Monitor Production After Deployment
Confirmation that the fix is stable in production with no adverse effects.
First, obtain a clear, repeatable set of steps that triggers the bug. Use logs, screenshots, or video capture to document the exact environment and input. This ensures you can verify the fix later and avoids chasing phantom issues.
Why Loom: Loom provides screen recording and AI-generated summarization, which directly supports reproducing and documenting the bug visually.
Analyze the reproduction evidence to locate the exact code path, configuration, or data state causing the failure. Use debugging tools, log analysis, and code inspection to trace from symptom to origin.
Why Kilo Code v7: Kilo Code v7 explicitly includes debugging errors and tracing root causes, directly matching the need for root cause diagnosis.
Create a minimal, targeted code change that addresses the root cause without introducing new issues. Write or update unit tests to cover the bug scenario, and ensure the fix follows project coding standards.
Why Factory: Factory provides code generation, automated unit testing, and bug fixing, directly covering the needs for designing and implementing a fix.
Deploy the fix to a staging or development environment that mirrors production. Execute the reproduction steps to confirm the bug is resolved, and run a broader smoke test to check for regressions.
Why Applitools: Applitools provides visual regression testing and cross-browser layout validation, which are critical for verifying fixes in a staging environment.
Update the bug ticket with the root cause, fix description, and verification results. Notify stakeholders (e.g., QA, product manager) and, if applicable, write a postmortem or release note entry.
Why Jira Software: Jira Software is a bug tracking tool that directly supports documenting and communicating the resolution within the workflow.
After the fix is deployed to production, monitor error rates, performance metrics, and user reports for at least one business cycle. This ensures the fix is effective and no new issues have surfaced.
Why Datadog: Datadog provides application performance monitoring and log aggregation, directly matching the need for production monitoring.
§ Before you start
Teams or solo builders working on work tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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