Cursor BugBot and Copilot Coding Agents: An AI-driven review and implementation workflow
Recently, while working on my Terraform modules, I had an idea: What about using BugBot to check Copilot’s Coding Agents? With Copilot Coding Agents already helping automate PRs and refactors, I decided to give BugBot a try as an additional reviewer.
The Experiment
I assigned Copilot Coding Agents to implement new features and fixes in my repositories. Once the PRs were ready, I ran BugBot to review the changes. The results were fascinating:
BugBot quickly detected issues that Copilot’s own review had missed, especially around edge cases and variable usage. Its bug detection capabilities are on another level compared to Copilot’s built-in review features.
For example, BugBot flagged overly restrictive regex validation and unused variables in a Copilot-generated PR. It provided actionable feedback, which I then relayed to Copilot for fixes.
Highlights
- BugBot excels at bug detection: It found subtle issues in Copilot Coding Agents’ PRs, providing detailed explanations and suggestions.
- Copilot Coding Agents excel at implementation: They quickly propose and implement solutions, and can interpret and act on BugBot’s feedback.
- AI is not infallible: Even with both tools, bugs or outdated code can slip through. For example, Copilot generated a workflow using a deprecated GitHub Action, and BugBot didn’t catch it either.
Conclusion
Combining Copilot Coding Agents and BugBot creates a powerful AI-driven review and implementation workflow. Each tool has its strengths: BugBot for deep bug detection, Copilot for rapid solution delivery. However, human oversight is still essential to catch edge cases and evolving platform changes.
Note: BugBot is a paid service for automated code reviews. My tests and experiments described here were conducted during the free trial period, which allowed me to evaluate its capabilities without incurring any cost.
