Leveraging GitHub Copilot as an Engineering Manager
David O'Regan
As a Senior Engineering Manager at GitHub working on Copilot, I’ve had the unique opportunity to experience firsthand how AI assistants can transform software development workflows. Here’s how I’ve leveraged GitHub Copilot to improve my team’s productivity and code quality.
The Engineering Manager’s Perspective
Engineering managers occupy a unique position, bridging the gap between technical expertise and leadership responsibilities. While we may not write code daily like our team members, our technical background remains crucial for effective leadership. GitHub Copilot has emerged as a powerful ally in maintaining technical sharpness while managing teams and automating workflows that improve team productivity.
Automating Team Workflows with Copilot
As an engineering manager, one of my most valuable use cases for GitHub Copilot has been creating automated pipelines that reduce manual work for my teams. With Copilot’s assistance, I’ve built several automation tools that have transformed how we work:
- Weekly team newsletters: Copilot helped me develop scripts that aggregate team accomplishments, ongoing work, and upcoming milestones into automated newsletters, keeping stakeholders informed without manual reporting.
- CI/CD workflow automation: I used Copilot to create GitHub Actions workflows that automatically label PRs, assign reviewers, and trigger appropriate CI pipelines based on code changes.
- Incident management automation: With Copilot’s help, I built an automatic triage system that categorizes incoming incidents, assigns priority levels, and routes them to the appropriate team members.
- Board automation: Copilot assisted in creating scripts that sync our project boards with specific GitHub events, automatically moving cards when PRs are created, reviewed, or merged.
“Building these automations with Copilot has saved my team approximately 15-20 hours per week of manual work, allowing us to focus on higher-impact tasks while maintaining better visibility into our workflows.”
Code Reviews: Deeper Insights in Less Time
One of the most impactful ways I use GitHub Copilot as an engineering manager is during code reviews. By asking Copilot to explain complex code patterns, identify potential edge cases, or suggest performance improvements, I’m able to provide more valuable feedback to my team members. This improves code quality while also serving as a learning opportunity for everyone involved.
“Copilot has reduced the cognitive load during reviews by 40%, allowing me to focus on architectural decisions and mentoring rather than syntax or boilerplate issues.”
Architectural Documentation
Documentation is often an afterthought in fast-paced development environments. I use GitHub Copilot to generate and improve architectural documentation, making it easier for team members to understand system design decisions and technical constraints. By prompting Copilot with the right questions, we can quickly create comprehensive documentation that evolves alongside our codebase.
For example, we’ve used Copilot to document:
- API contracts and interfaces
- System architecture diagrams (with additional visual tools)
- Performance expectations and bottlenecks
- Migration strategies for legacy systems
Enhancing Team Velocity with Copilot Agents
Beyond coding assistance, I’ve leveraged Copilot’s agents to create intelligent workflows that integrate with various systems. These agents monitor and respond to events across our development ecosystem:
- Automated PR quality checks: Custom agents review PRs for best practices, documentation requirements, and potential security issues before human review.
- Knowledge base integration: When developers encounter errors, agents automatically search our internal knowledge base and suggest relevant solutions.
- Issue triaging: Agents analyze new GitHub issues, categorize them, suggest priority levels, and recommend team members best suited to address them.
- Dependency monitoring: Custom workflows built with Copilot help identify outdated dependencies and security vulnerabilities, creating automated PRs to update them.
These automations have not only improved team efficiency but have also enhanced the quality of our work by ensuring consistent processes and reducing human error in repetitive tasks.
Best Practices for Engineering Managers
Based on my experience, here are some best practices for engineering managers looking to leverage GitHub Copilot effectively:
- Use Copilot to stay technically sharp. Regularly work through coding exercises or small projects to maintain your technical skills.
- Automate repetitive workflows. Identify manual processes that consume team time and use Copilot to build automation scripts.
- Leverage Copilot for architectural discussions. Generate alternative approaches to discuss with your team.
- Build custom agents for team-specific tasks. Use Copilot to develop specialized tools that address your team’s unique challenges.
- Ask Copilot to explain complex code. This helps you provide more valuable feedback during code reviews.
- Generate documentation templates. Use Copilot to create consistent documentation structures for your team.
- Analyze technical debt. Ask Copilot to identify refactoring opportunities in your codebase.
Case Study: Team Productivity Boost
When I introduced automated issue triaging and workflow management built with Copilot to my team, we saw tangible improvements:
- 35% reduction in time spent on administrative tasks
- 28% faster mean time to resolution for customer-reported issues
- 91% accuracy in automatic priority assignment for new issues
- 42% decrease in context switching for developers
The most significant impact wasn’t just efficiency but team morale. Engineers could focus on solving interesting technical problems instead of repetitive process tasks, leading to higher job satisfaction and lower burnout rates.
Conclusion
GitHub Copilot has become an indispensable tool in my engineering management toolkit. By leveraging AI assistance for code reviews, documentation, onboarding, and—most importantly—building automation systems that eliminate repetitive work, I’ve been able to provide more value to my team while maintaining technical sharpness. As AI assistants continue to evolve, the opportunities for engineering managers to leverage these tools will only expand.
What’s your experience using AI coding assistants in engineering management? Have you built any automation tools or workflows with GitHub Copilot? I’d love to hear your thoughts and strategies in the comments below.
About the Author
David O'Regan is a Senior Engineering Manager at GitHub, leading teams responsible for GitHub Copilot. With a background in frontend engineering and AI, David is passionate about the intersection of artificial intelligence and developer tooling.
