Teaching Your Team to Think Async-First with GitHub Copilot
Teams must learn to ask “Should Copilot do this?” before starting work. This post shows how to teach async-first thinking, delegate routine tasks to AI, and redesign workflows for parallel experimentation.
- The Fundamental Question: “Should Copilot Do This?”
- Practical Async Patterns: Examples That Work
- Redesigning Workflows for Parallel Experimentation
- Teaching the Mindset: What to Delegate, What to Keep
- Overcoming Resistance: “This Feels Like Cheating”
- Conclusion
The Fundamental Question: “Should Copilot Do This?”
Once you’ve built an enablement program (see my companion post on Building a GitHub Copilot Enablement Program That Actually Works), the next challenge is teaching your team a new way of thinking.
The fundamental shift is asking a new question before starting any task: “Should Copilot do this instead of me?”
This isn’t about laziness - rather, it’s about leverage. When Copilot handles routine tasks, developers have more cognitive bandwidth for architecture, business logic, and creative problem-solving. But most developers have years of muscle memory telling them “I must do this myself.”
The core shift your team needs to make is recognizing which work should be delegated to Copilot versus done manually. In this post, I’ll show you practical patterns for async, multi-threaded development where AI handles routine work in parallel while humans orchestrate and make decisions.
Practical Async Patterns: Examples That Work
Let’s look at specific tasks where async thinking delivers massive wins.
Test Generation
Traditional approach: Developers write feature code then spend hours crafting test cases, debugging failures, and iterating until coverage is acceptable.
Async approach with Copilot: You describe behavior in comments as you write features, then ask Copilot to generate comprehensive tests including edge cases. Review the generated tests to ensure assertions meaningfully verify the contract (not just pass), adjust as needed, and move to your next task while Copilot handles integration tests in the background.
This delivers huge time savings while often improving coverage, since AI identifies edge cases humans miss.
Key technique: Create custom agents for testing to encapsulate your team’s patterns and ensure consistent output.
Documentation
Traditional approach: Docs fall out of date because writing them is tedious. Developers ship features, promise to “update docs later,” and never do.
Async approach with Copilot: Copilot generates README sections, API documentation, and code comments alongside your implementation, making it fast enough that developers actually keep docs current.
Key technique: Use Copilot Chat to generate documentation as you go. Prompt: “Generate API documentation for this function including parameters, return values, and usage examples.”
Prototyping Multiple Solutions
Traditional approach: Teams debate approaches theoretically in design meetings. They pick one design, discover limitations during implementation, then either live with them or go back to design. This cycle wastes days.
Async approach with Copilot: Create multiple GitHub issues describing different approaches and assign them all to Copilot coding agent. Within hours, you’re reviewing three actual implementations side by side, making decisions based on real code rather than speculation.
This saves days compared to sequential implementation and dramatically improves decision quality.
Key technique: Write clear, detailed issue descriptions. Copilot coding agent works best when you specify requirements, constraints, and success criteria upfront.
Build Failure Analysis
Traditional approach: Developers scroll through hundreds of lines of logs guessing at root causes. They ping team members for help. They search Stack Overflow. It takes hours to diagnose complex failures.
Async approach with Copilot: GitHub Actions can automatically invoke Copilot to analyze failures, categorize them (code, config, test, infrastructure, transient), and generate remediation plans with the appropriate team member tagged.
You can implement this today with the actions/ai-inference action and GitHub Models (see my post on self-healing devops for details).
Key technique: Set up automated workflows that capture build/test output and feed it to Copilot with context about your repo structure and conventions.
Code Optimization
Traditional approach: Developers manually review code for dead imports, unused variables, inefficient algorithms, and memory leaks. They profile the application, identify hotspots, then spend hours refactoring. Code reviews often catch optimization opportunities too late, after the feature ships.
Async approach with Copilot: Ask Copilot to analyze your codebase for optimization opportunities while you work on new features. Copilot can identify dead code, suggest more efficient algorithms, highlight memory-intensive operations, and recommend performance improvements.
For example, prompt Copilot Chat: “Analyze this module for optimization opportunities including dead code, inefficient loops, and memory usage.”
This is particularly powerful for refactoring legacy code. Create GitHub issues describing different optimization strategies (memory reduction, CPU optimization, code simplification) and assign them to Copilot coding agent. Compare the results to choose the best approach.
Key technique: Combine Copilot analysis with profiling data. Share performance metrics in your prompt: “This function takes 500ms on average with 10k records. Optimize for speed while maintaining correctness.” Copilot can suggest targeted optimizations based on actual bottlenecks rather than premature optimization.
These patterns represent the essence of async, multi-threaded development: AI agents work in parallel while humans orchestrate and make decisions.
Redesigning Workflows for Parallel Experimentation
Async development requires rethinking how your team coordinates work. Traditional rituals assume synchronous, sequential workflows. They break down when developers work on multiple features simultaneously while AI handles background tasks.
What Breaks in Async Workflows
Daily standups: “What did you do yesterday?” becomes less meaningful when you’re orchestrating multiple parallel Coding agent tasks rather than completing one task yourself.
Code review queues: Reviewers expect PRs to arrive sequentially. In async workflows, developers might open multiple PRs for the same feature (testing different approaches) simultaneously.
Deployment schedules: Batch deployments assume teams synchronize to a release train. Async teams ship when features are ready, not on a schedule.
Design reviews: Traditional design reviews debate approaches theoretically. Async teams prototype multiple approaches with AI and compare actual implementations and working prototypes.
New Coordination Patterns
Here’s what works better for async teams:
Repurpose daily standups: Rather than “what I did yesterday, what I’m doing today and what’s blocking me”, change the format to cover what Copilot is working on, what needs review and what prompts are yielding the best results.
Move to continuous code review: Create a policy that requires Copilot Code Review on all PRs, and continuously iterate on Copilot instructions that help guide the review to your standards and conventions.
Enable on-demand deployments: Shift from “we deploy every Friday” to “we deploy when features pass quality gates.” Use GitHub Actions to automate deployments on merge to main. Requires strong automated testing, but eliminates batching delays.
Invest in good quality gates and policies: including Code Quality and GitHub Advanced Security scans, linting and test coverage. You’re aiming to have high confidence that when all automated gates pass, the PR can be shipped as soon as human review is completed. No waiting for Friday.
Shift design reviews to “build and compare”: Instead of debating approaches in a meeting, create multiple issues describing each approach. Assign to Copilot coding agent or have developers prototype with Copilot assistance. Review actual code, not theoretical designs.
Teaching the Mindset: What to Delegate, What to Keep
The hardest part of async thinking isn’t the mechanics - it’s the judgment. Developers need to learn what work Copilot handles well versus what requires human expertise.
Copilot Excels At
- Repetitive code (CRUD operations, boilerplate, type conversions)
- Test generation (unit tests, integration tests, edge cases)
- Documentation (README, API docs, code comments)
- Code transformations (refactoring, format changes, migrations)
- Pattern matching (finding similar code, applying conventions)
- Performance optimization (is this efficient enough?)
- First-draft implementations of well-specified features
Humans Excel At
- Architecture decisions (system design, tech stack choices)
- Business logic validation (does this match requirements?)
- Context synthesis (how does this fit the broader system?)
- Ambiguity resolution (what did the stakeholder really mean?)
Key principle: Use Copilot to accelerate execution, but keep humans in the decision loop for validation and high-level thinking.
Overcoming Resistance: “This Feels Like Cheating”
Some developers resist async thinking because it feels like they’re not “really” coding. They worry about skill atrophy or whether they deserve credit for AI-assisted work.
Reframe the conversation:
- “Using Copilot isn’t cheating. It’s like using a compiler, debugger, or IDE - it’s a tool that makes you more effective.”
- “Your skills shift from typing code to refining specs and reviewing code, which is actually more valuable. Senior engineers spend more time reviewing than typing.”
- “You’re orchestrating complexity. That’s higher-level thinking than implementing details manually.”
Address skill atrophy concerns:
- “You’re still coding. You’re just moving faster on routine work and spending more time on hard problems.”
- “Review every suggestion critically. You’ll learn from seeing multiple approaches to problems.”
- “Experiment with Copilot turning off periodically to ensure you retain fundamentals.”
Celebrate the shift:
- “Think about what you can build now that you couldn’t before. More features? Better quality? Time to learn new skills?”
Most resistance fades once developers experience the productivity boost firsthand.
Conclusion
Teaching async-first thinking is the key to unlocking GitHub Copilot’s full potential. It’s not just about using AI tools - it’s about fundamentally rethinking how work gets done.
Start by teaching your team to ask “Should Copilot do this?” before starting tasks. Show them practical patterns for test generation, documentation, prototyping, and build analysis. Redesign coordination patterns (standups, code review, deployments) to support parallel work. And help them develop judgment about what to delegate versus what requires human expertise.
The shift from sequential, single-threaded development to async, multi-threaded workflows takes practice. But once your team internalizes the mindset, they’ll wonder how they ever worked any other way.
For the leadership and enablement foundation that makes this possible, see my companion post on Building a GitHub Copilot Enablement Program That Actually Works.
Happy orchestrating!
