WRAP: GitHub's Framework for Getting Real Work Done with Copilot

TL;DR

• GitHub engineers developed WRAP (Write, Refine, Atomic, Pair) after a year of internal testing with Copilot coding agent
• The framework treats coding agents like junior developers: give them context, break down tasks, and play to their strengths
• Coding agents excel at tireless execution and repetitive work; humans handle ambiguity and cross-system thinking
• If you have backlog debt — dependency updates, test coverage gaps, refactoring — this is how you actually clear it

The Big Picture

GitHub's engineers have been dogfooding Copilot coding agent for over a year. Not in demos. Not in marketing materials. In production, on real backlogs, with real tech debt.

What they learned: coding agents aren't magic. They're tools. And like any tool, there's a right way and a wrong way to use them.

The wrong way: throw vague issues at the agent and hope for the best. The right way: WRAP. Write effective issues. Refine your instructions. Keep tasks Atomic. Pair strategically with the agent.

This matters because most teams have backlogs full of work that never gets prioritized. Dependency updates. Test coverage. Refactoring to new patterns. The stuff you know you should do but never have time for. WRAP is GitHub's answer to that problem: a systematic approach to delegating grunt work to an AI agent while you focus on the parts that actually need human judgment.

How It Works

Write Effective Issues

The first mistake most developers make: writing issues for themselves, not for the agent. You know the codebase. The agent doesn't.

GitHub's rule: write issues as if you're onboarding someone brand new. That means context. That means examples. That means specificity.

Bad issue: "Update the entire repository to use async/await."

Good issue: "Update the authentication middleware to use the newer async/await pattern, as shown in the example below. Add unit tests for verification of this work, ensuring edge cases are considered."

The difference? The good issue tells the agent where to work, what pattern to follow, and what success looks like. It includes a code example. It sets expectations for testing.

Another tip: craft descriptive titles. When you're juggling ten agent-generated PRs, "Update auth middleware to async/await" is a lot more useful than "Fix async issue."

Refine Your Instructions

Custom instructions are where you teach Copilot how your team actually works. GitHub breaks these into three levels:

Repository instructions apply to a single codebase. If you have Go style preferences or specific error-handling patterns, put them here. Every Copilot interaction in that repo will use these instructions. GitHub's meta-tip: use the coding agent itself to generate your first draft of repository instructions.

Organization instructions apply across all repos. Testing requirements. Security patterns. Anything that's universal to your org goes here.

Custom agents are for repetitive tasks that don't apply to every change. Think "Integration Agent" for connecting new products to a specific service. These are scoped to enterprise, org, or repo level and defined in natural language text files.

The pattern here is clear: invest time upfront in instructions, save time on every subsequent task. This is the same principle behind context engineering — the more context you give the agent, the better its output.

Atomic Tasks

Coding agents are good at small, well-defined tasks. They're bad at sprawling, ambiguous ones.

The trick: break large problems into independent subtasks.

Don't ask Copilot to "Rewrite 3 million lines of code from Java to Golang." That's a nightmare to review and almost certain to fail.

Instead, break it down:

• Migrate the authentication module to Golang, ensuring all existing unit tests pass
• Convert the data validation utilities package to Golang while maintaining the same API interface
• Rewrite the user management controllers to Golang, preserving existing REST endpoints and responses

Each task is testable. Each PR is reviewable. Each piece can fail independently without blocking the others.

This is the same principle that makes microservices work: small, isolated units are easier to reason about than monoliths.

Pair with the Agent

The last piece of WRAP is knowing what to delegate and what to keep.

Humans are good at:

• Understanding why. You know why the issue exists. You can judge if the fix actually solves the underlying problem.
• Navigating ambiguity. When requirements are fuzzy, humans fill in the gaps. Agents need explicit instructions.
• Cross-system thinking. You know how a change in one repo affects three others. The agent doesn't.

Agents are good at:

• Tireless execution. Assign ten tasks right now. The agent will work on all of them.
• Repetitive work. Updating naming conventions across 50 files? Humans get bored and make mistakes. Agents don't.
• Exploring possibilities. Considering two approaches to a problem? Assign both to the agent and see how they play out.

The pairing model is about playing to strengths. Let the agent grind through the tedious stuff. You handle strategy, review, and integration.

What This Changes For Developers

WRAP isn't revolutionary. It's practical.

The real shift is psychological: treating coding agents like junior developers instead of magic boxes. You wouldn't throw a vague task at a new hire and expect perfection. You'd give them context, examples, and clear success criteria. Same with agents.

For teams with chronic backlog debt, this is the unlock. That dependency update you've been putting off for six months? Write a clear issue, assign it to Copilot, review the PR. Done.

Test coverage gaps? Break them into atomic tasks. One module at a time. Let the agent write the boilerplate tests while you focus on the edge cases that actually matter.

Refactoring to new patterns? Same deal. Write an example of the pattern you want, point the agent at a module, let it execute.

The workflow changes from "I don't have time for this" to "I don't have time to do this myself, but I have time to review it." That's a meaningful difference.

Try It Yourself

Start small. Pick one backlog issue that's been sitting for months. Something tedious but well-defined.

Write the issue using WRAP principles:

• Context for someone new to the codebase
• Descriptive title with location and scope
• Example of what you want
• Clear success criteria

Assign it to GitHub Copilot coding agent. Review the PR. See what works and what doesn't.

Then iterate. Add repository instructions based on what the agent got wrong. Break larger tasks into smaller ones. Refine your issue-writing process.

The goal isn't perfection on the first try. It's building a system that gets better over time.

The Bottom Line

Use WRAP if you have a backlog full of work that's important but never urgent. Dependency updates, test coverage, refactoring, documentation. The stuff that makes your codebase better but doesn't ship features.

Skip it if you're working on greenfield projects with lots of ambiguity. Agents need clear requirements. If you're still figuring out what to build, WRAP won't help.

The real opportunity here isn't speed. It's leverage. One developer with WRAP can clear backlog debt that would normally take a team weeks. The risk is treating the agent like a senior engineer instead of a tireless junior. Set expectations accordingly, and you'll actually get value out of this.

Source: GitHub Blog