After more than a year of hands-on experience with GitHub Copilot coding agent, engineers at GitHub have identified a structured approach to extracting maximum value from the tool. The methodology, summarized by the acronym WRAP, provides guidance on composing tasks, customizing agent behavior, and understanding where human and machine capabilities complement each other best.

The WRAP framework consists of four components:

  • W – Write effective issues
  • R – Refine your instructions
  • A – Atomic tasks
  • P – Pair with the coding agent

This structured approach proves particularly valuable for teams facing accumulated backlogs. Many organizations have postponed infrastructure improvements to prioritize feature releases, or have split engineering effort between customer-facing bug fixes and longer-term initiatives. WRAP enables teams to address previously deferred work by leveraging the coding agent to handle tasks that lacked available capacity.

Write effective issues

Creating well-structured issues represents the foundation of successful coding agent deployment. The goal is to equip the agent with sufficient context, much as you would onboard a new team member unfamiliar with your codebase. Several principles guide this process:

  • Frame issues from the perspective of someone encountering the codebase for the first time. This mindset naturally prompts inclusion of the contextual details necessary for agent success.
  • Compose titles that specify both the nature of the work and its location within the system. Since the coding agent can accept numerous simultaneous assignments, clear naming prevents pull request review workflows from becoming unwieldy.
  • Incorporate concrete examples of desired implementation patterns. When you can demonstrate a specific approach—such as error handling conventions—including that example substantially increases the likelihood of receiving the intended output.

Consider these contrasting examples:

Update the entire repository to use async/await

versus

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.

async function exampleFunction() {
  let result = await promise;
  console.log(result); //"done!"
}

Refine your instructions

Customizing GitHub Copilot's instructions enhances the quality of agent-generated pull requests. Multiple instruction categories serve different organizational scopes:

Repository custom instructions

Repository-level instructions capture conventions applicable to a single codebase. A Go application with specific stylistic preferences, for instance, benefits from documenting those preferences in repository instructions. This approach compounds over time, progressively improving all Copilot interactions within that repository. Notably, the coding agent itself can generate initial repository instructions—an effective first use case for the tool.

Organization custom instructions

Just as repositories can have custom instructions, entire organizations can establish instruction sets applicable across all repositories. This proves ideal for organization-wide requirements, such as standardized testing approaches that apply universally.

Coding agent custom agents

Custom agents, defined through natural language text files, address repetitive development patterns that occur frequently but don't apply universally. An organization might create an "Integration Agent" specialized in connecting new products to a particular repository. These custom agents operate at enterprise, organization, or repository levels.

Atomic tasks

The coding agent excels with small, tightly scoped, and unambiguous assignments. While it can tackle larger problems, success requires decomposing them into independent, manageable pieces.

Assigning a single issue requesting "Rewrite 3 million lines of code from Java to Golang" would overwhelm the agent and create review nightmares. Instead, subdivide the migration into focused tasks:

  • 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.

This granular approach simplifies testing, validation, and pull request review of individual components.

Pair with coding agent

Effective collaboration requires recognizing distinct strengths: where humans excel and where the coding agent performs best.

Human strengths

  • Understanding intent: Humans grasp why a task exists and can evaluate whether proposed changes actually resolve the underlying problem.
  • Handling ambiguity: Humans navigate unclear specifications better than AI systems. You might omit details in an issue that would be essential for the agent to complete the work successfully, such as expected test coverage and telemetry requirements.
  • Systems thinking: Humans better comprehend how changes in one system affect others. When the coding agent completes a task in one repository, it typically lacks visibility into impacts on dependent systems.

Coding agent strengths

  • Sustained execution: Assign ten concurrent tasks to the coding agent and it will work through each without fatigue.
  • Repetitive work: While humans tire or make mistakes during monotonous tasks like updating naming conventions across multiple files, the coding agent handles such work reliably.
  • Rapid exploration: Testing multiple solution approaches becomes feasible by assigning each variant to the coding agent, providing quick feedback on different strategies without consuming substantial development resources.

Take this with you

Armed with GitHub Copilot and the WRAP framework, teams can systematically address accumulated backlog items. Whether updating dependencies, expanding test coverage, adopting new error handling patterns, or establishing repository instructions, WRAP provides the structure needed to clear deferred work efficiently.