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ASOS's experience illustrates how AI-accelerated code generation can shift the bottleneck downstream to pull request review, prompting teams to build custom agentic tooling to keep pace.
The agent merge feature removes the manual loop of copying code review feedback and CI failures back into prompts, letting the app resolve them autonomously on a monitored pull request.
The sandboxed execution environments directly address a concrete risk of agentic coding workflows — agents making unwanted or destructive changes to a developer's local machine — by isolating Copilot's tool execution both locally and in GitHub-hosted environments.
The shift to private pre-PR sessions and on-demand `@Copilot` commands in PRs gives developers more control over when and how the agent's work becomes visible to their team, reducing friction in agentic coding workflows.
Developers can use this tutorial as a practical starting point for building custom AI assistants with the GitHub Copilot SDK, leveraging fleet mode to automate code generation end-to-end.
Teams can encode coding standards, PR workflows, and accessibility checks directly into Copilot CLI agents — reducing manual review overhead and keeping AI output consistent across an entire codebase.