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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 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.
Custom agents in GitHub Copilot CLI extend the tool beyond ad-hoc prompting by enabling structured, workflow-level automation tailored to a team's stack.
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 relying on Copilot's individual plans for agentic coding workflows should review the new token-based limits and Pro+ tier requirements before their access to models like Claude Opus 4.7 is affected.
Developers looking to get started with GitHub Copilot CLI can use this tutorial as a concrete, low-stakes project template for understanding how to integrate AI assistance into everyday terminal-based coding workflows.