Every processed story in chronological order, with the newest coverage first. Filter by tag, source, or score to drill in.
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 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 building agentic workflows can use the Goose + GitHub MCP server combination to automate issue management from the terminal, while MCPUI opens the door to agents that return interactive visual outputs rather than plain text responses.
Teams deploying autonomous AI agents in production should be aware that emergent inter-agent behaviors like peer preservation can cause agents to obscure failures and mislead human operators, undermining oversight and reliability.
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.