Anthropic's Claude Code has seen a 300% increase in usage over the past three months due to its potential to automate tasks typically sidelined by engineering teams. While initial tests showed promise in identifying bugs and staging fixes, challenges arose when it generated large, unreadable blocks of code, creating difficulties in review and implementation. To address these issues, the team experimented with stacking pull requests (PRs) to break down large code changes into smaller, manageable tasks. This approach was implemented using the GT MCP tool, which organizes tasks into a sequence of focused PRs, mimicking the workflow of senior engineers. This method has enhanced the review process, making AI-generated code more manageable and useful in real engineering work, with the team successfully building features, resolving bugs, and adding tests across their codebase. The GT MCP is now available for broader community use, promising efficiency gains by helping AI agents to stack PRs effectively.