What Breaks When You Sprint with 10 AI Agents (venturecrane.com)

In a recent experiment, a team successfully orchestrated ten AI coding agents across four machines to handle a series of GitHub issues. This setup, designed to test the limits of parallel processing in AI deployments, utilized Claude Code agents to work on isolated worktrees. The largest test involved executing 36 pull requests (PRs) across four waves, revealing both strengths and weaknesses in their approach. The orchestrator managed tasks statelessly, ensuring agents could work independently and focus on individual issues, which led to small and coherent PRs ready for human review. However, the exercise also highlighted significant challenges. The failure of Wave 4 PRs was traced back to stale branches on some machines, leading to merge conflicts that the agents could not detect. This underlined a critical insight: while agents can produce accurate code against their local environments, they need a current base to ensure integration correctness. The team implemented a synchronization step to fetch updates from the main branch before each wave, preventing wasted work. This experience emphasized the importance of maintaining machine health and synchronization in multi-agent workflows, ultimately pointing to the human review process as a key bottleneck in throughput that requires optimization to enhance overall efficiency.