Jiayi Geng

AI agents have become increasingly capable at isolated software engineering (SWE) tasks such as resolving issues on Github. Yet long-horizon tasks involving multiple interdependent subtasks still pose challenges, both with respect to accuracy and to timely completion. A natural approach is asynchronous multi-agent collaboration, where multiple agents work on different parts of the task at the same time, but this has proven surprisingly difficult: concurrent edits interfere with each other, dependencies are difficult to synchronize, and combining partial progress into a coherent whole is challenging. Human developers, on the other hand, have long relied on mature collaboration infrastructure to manage these challenges in large software projects. Inspired by these collaboration primitives, we introduce Centralized Asynchronous Isolated Delegation (CAID), a structured multi-agent coordination paradigm grounded in three core SWE primitives: centralized task delegation, asynchronous execution, and isolated workspaces. A central manager constructs dependency-aware task plans, engineers execute subtasks concurrently in isolated workspaces, and progress is consolidated via structured integration with executable test-based verification. CAIDimproves accuracy over single-agent baselines by 25.6% absolute on paper reproduction tasks (PaperBench) and 14.7% on Python library development tasks (Commit0). Our analysis further shows that branch-and-merge is a central coordination mechanism for multi-agent collaboration, and that SWE primitives such as git worktree, git commit, and git merge make it reliable and executable.