NEW research from CMU. (bookmark this one) The biggest unlock in coding agents is understanding strategies for how to run them asynchronously. Simply giving a single agent more iterations helps, but does not scale well. And multi-agent research shows that coordination > compute. A new paper from CMU proves this with a practical multi-agent system. CAID (Centralized Asynchronous Isolated Delegation) borrows proven human SWE practices: a manager builds a dependency graph, delegates tasks to engineer agents who work in isolated git worktrees, execute concurrently, self-verify with tests, and integrate via git merge. CAID improves accuracy over single-agent baselines by 26.7% absolute on paper reproduction tasks (PaperBench) and 14.3% on the Python library development tasks (Commit0). The key insight is that isolation plus explicit integration beats both single-agent scaling and naive multi-agent approaches. For long-horizon software engineering tasks, multi-agent coordination using git-native primitives should be the default strategy, not a fallback. Paper: arxiv.org/abs/2603.21489 Learn to build effective AI agents in our academy: academy.dair.ai/

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