AI 编码采用率已达约 90%，推动从单智能体的同步协作转向多智能体的异步统筹。文中强调人类从逐步指导转向批量委托，特别是在并行执行下可同时触发多个任务，吞吐量较单智能体模式呈数量级提升。未来可出现 AI 产出代码占 80–90%、人类执行约 10% 审核与关键判断的比例，并伴随每日可委托超过 10 个拉取请求的实践，使个体工程师在 5–10 年内能利用多智能体结构实现显著的生产率复利。

在工作流层面，指挥者模式依赖持续人工介入、临时会话与逐步验证，而统筹者模式依赖持久产物、异步执行与自主规划，包括自动创建分支、运行测试、生成提交与打开 PR。其核心差异体现为控制粒度缩放、实时循环转向延迟循环，以及从逐行补全转向端到端实现。多个智能体的并发执行要求隔离环境与任务拆分，以避免冲突并保持可追溯性。

趋势显示软件开发正向分工化的 AI “团队”迁移：规划、编码、测试、审查、文档更新与部署分别交由专用智能体执行，人类则维持质量控制、规范精确度、冲突裁决与最终合并责任。此模式的主要挑战集中在信任边界、冲突协调、状态共享、规范质量与责任承担，但总体轨迹指向持续委托与多智能体统筹将成为标准工程实践。

AI coding adoption has reached roughly 90%, driving a shift from single-agent synchronous collaboration to multi-agent asynchronous orchestration. The text emphasizes a move from stepwise human steering to high-level delegation, with parallel execution enabling multiple tasks to run concurrently and producing an order-of-magnitude throughput increase over single-agent workflows. Future projections include AI drafting 80–90% of code while humans provide about 10% oversight, with reports of delegating more than 10 pull requests per day, suggesting that in 5–10 years individual engineers may achieve substantial productivity compounding through multi-agent structures.

At the workflow level, the Conductor model relies on continuous human involvement, ephemeral sessions, and stepwise verification, while the Orchestrator model depends on persistent artifacts, asynchronous execution, and autonomous planning, including automatic branch creation, test execution, commit generation, and PR submission. Key contrasts include scaled control granularity, a shift from real-time to delayed loops, and a move from line-level completions to end-to-end implementations. Concurrent multi-agent execution requires isolated environments and task partitioning to avoid conflicts and maintain traceability.

The trend shows software development moving toward specialized AI “teams”: planning, coding, testing, reviewing, documentation, and deployment handled by dedicated agents, with humans maintaining quality control, specification precision, conflict resolution, and final merge responsibility. Core challenges center on trust boundaries, conflict coordination, state sharing, specification quality, and accountability, yet the trajectory points toward continuous delegation and multi-agent orchestration becoming standard engineering practice.