内容来源：https://www.technologyreview.com/2026/04/14/1134397/redefining-the-future-of-software-engineering/

内容总结：

智能体AI或成软件工程第三次革命，调查显示企业加速布局但挑战并存

本世纪以来，软件工程领域已历经两次重大变革：开源运动的兴起，以及DevOps与敏捷方法的普及。如今，随着智能体AI（Agentic AI）的应用，第三次变革正在酝酿。与当前主要辅助编码、测试的AI工具不同，智能体AI具备自主推理与决策能力，有望管理完整的软件项目乃至全生命周期，实现端到端的流程自动化。

一项针对300名工程技术高管的调查显示，尽管企业普遍看好其潜力，但全面应用仍面临挑战。关键发现如下：

• 应用势头强劲：目前半数企业将智能体AI列为软件工程投资重点，预计两年内这一比例将超过八成。当前已有51%的软件团队开始（有限度）使用，45%计划在未来12个月内引入。
• 早期收益渐进：多数团队预计未来两年内成效将较为有限（14%认为轻微改善，52%认为中等改善），仅9%认为将带来颠覆性变化。
• 显著提速预期：近所有受访者（98%）认为智能体AI将加快软件从试点到投产的进程，预计平均提速37%。
• 瞄准全生命周期管理：41%的企业团队目标在18个月内对大部分或全部产品实现AI对产品与软件开发全生命周期的端到端管理，预计两年内该比例将升至72%。
• 成本与集成是初期挑战：尤其对媒体、娱乐及科技硬件等先行行业而言，将智能体与现有系统整合以及算力成本是主要障碍。专家同时指出，工作流程变革带来的管理挑战更为关键。

报告指出，要像拥抱DevOps与敏捷那样充分释放智能体AI的价值，企业需同步推动艰难的组织与流程变革。然而，其在速度、效率与质量上带来的巨大潜力，使得这场变革值得期待。

本文由MIT Technology Review定制内容团队Insights制作，基于人工调研、分析与撰写。

中文翻译：

赞助内容
重塑软件工程的未来
智能体人工智能将如何改变软件开发与管理模式。
本文与SoftServe联合呈现

本世纪以来，软件工程领域已历经两次重大变革。首先是开源运动的兴起，逐步让全球开发者与工程师能够共享代码资源。其次，开发运维（DevOps）与敏捷方法的普及，推动软件从封闭式开发转向协作开发，从批量交付转向持续交付。如今，随着智能体人工智能在软件工程中的应用，第三次变革正初现雏形。

迄今为止，工程团队主要将人工智能用于编码、测试等特定任务，且通常限于严格设定的参数范围内。但借助智能体能力，AI智能体正演变为具备推理与自主决策能力的实体，不仅能处理独立任务，还能整体管理软件项目——并在很大程度上实现自主运作。若工程团队全面接纳这项技术，智能体人工智能将引领端到端的软件流程自动化，最终实现由智能体主导的开发和产品生命周期自动化。

本报告基于对300位工程技术高管的调研发现：软件工程团队已认识到智能体人工智能的潜力并开始尝试应用，但目前应用范围仍较为有限。尽管对其期望很高，但多数团队意识到，要全面推广这项技术仍需克服诸多障碍。正如DevOps和敏捷转型一样，要在工程领域充分发挥智能体人工智能的效益，往往需要伴随技术应用推动艰难的组织与流程变革。然而，其在速度、效率和质量方面可能带来的巨大提升，足以让所有付出变得值得。

核心发现如下：

应用势头正在积聚
目前半数企业将智能体人工智能视为软件工程领域的投资重点，而未来两年内这一比例将升至五分之四以上。资金投入正加速技术落地：当前已有51%的软件团队（多数为有限度）使用智能体人工智能，45%的团队计划在未来12个月内引入该技术。

早期收益将逐步显现
软件团队对智能体人工智能的投资需要时间才能收获成果。未来两年内，多数团队预计智能体带来的改进将较为有限（14%）或至多中等程度（52%）。但约三分之一（32%）的团队抱有更高期待，其中9%认为其将带来颠覆性改变。

智能体将加速产品上市
未来两年内，智能体人工智能的主要收益将体现在速度提升上。近全部受访者（98%）预期其团队从试点到投产的软件项目交付速度将加快，整体平均预期提速幅度达37%。

多数团队追求全生命周期智能体管理
团队对扩展智能体人工智能的愿景宏大。大多数团队目标是在较短时间内实现AI智能体端到端管理产品开发与软件开发生命周期（PDLC与SDLC）。41%的企业计划在18个月内对大部分或全部产品实现这一目标；若进展符合预期，两年后这一比例将升至72%。

算力成本与集成构成初期主要挑战
对所有受访者而言（尤其在媒体娱乐、技术硬件等先行行业），将智能体与现有应用集成以及计算资源成本是当前软件工程中应用智能体人工智能的主要挑战。受访专家同时强调，团队在调整工作流程时将面临更艰巨的变革管理难题。

本文由《麻省理工科技评论》定制内容部门Insights制作，并非编辑部撰写。内容由人类作者、编辑、分析师及插画师完成调研、设计与撰写，包括问卷设计及数据收集。若使用AI工具，仅限经过严格人工审核的辅助生产环节。

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英文来源：

Sponsored
Redefining the future of software engineering
How agentic AI will change the way software is developed and managed.
In partnership withSoftServe
Software engineering has experienced two seismic shifts this century. First was the rise of the open source movement, which gradually made code accessible to developers and engineers everywhere. Second, the adoption of development operations (DevOps) and agile methodologies took software from siloed to collaborative development and from batch to continuous delivery. Now, a third such shift looks to be taking shape with the adoption of agentic AI in software engineering.
Thus far, engineering teams have mainly used AI to assist with coding, testing, and other individual tasks, within tightly designed parameters. But with agentic capabilities, AI agents become reasoning, self-directing entities that can manage not just discrete tasks but entire software projects—and do so largely autonomously. If adopted and fully embraced by engineering teams, agentic AI will usher in end-to-end software process automation and, ultimately, agent-managed development and product lifecycle automation.
This report, which is based on a survey of 300 engineering and technology executives, finds that software engineering teams are seeing the potential in agentic AI and are beginning to put it to use, but so far in a mainly limited fashion. Their ambitions for it are high, but most realize it will take time and effort to reduce the barriers to its full diffusion in software operations. As with DevOps and agile, reaping the full benefits of agentic AI in engineering will require sometimes difficult organizational and process change to accompany technology adoption. But the gains to be won in speed, efficiency, and quality promise to make any such pain well worthwhile.
Key findings include the following:
Adoption momentum is building. While half of organizations deem agentic AI a top investment priority for software engineering today, it will be a leading investment for over four-fifths in two years. That spending is driving accelerated adoption. Agentic AI is in (mostly limited) use by 51% of software teams today, and 45% have plans to adopt it within the next 12 months.
Early gains will be incremental. It will take time for software teams’ investments in agentic AI to start bearing fruit. Over the next two years, most expect the improvements from agent use to be slight (14%) or at best moderate (52%). But around one-third (32%) have higher expectations, and 9% think the improvements will be game changing.
Agents will accelerate time-to-market. The chief gains from agentic AI use over that two-year time frame will come from greater speed. Nearly all respondents (98%) expect their teams’ delivery of software projects from pilot to production to accelerate, with the anticipated increase in speed averaging 37% across the group.
The goal for most is full agentic lifecycle management. Teams’ ambitions for scaling agentic AI are high. Most aim for AI agents to be managing the product development and software development lifecycles (PDLC and SDLC) end to end relatively quickly. At 41% of organizations, teams aim to achieve this for most or all products in 18 months. That figure will rise to 72% two years from now, if expectations are met.
Compute costs and integration pose key early challenges. For all survey respondents—but especially in early-adopter verticals such as media and entertainment and technology hardware—integrating agents with existing applications and the cost of computing resources are the main challenges they face with agentic AI in software engineering. The experts we interviewed, meanwhile, emphasize the bigger change management difficulties teams will face in changing workflows.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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