Agent confidence on the technical frontier

技术前沿的智能体信任度

Sponsored In partnership with Microsoft. Enterprise investment in AI is booming. Gartner is calling 2026 an “inflection year” for organizations to align their AI projects with strategic business objectives. As the pressure to prove ROI mounts, executives and technology leaders are looking to agentic AI to drive the measurable financial outcomes their businesses seek. 本内容由微软赞助。企业对人工智能的投资正处于蓬勃发展期。高德纳（Gartner）将 2026 年称为企业的“转折年”，届时企业需将人工智能项目与战略业务目标保持一致。随着证明投资回报率（ROI）的压力日益增大，高管和技术领导者正寄希望于智能体 AI（Agentic AI），以推动企业所追求的可衡量财务成果。

A prime opportunity for AI agents exists in the tech function, where IT infrastructure costs are projected to grow two to three times by 2030, even as budgets remain unchanged, according to McKinsey. And in the last 18 months, tech teams—the engineers, developers, architects, and other practitioners who are building, deploying, and continually improving their organizations’ infrastructure and applications—are clearly putting agents to work. 根据麦肯锡的数据，AI 智能体在技术职能领域拥有绝佳机遇。预计到 2030 年，在预算保持不变的情况下，IT 基础设施成本将增长两到三倍。在过去的 18 个月里，技术团队（包括构建、部署并持续改进组织基础设施和应用程序的工程师、开发人员、架构师及其他从业者）显然已经开始投入使用智能体。

The ultimate promise of agents is not only to automate tasks but to manage and coordinate entire workflows, pursuing business goals in a way that allows humans and agents to work together. Given the risks involved in automated decision-making, teams cannot delegate the work that agents do without confidence that they are fully capable of performing the task and that it will do so in a safe, reliable, and secure manner. 智能体的终极承诺不仅是自动化任务，更是管理和协调整个工作流程，以一种人机协作的方式追求业务目标。鉴于自动化决策中存在的风险，团队在确信智能体完全有能力执行任务，且能以安全、可靠和稳妥的方式完成工作之前，无法将工作委托给它们。

Among technology experts, our research shows that teams are exceedingly confident about using agentic AI across a significant amount of AI, data, and cloud tasks. Where agent readiness drops is largely due to a lack of business context being supplied to agentic systems. The more complex the task, the more reasoning capability an agent requires and the greater its need for business context. 我们的研究表明，技术专家团队对于在大量 AI、数据和云任务中使用智能体 AI 充满信心。智能体准备度下降的主要原因，在于未能向智能体系统提供足够的业务背景。任务越复杂，智能体所需的推理能力就越强，对业务背景的需求也就越大。

Such context-generation capabilities for agents are still at an early stage of development, especially in situations where enterprise data is difficult to wrangle and connect into the agent lifecycle at the speed and quality in which developers and executives need it. Human oversight is a key factor of success in deploying agentic AI. 目前，智能体的这种背景生成能力仍处于发展初期，特别是在企业数据难以整理，且无法以开发人员和高管所需的速度和质量接入智能体生命周期的情况下。人工监督是部署智能体 AI 成功的关键因素。

Knowing that tech teams are in a pivotal position to lead this transformation, the experts we interviewed expect agent confidence to accelerate as experience with agents deepens and business environments mature. “As we design agents to operate within the same operational boundaries, identity systems, and governance models that teams already use, they start to behave more like the systems organizations already trust,” says Jeremy Winter, corporate副总裁 and chief product officer at Microsoft Azure Platform. 鉴于技术团队在引领这一转型中处于关键地位，我们采访的专家预计，随着智能体使用经验的加深和商业环境的成熟，对智能体的信任度将会加速提升。微软 Azure 平台企业副总裁兼首席产品官 Jeremy Winter 表示：“当我们设计智能体，使其在团队已经使用的相同操作边界、身份系统和治理模型内运行时，它们开始表现得更像组织已经信任的系统。”

This report, based on a survey of 300 global technology experts, ranks 101 tasks across AI, data, and cloud workflows based on respondents’ confidence in agents acting on their behalf. It also examines how technology teams view the opportunities and challenges related to agentic AI, along with the potential for the technology to enhance their careers. 本报告基于对全球 300 位技术专家的调查，根据受访者对智能体代其执行任务的信任度，对 AI、数据和云工作流程中的 101 项任务进行了排名。报告还探讨了技术团队如何看待与智能体 AI 相关的机遇和挑战，以及该技术提升其职业生涯的潜力。

Key findings from the report include: 报告的主要发现包括：

Confidence in agents is surging for measurable tasks and growing in areas of complex judgment. Technology experts overwhelmingly believe agents help with everyday work including streamlining processes, improving performance, and reducing repetitive tasks. Confidence is highest for processes like generating reports and boilerplate code, and there is clear opportunity where tasks involve multistep workflows and advanced reasoning to make decisions. 对于可衡量的任务，对智能体的信任度正在激增，在涉及复杂判断的领域也在不断增长。技术专家们普遍认为，智能体有助于日常工作，包括简化流程、提高绩效和减少重复性任务。在生成报告和样板代码等流程中，信任度最高；而在涉及多步骤工作流程和高级推理决策的任务中，也存在明显的应用机会。

Data workflows are the breakthrough domain. Tech teams trust agents most where structure can provide a reliable foundation for decisions. This includes areas such as data quality monitoring, visualization anomaly detection, real-time data stream monitoring, and data profiling. This is where domain experts closest to the point of data generation can provide context to allow agents to act and deliver trusted outcomes. 数据工作流程是突破性领域。技术团队最信任那些结构化程度高、能为决策提供可靠基础的智能体应用。这包括数据质量监控、可视化异常检测、实时数据流监控和数据分析等领域。在这些领域，最接近数据生成点的领域专家可以提供背景信息，使智能体能够采取行动并交付值得信赖的结果。