当关键应用发生故障时，IT团队最不需要的就是争论责任归属。然而，在复杂的企业环境中，网络、应用和计算团队往往依赖各自独立的传统监控工具，而每种工具只能提供部分信息。在关键服务仍不可用的情况下，查明根本原因可能非常耗时。随着组织日益广泛地采用人工智能、混合基础设施和主权云环境，这一挑战正愈发严峻。
When a critical application fails, the last thing an IT team needs is a debate over who's responsible. Yet in complex enterprise environments, network, application, and compute teams often rely on separate legacy monitoring tools, each providing only part of the picture. Identifying the root cause can become a time-consuming exercise while essential services remain unavailable. As organizations expand their use of AI, hybrid infrastructure, and sovereign cloud environments, the challenge is becoming more pressing.
支撑关键服务的系统相互联系日益紧密，而对韧性、运营效率和有效治理的需求也在不断增长。人工智能智能体开始承担一部分运营负担，但智能体依据传统监控数据进行推理时，所看到的仍与人类一样，只是局部信息。那么，负责维持这些系统运行的企业领导者，是否也看到了同样的风险？Virtana的研究表明，企业董事会对其IT环境的认知，与一线运维人员的实际体验之间存在显著的可观测性缺口。
The systems supporting critical services are growing more interconnected, while the need for resilience, operational efficiency, and effective governance continues to increase. AI agents are starting to take on some of that operational load, but an agent reasoning from legacy monitoring data sees the same partial picture the humans do. But are enterprise leaders seeing the same risks as the teams responsible for keeping these systems running? Research by Virtana suggests a significant observability gap between what boardrooms believe they know about their IT environments and what frontline operators experience.
在这次访谈中，The的蒂姆·菲利普斯与Virtana首席执行官保罗·阿普尔比探讨了传统监控方法的局限，以及智能体可观测性如何帮助组织更清晰地了解其基础设施。阿普尔比分析了分散的传统监控为何会使IT团队难以区分事故原因与症状，并介绍了智能体可观测性如何关联跨应用、云、基础设施、网络和人工智能系统的实时遥测数据，从而为人工智能智能体提供上下文信息，加快根因分析和修复。此次讨论还探讨了可观测性对于数据主权和人工智能基础设施日益增长的重要性。
In this interview, The's Tim Phillips speaks with Paul Appleby, CEO of Virtana, about the limitations of legacy monitoring approaches and how agentic observability could help organizations gain a clearer picture of their infrastructure. Appleby explores why fragmented legacy monitoring can leave IT teams struggling to distinguish the cause of an incident from its symptoms, and how agentic observability, which correlates real-time telemetry across applications, cloud, infrastructure, networks, and AI systems, gives AI agents the context to accelerate root cause analysis and remediation. The discussion also examines the growing importance of observability for data sovereignty and AI infrastructure.
随着企业不断投资于人工智能（AI）相关技术以及日益复杂的工作负载，Appleby开始思考：企业该如何在整个技术体系中保持清晰的视野（即“可见性”），加强治理机制，并在这些问题影响到关键业务之前及时发现并解决潜在的运营风险。对于 IT 领导者来说，这次采访提供了一个新的视角——即应从过去单纯追究责任的做法中转变过来，转向更快速的问题解决方式、更具预防性的监控机制以及更加灵活、更具韧性的运营模式。
As enterprises invest in AI factories and increasingly complex workloads, Appleby considers how organizations can maintain visibility across the technology stack, strengthen governance, and address operational risks before they disrupt critical services. For IT leaders, the interview offers a perspective on moving beyond the traditional focus on proving which team wasn't responsible, toward faster resolution, preventative observability, and more resilient operations.
同时，这也引发了一个重要的问题：企业在治理和运营控制方面的投入，是否能够跟上其业务对基础设施和 AI 服务日益增长的依赖需求？请观看下面的完整采访，了解“主动式监控”（agentic observability）技术如何帮助企业填补“可见性缺口”（即提高对系统运行状态的感知能力），从而提升其 IT 系统的韧性（即系统的抗干扰能力和恢复能力）。本文由 Virtana 提供。
It also raises an important question for executive teams: Is investment in governance and operational controls keeping pace with the infrastructure and AI services their businesses increasingly depend on? Watch the full interview below to explore how agentic observability can help enterprises can close the observability gap and strengthen their approach to IT resilience. Contributed by Virtana