Google Cloud 推出 Database Operations Agents，实现自主数据库管理

通向职场人工智能的前门

作为 Agentic Data Cloud 的一部分：https://cloud.google.com/blog/products/data-analytics/whats-new-in-the-agentic-data-cloud?e=48754805，在 Google Cloud Next '26 上发布时，我们宣布了两个由 AI 驱动的数据库代理，以简化数据库管理。这些包括用于第 0 天操作的数据库入门代理—设置、配置和初始部署—以及用于第 1 天和第 2 天操作的数据库可观察性代理，包括监控、排查故障和持续维护。

这些代理始终在线，借鉴了 Google 多年的经验，并在 Google 的各个平台上集成，例如 Chat、CLI、Google Cloud 控制台、托管上下文协议 (MCP) 服务器，以及第三方工具—包括您偏好的集成开发环境 (IDE)，因此您可以在需要的时间和地点获得帮助。

传统上，管理和创建数据库涉及手工架构规划、自定义脚本以及各类独立工具的结合。团队需要处理数据库配置、模式设计、索引配置和查询优化，以及性能监控—通常随着应用需求的变化，需要多次测试和优化循环。虽然这种方法可行，但它需要大量技术技能，并在整个数据库生命周期中持续关注。例如，开发人员往往担心进行更新可能会限制日后系统的可扩展性。同样，当应用程序变慢时，找出导致问题的具体查询或资源约束可能需要数小时的手动调查和排查。

智能 AI 驱动的代理可以通过自动化许多任务来简化数据库生命周期管理，例如推荐适合工作负载的数据库类型、检测异常、推荐适当的配置、优化查询，并提供可操作的洞察以提升运营效率。通过将这些功能直接嵌入到您需要的工作流程中，代理可以帮助组织更高效地构建、操作和优化数据库，同时降低运营开销。

让我们仔细看看这些新的数据库代理。

可观察性代理使网站可靠性工程师（SRE）、DevOps 专业人员、数据库管理员和开发人员能够使用简单的自然语言提示诊断复杂问题并进行修复。

随着您的运营规模扩大，识别诸如查询热点或锁争用等微妙问题会变得成本高昂。数据库可观察性代理利用 Google 的运营专业知识和 Gemini 的推理能力来解决这些挑战。通过自动连接包括 Database Insights、Cloud Monitoring、Cloud Logging 和 Cloud Trace 在内的多个来源的遥测数据，该代理可在几分钟内提供清晰的根本原因分析。

除了识别“原因”，该代理还建议解决发现问题的推荐操作，并可在您批准后执行验证操作。例如，如果检测到瓶颈，它可能会建议您“为 Cloud SQL 实例启用连接池”，在您执行更改之前提供理由和预期影响。一些功能包括：

车队级故障排除：可观察性代理已集成到数据库中心，因此您可以使用 Gemini Chat 提出复杂的车队范围问题，例如，“我车队中哪些数据库在过去 7 天消耗的 CPU 最多？”，从而获得整个车队的汇总分析。

产品内调查：该代理关联来自数据库遥测、Cloud Monitoring、Cloud Logging、Cloud Trace 以及多个其他数据源的复杂遥测信息，以定位延迟峰值或锁争用等问题。（与特定客户的预览版）

验证修复：该代理不仅仅识别问题，还提供明确的建议，并可在您批准后执行验证操作，例如为 Cloud SQL 实例添加索引。（与特定客户的预览版）

MCP 工具：可观察性代理利用系统指标、查询指标、车队清单和问题等工具获取洞察，这些工具也可通过 Database Insights MCP 服务器和 Database Center MCP 服务器作为 MCP 工具使用。

适合您工作流程的集成

您可以在现有数据库管理流程中直接访问这些数据库可观察性代理功能。该代理支持多种体验，包括：

Cloud Assist 聊天：用自然语言提问，例如，“我的顶级 Cloud SQL 实例的 CPU 利用率趋势如何？”即可获得带有图表的汇总分析。然后，在聊天窗口中，您可以针对发现的任何问题开始调查：https://docs.cloud.google.com/cloud-assist/investigations，并获得根本原因分析和解决方案。

支持多个托管数据库

您可以使用可观测性代理获取数据库查询答案，即时访问任何数据库指标，或利用 AI 驱动的诊断来解决复杂问题。该代理涵盖了各种 Google Cloud 数据库的一系列问题，包括：

Cloud SQL：https://cloud.google.com/sql：排查并优化您数据库实例的负载、查询性能或所有 Cloud SQL 数据库引擎的连接问题。对于 Cloud SQL for PostgreSQL，可利用代理排查常见数据库问题。

同样，该代理帮助您识别问题、找到根本原因，并对其他支持的数据库和问题类型采取纠正措施。

新的数据库入门代理是在数据库选择过程中您的主动伙伴。无需花费数小时阅读文档，您可以用自然语言向代理描述应用需求。代理能够理解 IOPS、延迟限制和复制延迟等技术指标，从而提供合理的建议。您可以在 Gemini 聊天界面中直接访问数据库入门代理的功能：https://docs.cloud.google.com/cloud-assist/overview。使用数据库入门代理，您将获得：

数据库可观测性与入门代理的功能可用于多种服务，包括 AlloyDB、Bigtable、Cloud SQL（PostgreSQL、MySQL、SQL Server）、Firestore、Memorystore 和 Spanner。目前，这些代理可以通过 Gemini Cloud Assist 使用。探索 AlloyDB 的 AI 辅助排障和 Gemini Chat：https://docs.cloud.google.com/alloydb/docs/monitor-troubleshoot-with-ai，Cloud SQL：https://docs.cloud.google.com/sql/docs/postgres/monitor-troubleshoot-with-ai，Spanner：https://docs.cloud.google.com/spanner/docs/monitor-troubleshoot-with-ai，并访问 Gemini Cloud Assist 页面了解更多信息：https://cloud.google.com/products/gemini/cloud-assist?hl=en&e=48754805

The front door to AI in the workplace

As part of the Agentic Data Cloud ：https://cloud.google.com/blog/products/data-analytics/whats-new-in-the-agentic-data-cloud?e=48754805 launch at Google Cloud Next ‘26, we announced two AI-powered database agents to simplify database management. These include the Database Onboarding Agent for Day 0 operations — setup, configuration, and initial deployment — as well as the Database Observability Agent for Day 1 and 2 operations, including monitoring, troubleshooting, and ongoing maintenance.

These agents are always on, informed by Google’s years of experience, and integrated across Google surfaces such as Chat, CLI, the Google Cloud console, Managed Context Protocol (MCP) servers, and third-party tools — including your preferred integrated development environment (IDE), so you get help where and when you need it.

Traditionally, managing and creating databases has involved a combination of manual architecture planning, custom scripts, and distinct tools. Teams handle database provisioning, schema design, index configuration, and query tuning, alongside performance monitoring—often cycling through repeated testing and optimization cycles as application demands change. Although this method is functional, it demands substantial technical skill and continuous attention throughout the entire database lifecycle. For example, developers often fear making an update that may limit their ability to scale the system later. Similarly, when an application slows down, finding the exact query or resource constraint causing the issue can take hours of manual investigation and troubleshooting.

Intelligent AI-powered agents can simplify database lifecycle management by automating many of these tasks such as recommending the right database type for the workload, detecting anomalies, recommending the right configurations, optimizing queries, and providing actionable insights to improve operational efficiency. By embedding these capabilities directly into workflows where you need them, agents help organizations build, operate, and optimize databases more efficiently while reducing operational overhead.

Let’s take a closer look at these new database agents.

The Observability Agent empowers Site Reliability Engineers (SREs), DevOps pros, DBAs and developers to diagnose complex issues and remediate them using simple natural language prompts.

As your operations scale, identifying subtle issues like query hotspots or lock contention becomes an expensive burden. The database observability agent uses Google’s operational expertise and the reasoning capabilities of Gemini to solve these challenges. By automatically connecting telemetry across multiple sources including Database Insights, Cloud Monitoring, Cloud Logging, and Cloud Trace the agent provides a clear root cause analysis in minutes.

Beyond just identifying the "why," the agent suggests recommended actions to fix the issues found, and can execute validated actions with your approval. For example, if it detects a bottleneck, it might suggest you "Enable connection pooling for Cloud SQL instance," providing the rationale and expected impact before you commit to the change. Some capabilities include:

Fleet-level troubleshooting: The Observability Agent is integrated with Database Center so you can use Gemini Chat to ask complex fleet-wide questions like, "Which databases in my fleet consumed the most CPU in the last 7 days?" to receive a summarized analysis across your entire fleet.

In-product investigations: The agent correlates complex telemetry across Database telemetry, Cloud Monitoring, Cloud Logging, Cloud Trace, and multiple other data sources to pinpoint issues like latency spikes or lock contention. (In preview with select customers)

Validated remediations: Instead of just identifying problems, the agent provides crisp recommendations and can execute validated actions with your approval, such as adding indexes for a Cloud SQL instance. (In preview with select customers)

MCP tools: The Observability Agent derives insights with the help of tools such as system metrics, query metrics, fleet inventory, and issues, which are also available as MCP tools via the Database Insights MCP Server and Database Center MCP Server.

Integration that fits your workflow

You can access these Database Observability Agent capabilities directly within your existing database management processes. The agent powers several experiences, including:

Cloud Assist chat: Ask questions in natural language, for example, "What is the CPU utilization trend for my top Cloud SQL instances?" to get a summarized analysis complete with charts. Then, within the Chat window, you can start an investigation ：https://docs.cloud.google.com/cloud-assist/investigations for any issues found,and get a root-cause analysis and remediations.

Supports multiple managed databases

You can use Observability Agent to get answers to your database queries, to access any database metric instantaneously, or to leverage AI-powered diagnosis to resolve complex problems. The agent covers a broad set of issues across a variety of Google Cloud databases, including:

Cloud SQL ：https://cloud.google.com/sql : Troubleshoot and optimize your database instance load, query performance or connectivity issues for all Cloud SQL database engines. For Cloud SQL for PostgreSQL, leverage the agent to troubleshoot common database issues.

Similarly, the agent helps you identify issues, find their root cause, and take remediation actions for other supported databases and issue types.

The new Database Onboarding Agent is your active partner during the database selection process. Instead of spending hours reading documentation, you can describe your application requirements to the agent in natural language. The agent understands technical metrics like IOPS, latency limits, and replication lag, so it can provide a sound recommendation. You can access the Database Onboarding Agent’s capabilities directly within the Gemini chat ：https://docs.cloud.google.com/cloud-assist/overview interface. With the Database Onboarding Agent, you get:

The Database Observability and Onboarding Agent’s capabilities are available for a wide range of services, including AlloyDB, Bigtable, Cloud SQL (PostgreSQL, MySQL, SQL Server), Firestore, Memorystore, and Spanner. These agents are currently available via Gemini Cloud Assist. Explore AI assisted troubleshooting and Gemini Chat for AlloyDB ：https://docs.cloud.google.com/alloydb/docs/monitor-troubleshoot-with-ai , Cloud SQL ：https://docs.cloud.google.com/sql/docs/postgres/monitor-troubleshoot-with-ai , Spanner ：https://docs.cloud.google.com/spanner/docs/monitor-troubleshoot-with-ai , and Visit Gemini Cloud Assist ：https://cloud.google.com/products/gemini/cloud-assist?hl=en&e=48754805 page to learn more.