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WindsurfCovers Guance core data domains and actions, supports personal and enterprise scenarios, and continuously iterates rapidly.
Query logs, metrics, links, events, RUM, and other data
Multidimensional analysis, association analysis, root cause localization, and trend insights
Manage monitoring and alarm rules, and check alarm status
View service topology, host resources, and dependencies
Manage dashboards, dashboards, data sources, and integration
Creating, querying, and sharing dashboards and dashboards
Actual actions during shifts, troubleshooting, and inspections are entrusted to Agent for execution, while engineers retain the right to review parameters, evidence, and conclusions.
Agent Query logs, trace, and RUM errors via OWL CLI, and summarize the impact area by service, interface, and version.
Agent Pull page access, RUM clicks, and business events, break down lost nodes by source, device, and path, and output actionable conversion optimization suggestions.
Scripts or Agent periodically call the OWL CLI to synchronize core metrics and accumulate daily reports.
No. The OWL CLI is first and foremost a command-line entry point that engineers can use directly, and it can also serve as a tool layer for AI Agent to invoke Guance capabilities. Its value lies in organizing queries, diagnostics, and context into a unified, executable, and reviewable tool catalog.
Available capabilities depend on tool catalogs, configurations, and API Key permissions. Typical scenarios include querying, diagnosing, and contextualizing observation objects such as logs, metrics, links, events, RUM, APM, infrastructure, monitors, dashboards, as well as monitoring and alerting, resources and topology, configuration and platform, dashboard and Kanban operations.
You can start by automatically installing documentation. Install and configure OWL CLI and OWL Skill in the current environment according to the documentation. Once connected, you can call Guance capabilities via command line or AI Agent workflows.
OWL CLI is more like a local command and tool execution layer, suitable for engineer terminals, scripts, and Agent workflows; Guance MCP Server acts more like a protocol bridge, suitable for connecting Guance capabilities to AI clients that support MCP. The two can be used together to jointly provide AI-assisted troubleshooting.
Starting from model requests, prompts, response time, errors, and costs, establish observable analytical entry points for large model applications.
Learn about Agent monitoring applications, LLM monitoring applications, session lists, trace lists, and risk event analysis.
Organize logs, metrics, links, events, RUM, and infrastructure queries into executable, auditable diagnostic processes.
Connect AI IDEs, desktop clients, and automated Agent via Model Context Protocol to make recommendations based on authorized observation data.
Ready to let AI Agent call real observational data for analysis and troubleshooting?
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