> For the complete documentation index, see [llms.txt](https://docs.lleverage.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.lleverage.ai/build-and-improve/monitoring/alerts-system.md). # Alerts Use alerts to surface operational conditions that need attention without continuously watching Monitoring. Alerts help bring important operational conditions to your attention without requiring someone to continuously watch Monitoring. They are most useful when you already know **what kind of event deserves attention**. ## Start from the condition, not the notification A useful alert begins with an operational question such as: * Did a production execution fail? * Has work been waiting longer than expected? * Is a particular process producing repeated errors? * Has a critical automation stopped running? Define that condition first. Then decide who should be notified and what they are expected to do. ## Use alerts for actionable exceptions An alert should normally cause a clear next action. Good examples include: * investigate a failed production execution; * review unusually slow work; * check repeated negative feedback; * inspect activity matching a known risk condition. Avoid alerting on information that nobody is expected to act on. Too many non-actionable alerts make real exceptions easier to miss. ## Connect alerts to Monitoring Alerts are based on activity conditions. Use [Monitoring activity](/build-and-improve/monitoring.md) to define and verify the view of work you care about before relying on it as an alert condition. When an alert fires, use the underlying activity and traces to understand what actually happened rather than treating the notification itself as the evidence. ## Review alert quality over time Operational conditions change. Periodically check whether an alert is still useful: * Does it identify a real exception? * Is the right person receiving it? * Does it fire too often? * Are repeated alerts exposing a structural issue that should be fixed instead? A recurring alert can be a signal that the underlying Agent, Workflow, data or process boundary needs improvement. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.lleverage.ai/build-and-improve/monitoring/alerts-system.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.