> For the complete documentation index, see [llms.txt](https://docs.aisera.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.aisera.com/ai-ops/problem-detection.md).

# Problem Detection

Problem detection is a key capability of the Aisera AI Ops platform. Based on AIsera LLM driven clustering and longitudinal behavioral analysis on temporal and ticket specific attributes, the system identifies application and infrastructure issue that need to be resolved and which might or may have already caused incidents and/or outages.

Problem detection works by:

1. **Real time data ingestion:** Aisera AIOps continuously monitors IT infrastructure and applications, collecting data, such as, alerts, logs, metrics, and real-time system performance indicators. This data goes into ticket clustering module which identifies the relevant clusters and anomalies in them. These anomalies are indicative of incidents and the source of the anomalies as determined by the Causal Graph point to the PRB that needs to be created.
2. **Leveraging ITOps specific AiseraLLM:** The platform employs IT Operations-specific Large Language Model to analyze the textual data within incident tickets, alerts and logs. Aisera LLM is adept at correlating between machine generated (alerts and logs) and human generated (user reported incidents) data and create corresponding clusters.
3. **Ticket Clustering:** The system then performs unsupervised clustering of the tickets (alerts, logs, incidents) based on output from the AiseraLLM analysis.
4. **Longitudinal analysis:** The platform employs long range time series analysis that incorporates seasonality to identify anomalies.


---

# 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 dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.aisera.com/ai-ops/problem-detection.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

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.