Analyze logs using pattern discovery or direct queries. Start by opening a new chat.

Pattern discovery

Use pattern discovery to get a high‑level overview of the types of logs a source is generating. Ask AI SRE to "analyze log patterns for the API service from the last 6 hours."

This is useful for understanding what data is available before you write specific queries. AI SRE returns the most frequent log patterns it finds, including counts and sample messages for each pattern.

Filter pattern discovery by:

• Log level (e.g., error, warn)
• Service (e.g., redis, mongodb)
• Time range (relative or absolute)
• Number of patterns to return, with a default of 20 and a maximum of 100.

Pattern discovery limitations

Pattern discovery works only on logs sources, not metrics or traces.

Charting data

Ask for inline charts to quickly visualize data and even add them to your dashboards.

Direct queries

For more specific investigations, ask AI SRE to run a direct SQL query against your logs, metrics, or traces. You don't need to write the SQL; the agent generates it from your natural‑language prompt. For example: "@telemetry.example.com average response time over the last day broken down by status code".

Display the results in three modes:

• Sample (default): Shows 30 representative rows and a total count. Use this to get a quick look at the data.
• Summary: Provides the same sample data plus an AI‑generated summary of key insights, such as error rate changes or performance spikes. This is the best way to spot anomalies in large result sets.
• Raw: Returns the full query results, up to 1000 rows. Use this when you need AI SRE to perform its own analysis on the complete dataset.

Query results appear in a scrollable, sortable table directly in the chat.

Large result sets

For very large result sets, AI SRE may use the Summary mode internally to stay within its context window. If you need the full output, ask it to dump the rows to a CSV or table.