Insights
Discover signals from automated analysis of log data
An Insight is a detected behavioral signal generated from unsupervised analysis of enriched logs as part of the Data Pipeline.
Insights represent signals that the user can triage and refine through the Explorer or inspection of Logs and track as Metrics or Segments. They represent clusters of log data defined by filters on Columns corresponding to unique patterns of behavior. Insights can point to errors, issues, or changes within your agentic application that can be used to inform where and how to improve or fix your agent as part of an Analytics-Driven Data Flywheel. Insights can reveal new metrics to eval or incorporate into reward functions, they can also pinpoint specific segments of data that can be used for focued post-training optimization, whether that be fine tuning, reinforcement learning, hyperparameter optimization, prompt optimization, or any other method.
What to Expect
Typical insight volume: Most projects generate 5-20 new insights per week. Projects with stable, consistent behavior may generate fewer insights, while projects with volatile or rapidly changing behavior may generate more.
Insight Structure
Summary: Human readable explaination of Insight with impact and severity
Examples: Specific evidence of the discovered pattern from the logs
Potential Fixes: How you could remediate the issue, ranked by effort. Used to complete the Analytics-Driven Data Flywheel by helping you fix or improve your agent.
Suggested Segment: A filter on the logs that approximates the behavior observed by the Insight. Used for tracking the issue and that it is corrected by the chosen fix.
If you see no insights:
DBNL requires at least 7 days of data to establish behavioral baselines
Check the Status page to ensure pipeline runs are completing successfully
Verify sufficient log volume (insights are more meaningful with hundreds of logs per day)
Very stable systems with little variation may naturally generate fewer insights
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