Triggered Automation Framework with AI Insight

Triggered Automation Framework (TAF) introduces an improved and simplified workflow by leveraging AI Insight. In previous versions, TAF required ticket analysis and an incident type definition to trigger the automation. TAF with AI Insight leverages the reasoning power of LLM (Large Language Model) to query the automation results. Hence, customers no longer need to analyze the tickets from 3rd party IT system and define incident types based on the analysis, simplifying the TAF workflow significantly.

When an API call (Alert from Splunk, Incident from ServiceNow, and so on) comes in from the 3rd party system, NetBrain TAF will process it.

• The existing flow: TAF will first try to match the incident type. TAF will trigger diagnosis and generate incidents if any incident type is matched.
• The new flow – TAF with AI Insight: AI will try to match the automation enabled in Insight Manager with its reasoning ability. No incident type is needed to define.
  • When automations are matched, the LLM will summarize the automation results, extract devices from the summary, and create a map with these devices. If the Enabled Live Execution for Triggered Automation option is checked in Insight Manager, TAF with AI Insight will execute automations using live data, then analyze and summarize its results.
  • When no automation is matched, the LLM will still provide a summary and generate an incident.

When new network issues emerge, TAF with AI Insight can adapt and resolve them once the corresponding automation is enabled in the Insight Manager.