intent-routing

Intent classification routes natural language input by applying keyword matching and semantic inference to categorize user messages, then directing them to the appropriate domain agent, steward function, or built-in command based on the detected intent.

To handle ambiguous user inputs during intent routing, the system applies robust fallback mechanisms that prompt the user for clarification and logs the outcome, ensuring safe handling and continuous improvement of classification accuracy.

An intent routing skill classifies user input into categories such as task-delegation, queries, system-commands, and ongoing conversations, routing each to the matching agent or command to reduce manual intervention.

Yes, intent classification distinguishes between system commands and general queries, routing a request like 'start a maintenance task' to a domain agent while directing general questions to a Researcher agent.

The best way to route user intents across Steward workflows is using NLP classification with explicit routing targets and fallback handling, which automatically delegates tasks, processes queries, and manages system commands based on detected intent.

Yes, explicit routing targets are required to reliably classify intents and delegate tasks, as the system needs defined agents, steward functions, or built-in commands to map the categorized user input to the correct handler.