Transport & Logistics — User Intent Classification for Agent Orchestration
NewThis DAG classifies user intents to optimize agent orchestration in transport logistics. By analyzing user interactions, it improves agent responses and enhances user satisfaction.
Overview
The primary purpose of this DAG is to classify user intents to facilitate the orchestration of agents in the transport and logistics sector. It ingests various data sources, including user interaction logs, query data, and historical agent performance metrics. The ingestion pipeline processes these inputs to extract relevant features that represent user intents. The processing steps include intent recognition, classification, and action suggestion based on predefined business rules. Quality cont
The primary purpose of this DAG is to classify user intents to facilitate the orchestration of agents in the transport and logistics sector. It ingests various data sources, including user interaction logs, query data, and historical agent performance metrics. The ingestion pipeline processes these inputs to extract relevant features that represent user intents. The processing steps include intent recognition, classification, and action suggestion based on predefined business rules. Quality controls are integrated to ensure the accuracy of classifications, including validation against historical data and user feedback. The outputs of this DAG include classified user intents, recommended actions for agents, and detailed logs of processing activities for error analysis. Monitoring KPIs such as classification accuracy, user satisfaction scores, and agent response times are established to evaluate the system's performance. By enhancing the responsiveness of agents to user queries, this DAG delivers significant business value, improving operational efficiency and customer satisfaction in the transport and logistics industry.
Part of the Data & Model Catalog solution for the Transport & Logistics industry.
Use cases
- Increased operational efficiency through optimized agent responses
- Improved user satisfaction and engagement levels
- Reduced response times for user queries
- Enhanced decision-making capabilities for agents
- Ability to adapt to changing user needs and preferences
Technical Specifications
Inputs
- • User interaction logs from chat interfaces
- • Query data from customer service requests
- • Historical performance metrics of agents
Outputs
- • Classified user intents for agent processing
- • Recommended actions for agent responses
- • Error logs for analysis and troubleshooting
Processing Steps
- 1. Ingest user interaction logs
- 2. Extract features from queries and interactions
- 3. Classify user intents using machine learning
- 4. Generate action recommendations based on classifications
- 5. Log processing activities and errors
- 6. Monitor performance against established KPIs
Additional Information
DAG ID
WK-1292
Last Updated
2025-04-27
Downloads
95