Chatsistant has 2 types of Agents: User-facing: This Agent directly interacts with users conversationally in a Q&A fashion. Only a single user-facing Agent engages with the user when a new query is input into the chatbot. Background: This Agent never interacts with users directly and instead monitors the conversation in an ongoing fashion. All background Agents are run whenever the user submits a new query to the chatbot. Customizable Agents in Chatsistant Chatsistant enables you to create powerful and adaptive workflows using two types of agents: 1. Front-End User-Facing Agents
  • These agents directly interact with users conversationally in a Q&A format.
  • Only one user-facing agent engages with the user at a time when a new query is submitted.
  • They are designed to provide answers, complete tasks, or guide users through various interactions.
2. Background Agents (Built-In Functionality) Background agents are now built-in to Chatsistant’s architecture, providing automated monitoring and task execution without requiring direct configuration. These agents work in the background to support front-facing agents by performing tasks such as:
  • Human Escalation: Automatically escalating conversations to a human agent based on user behavior or triggers.
  • Function Calling (Webhooks): Executing API calls to external systems when certain conversational conditions are met.
  • Conversational Tagging: Assigning dynamic tags to conversations to categorize user queries or detect specific intents (e.g., frustration detection).
Unlike front-facing agents, background agents are enabled and customized directly by users in the chatbot’s Customization Settings. They operate seamlessly behind the scenes to enhance chatbot functionality. Key Features of Built-In Background Agents
  • Trigger-Based Monitoring: Background agents automatically monitor user queries whenever a new message is submitted and execute actions such as tagging, escalation, or calling webhooks.
  • Workflow Optimization: By specializing in background monitoring, these agents create “multi-threaded” workflows, improving accuracy and consistency across chatbot interactions.
  • RAG Integration (Optional): In scenarios where background tasks need to evaluate user queries against policies, records, or other knowledge sources, RAG capabilities can be enabled.
How Built-In Background Agents Work
  • Simple, Focused Tasks: Background agents operate with predefined instructions. For instance, they monitor conversations for triggers like frustration, escalate to human support, or call a webhook when specific intents are detected.
  • No Manual Configuration Required: All agents created through Chatsistant are front-facing agents, while background capabilities are automatically enabled and integrated for seamless operation.
  • Optional Knowledge Source Access: Background agents can reference knowledge bases or RAG systems if needed for specific tasks, such as evaluating user statements before assigning tags or escalating queries.
Examples of Built-In Background Agent Use Cases
  • Human Escalation: Automatically escalate conversations to a human agent if a frustrated tone or critical issue is detected.
  • Function Calling: Execute specific actions, such as sending data to a CRM via webhook, based on user inputs like “Submit payment” or “Schedule appointment.”
  • Conversation Tagging: Dynamically tag conversations to streamline categorization. For example, add tags like “Frustrated” or “High-Priority” to enable tailored follow-ups.
By moving background agents to built-in functionality, Chatsistant ensures seamless integration into workflows, eliminating the need for manual setup while maintaining flexibility and scalability. A common use case for background Agents is conversation tagging. For an example of how to use background Agents to assign conversation tags, see Frustration Detection.

Here’s a fun setup that detects any mention of various fruits during the conversation and assigns the appropriate tags.