interactor-agents

Build conversational AI agents that call external tools by registering custom tools and configuring data connections within an LLM-powered assistant framework. This enables automated workflows where agents invoke tools for data retrieval, task execution, and live data queries during user conversations.

Connect AI agents to live databases and APIs using configurable data connectors. The agent queries linked data sources during conversations to retrieve real-time information, enabling automated responses grounded in current business data rather than static training knowledge.

Set up an LLM-powered agent with authentication and tool registration by configuring token management and registering custom tools before initiating the chat interface. This enforces secure access control while allowing the agent to orchestrate approved tool calls and data queries.

Building customer support agents that create tickets works by registering a ticket-creation tool and connecting a knowledge base data source to the conversational AI agent. The agent queries the knowledge base for answers and invokes the tool to create tickets when user requests require escalation.

Tool-calling orchestration for data-connected automation works by allowing the LLM agent to decide when to invoke registered tools and query connected data sources during a conversation. The agent manages context and triggers actions based on user intent, enabling end-to-end automated workflows across business systems.

Configuring token management is required before deploying AI agents with data connectors to enforce authentication across external data sources and tool calls. This ensures secure API integration and proper access control when the agent queries connected databases or executes registered tools.