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Tables — referred to internally as datagrids — are structured data stores that AI agents can query and populate during conversations. Unlike a Knowledge Base, which stores free text for semantic search, a table stores data in columns and rows with defined types, like a lightweight database directly accessible by the agent.

Core Concepts

Why Tables Matter

  1. Real-time data without external integration: the agent accesses tables directly during a conversation. No webhooks, APIs, or integrations need to be configured for the agent to query a product catalog or register a lead.
  2. Separation of structured and unstructured knowledge: Knowledge Bases are ideal for free text — manuals, policies, FAQs. Tables are ideal for data with a defined structure — catalogs, schedules, inventories, records. Using each resource for what it was designed for improves agent accuracy.
  3. Writing during the conversation: unlike Knowledge Bases (read-only for the agent), tables allow the agent to insert and update data in real time — the agent collects information from the user and records it in a structured way in the table, without human intervention.
  4. Updates independent of the prompt: data changes in the table without needing to edit the agent prompt. A price updated in the table is immediately available for the agent on the next query.
Typical use cases where tables outperform other approaches:
  • Product catalog with price, availability, and description
  • Professional or event schedule with time slots and vacancies
  • Lead records collected during conversations
  • Inventory with real-time quantity tracking