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DocumentationUptiq Agents
Documentation
DocumentationUptiq Agents
  • Getting Started
    • Introduction
    • Sign up to Developer Edition
    • Build Your First Agent
    • Developer Support
  • Core Concepts
    • Agent
      • Knowledge
      • Webhook
    • PII Masking
    • Sub-Agent
    • Intent
    • Workflow
      • Node
        • Input
        • Output
        • Loader
        • Display
        • API Node
        • Web Crawler
        • Table Write
        • Table Read
        • Ruleset
        • Upload Document
        • Javascript
        • Workflow
        • Loop
        • Document To Image
        • External Database
        • Storage Write
        • Storage Read
        • Fetch Document
        • Prompt
        • RAG Query
        • Vector Search
        • Emit Event
    • RAG
    • Model Hub
      • Entity Recognizers
    • Data Gateway
    • Rulesets
    • Code Snippets
    • Tables
    • Storage
    • Widget
  • Overview of GenAI
    • Introduction
    • Key concepts
      • Intent Classification
      • Inference
      • Generative AI Models
      • Large Language Models (LLMs)
      • Prompt Engineering
      • AI Agents
      • RAG (Retrieval Augmented Generation)
      • AI Workflow Automation
      • AI Agents vs LLM-based APPs
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On this page
  • Overview
  • How to Create a Table?
  • How to Use Tables in AI Agent Development
  • Best Practices for Using Tables in Workflows
  • Key Takeaways for Developers
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  1. Core Concepts

Tables

Overview

Tables in Workbench provide an abstract database layer that allows developers to persist data within agentic workflows. Think of it as a custom data storage system where developers can define and manage structured records without worrying about underlying database complexities.

This feature is particularly useful when AI agents need to store, retrieve, and manipulate data across workflows—such as tracking user interactions, logging processed data, or maintaining reference records.

🔹 Storage Limitation: Persistent database space is limited by default but can be expanded separately if developers anticipate storing large volumes of data within AI agents.

How to Create a Table?

There are two ways to create a Table in UPTIQ:

1. From Config & Utils (Recommended for Reusability)

  1. Navigate to Config & Utils → Tables Tab.

  2. Click "Create Table."

  3. Enter a name for the table (this serves as a unique identifier for data storage).

  4. Click Save—the table is now ready for use in workflows.

2. Directly from a Workflow Node (Quick Method)

  1. Drag a Table Write/Read node into a workflow.

  2. Click on the node to open the side panel.

  3. Click "Add Table"—this automatically assigns the table to the current agent.

  4. Save the table definition.

How to Use Tables in AI Agent Development

Developers can leverage persistent tables to:

✅ Store Data Across Workflow Executions – Maintain records of AI-generated outputs, processed user inputs, or any other structured data.

✅ Retrieve & Reuse Data – Query stored information to enhance AI responses, track user history, or fetch reference data dynamically.

✅ Automate Business Logic – Use tables to store intermediate results that can be accessed by multiple workflow nodes, reducing redundant computations.

✅ Enable Long-Term Data Persistence – Unlike temporary workflow variables, tables retain data across workflow executions, allowing AI agents to operate with stateful memory.

Best Practices for Using Tables in Workflows

✔ Design Tables Thoughtfully – Only store data that is required for AI workflows. Avoid persisting unnecessary information to conserve storage.

✔ Regularly Clean Up Data – Since storage space is limited, implement periodic clean-ups for expired or unnecessary records.

✔ Ensure Data Security & Compliance – Be mindful of storing sensitive user data and implement access control mechanisms where required.

Key Takeaways for Developers

✅ Use Tables to Persist AI Data – Maintain structured records that workflows can access and update dynamically. ✅ Store & Query Information Efficiently – Design data models that support your AI agent’s functionality. ✅ Leverage Tables for Stateful AI Workflows – Enable AI agents to retain context between workflow executions. ✅ Manage Storage Effectively – Be mindful of storage limitations and optimize data persistence strategies.

By integrating Tables in workflows, developers can enhance AI capabilities, improve decision-making, and enable long-term data-driven automation.

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Last updated 1 month ago