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Timely.ai allows Agents to interact with Tables (also known internally as Datagrids) through specialized Tools that provide permission-based access to perform specific operations — such as inserting, updating, or retrieving data. These Tools act as capability enablers: each one grants a distinct level of interaction between the Agent and the Table.
Without an appropriate Datagrid Tool assigned, an Agent cannot read or write data to that Table.

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🧠 How Integration Works

Each Table can be linked to one or multiple Agents.
However, instead of a direct connection, integration is managed through Datagrid Tools — each representing a precise permission scope. When a Datagrid Tool is attached to an Agent, it defines what kind of actions that Agent can perform on the target Table.
⚙️ Note:
Workflows currently cannot interact directly with Tables.
Only Agents can perform these operations, and if needed, they can pass the retrieved data to a Workflow through a Request Node.

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🧩 Available Datagrid Tools

Below is a list of the available Datagrid interaction Tools and their purposes:

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1. 🔵 Datagrid Row Insertion

Function:
Allows the Agent to insert a new row into a specific Table based on structured or contextual information gathered during a conversation. Typical use cases:
  • Logging user inquiries or form submissions.
  • Saving contact data, tickets, or feedback.
Example:
“Store this user’s question in the Unanswered Questions table.”

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2. 🔵 Datagrid Row Update

Function:
Allows the Agent to modify existing rows in a Table by matching a unique identifier or condition. Typical use cases:
  • Updating the status of a contact or request.
  • Changing the sentiment field in a feedback record.
Example:
“Update the status field of chat 5edca67d-dd2d... to resolved.”

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3. 🔵 Datagrid Row Semantic Search

Function:
Grants the Agent permission to search Table rows using semantic understanding — meaning queries are based on contextual similarity, not only keywords. Typical use cases:
  • Retrieving questions similar to a current user inquiry.
  • Searching for feedback containing specific intent or emotion.
Example:
“Find all feedbacks that mention onboarding issues.”

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4. 🔵 Datagrid Row Similarity Search

Function:
Allows the Agent to perform similarity-based vector searches among Table rows, often used for advanced matching or clustering operations. Typical use cases:
  • Identifying duplicate records or repeated questions.
  • Finding content with semantic similarity to a new input.
Example:
“Search for entries similar to this user’s message.”

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🔐 Permission-Based Architecture

Each Datagrid Tool represents a permission level within the Agent’s operational scope:
⚙️ Assigning a tool effectively grants that Agent the associated Table capability.
Without it, the Agent cannot perform those actions, even if it references the Table in conversation.

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🔗 Connecting a Table to an Agent

To link a Table to an Agent, follow these steps:
  1. Open the Agent Builder.
  2. Go to the Tools tab.
  3. Click Add Tool + and select one of the Datagrid Row Tools.
  4. Configure:
    • The target Table you want the Agent to access.
    • Any specific parameters or permissions.
Once added, the Agent can interact with the Table within the defined tool boundaries.

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🔁 Example — Using Multiple Tools

An Agent may require more than one Datagrid Tool to fully manage a Table.
For example, a Support Agent may need:
  • Datagrid Row Insertion → to record new issues.
  • Datagrid Row Update → to mark them as resolved.
  • Datagrid Row Semantic Search → to check if a similar issue already exists.
Each of these tools would be individually added to the Agent to grant the corresponding capabilities.

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🧩 Data Flow Example (Agent + Workflow)

While Workflows cannot access Tables directly, Agents can serve as data intermediaries:
  1. The Agent performs a Semantic Search in a Table.
  2. It retrieves the result in context.
  3. The Agent then sends this structured data forward through a Request Node (e.g., HTTP or Workflow call).
  4. The Workflow processes the received data for automation or analytics purposes.
This architecture maintains data integrity, ensures AI-controlled access, and prevents unauthorized manipulation of Table data.

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⚙️ Best Practices

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Final Note

In Timely.ai, Tables are not just passive data containers — they’re part of the Agent’s extended cognitive framework.
By combining Tables with the right Datagrid Tools, your Agents gain the ability to reason contextually, act autonomously, and persist knowledge — safely, intelligently, and under precise permission control.