Agent

The Flow Agent is an AI agent designed to operate inside task and workflow contexts, rather than direct user conversations. In this scenario the agent acts as a Task-based Agent, executed as part of an automated or analytical flow.

How to Access the Flow Agent

The Flow Agent is accessed through the Tasks panel in the left sidebar.

1. Open the left navigation menu

2. Go to Tasks

3. Select a task category (e.g. Analytics)

4. Click on the agent after selecting a table or access it by right-clicking on a table and searching for Agent.

A Flow Agent is used when you need AI to:

• Process data as part of a workflow

• Analyze, classify, or transform inputs

• Operate without direct end-user interaction

• Produce structured outputs for downstream steps

Unlike Chat Agents, Flow Agents are typically:

• Triggered by tasks

• Driven by prompts and inputs

• Integrated into pipelines

How to configure your Flow Agent

The General tab shown in this panel uses the same configuration model as a Chat Agent:

• Name

• Description

• Role

• Goal

• Backstory

• Tools

• RAG access

Since these concepts are already covered in the Chat Agent documentation, they are not redefined here.

Tools and RAG in Flow Agents

Flow Agents can access:

• Tools (Discovery, Diagrams, HTTP Request, MCP, RAG)

• Knowledge bases (RAGs)

• Schemas and diagrams

Access control follows the same rules:

• Only explicitly enabled tools can be used

• Only attached RAGs can be consulted

This ensures predictable and secure execution inside workflows.

Key Difference from Chat Agents

Aspect	Chat Agent	Flow Agent
Interaction	Conversational	Task / pipeline-based
Trigger	User message	Task Execution
Output	Natural language	Structured or operational
Usage	Front-facing	Backend/Analytical

Prompt Configuration

The Prompt tab defines how the Flow Agent receives instructions and input data during task execution.This is the core area where you specify what the agent should do with incoming data and how it should reason over it.

In a Flow Agent, the prompt is not written for direct user interaction, but for deterministic execution inside a workflow.

Purpose of the Prompt Tab

Use the Prompt tab to:

• Define the agent’s execution instructions

• Describe the expected input structure

• Control how data is injected into the prompt

• Ensure consistent and repeatable behavior

Prompt Tab Sections

The Prompt tab is composed of four main elements:

1. System Prompt

   1. Optional global instructions applied before any execution.

   2. Acts as a system-level context

   3. Useful for high-level constraints or global behavior

   4. Optional in most Flow Agent use cases

   5. Typical use cases:

      1. Enforcing strict output rules

      2. Applying compliance or formatting constraints

      3. Defining global execution policies

2. Loop Table

   1. Defines whether the agent should execute once or iterate over a table.

3. Prompt (Instructions)

   1. This is the main instruction block for the Flow Agent.

   2. Here you describe:

      1. The agent’s role for this specific task

      2. What it should analyze or transform

      3. How it should reason over the input

Output Configuration

The Output tab defines how and where the Flow Agent stores its execution results. This configuration turns the agent’s response into a structured artifact that can be consumed by downstream tasks, analytics, or storage layers.

Purpose of the Output Tab

Use the Output tab to:

• Persist agent results in a table

• Enforce a strict output schema

• Enable deterministic, machine-readable outputs

• Integrate AI results into pipelines and analytics

Output Destination

1. Save agent result at

   1. Choose where the agent output will be stored on a table or a parameter.

2. Table name

   1. Define the table where results will be written.

3. Select how the agent output will be stored as a text or JSON.

4. Insert mode: append records to table (optional)

5. Define output schema format

 {
  "type": "object",
  "properties": {
    "name": { "type": "string" },
    "email": { "type": "string" },
    "score": { "type": "integer" },
    "description": { "type": "string" },
    "date": { "type": "string" },
    "category_1": { "type": "string" },
    "category_1_score": { "type": "number" }
  }
}

This schema ensures that:

• Every output follows the same structure

• Data types are enforced

• AI responses are compatible with analytics and storage

Click Generate to auto-create a schema based on context.

When to Use a Flow Agent

Use a Flow Agent when:

• AI is part of a data or automation pipeline

• Outputs must be deterministic or structured

• The agent supports downstream tasks

• No direct chat interface is required

Best Practices for Flow Agent Outputs

• Always define an output schema for production workflows

• Prefer JSON for complex AI results

• Align output fields with downstream consumers

• Avoid free-text outputs in automated pipelines

• Version schemas when making structural changes