How to Use action_fastline_pro Action in Scenario?

The action_fastline_pro action allows using an AI agent or chatbot created in FastLinePro in a bot scenario. This allows integrating smart AI responses into the dialogue with the user.

When Needed

You need to use an AI agent to answer user questions.
You need to integrate a chatbot with a knowledge base into the scenario.
You need to automate responses to typical questions via AI.

What's Important to Know

action_fastline_pro — action for calling an AI agent from FastLinePro.
Agent name must exactly match the name specified in FastLinePro.
Conversation ID is responsible for saving dialogue context.
Vision file allows the agent to process images and files.

Prerequisites

You are logged in with integrator or administrator rights.
There is a created AI agent or chatbot in FastLinePro in the system.
You have access to the scenario constructor.

Step-by-Step Instructions

In the scenario constructor, add an "Action" block in the needed place in the scenario.
In the "Action" block settings, select the action_fastline_pro function.
In the configuration field (JSON), enter parameters:

{
  "agent_name": "Your company agent",
  "user_input": "{{question}}",
  "vision_file": true,
  "conversation_id": "fl_client_id",
  "save_response": "client_answer",
  "inputs": {
    "first_name": "Bohdan"
  }
}

Action Parameters

agent_name — name of the AI agent you created and connected in the interface. Must exactly match the name specified in Fast Line Pro.
user_input — user question or query. Usually this variable is filled in the previous step of the scenario, for example: {{question}}.
vision_file — boolean parameter that determines whether the agent has access to files, images, and other attachments:
- true — agent sees files
- false — agent works only with text
conversation_id — variable responsible for saving dialogue context. If you pass an empty value — the agent will treat the query as the start of a new dialogue. After the first response, it will generate a conversation_id itself, which should be saved and passed in subsequent queries.
save_response — name of the variable where the agent's response will be saved. For example: client_answer.
inputs — object containing all variables you want to pass to the agent (must be previously created in the agent's "Variables" block).

Save changes in the action and scenario.

Usage Example

Scenario:

"Wait for Response" block → user enters a question, saved in {{question}}
"Action" block with action_fastline_pro → agent responds, saved in {{client_answer}}
"Message" block → displays {{client_answer}}

What Happens After

After executing the action, the AI agent processes the user's query and generates a response based on its instructions and knowledge base (if connected). The response is saved to the variable specified in save_response and can be used in subsequent scenario blocks.

How to Verify It Worked

Check that the agent name in agent_name exactly matches the name in FastLinePro.
Make sure all parameters are specified correctly.
Test the scenario and check that the agent responds correctly.
Check that the response is saved to the variable from save_response.