Agent Bricks: Supervisor Agent long-running tasks

This page explains how to handle long-running tasks using task continuation with Agent Bricks: Supervisor Agent.

When a supervisor agent works on complex tasks that require many tool calls, the task might need more time than a single API request allows. To handle this, the supervisor supports long-running task mode, which automatically breaks long tasks into multiple request/response cycles so the agent can continue working without timing out.

How it works

1. The supervisor starts processing your request, calling tools and reasoning as needed.
2. Before the request times out, the supervisor pauses execution and emits a task_continue_request event in the streaming response.
3. To resume the task, your application sends a follow-up request that includes both the task_continue_request and a corresponding task_continue_response.
4. The supervisor picks up where it left off and continues working. This cycle repeats as needed until the task is complete.

Use long-running tasks in Playground

In AI Playground, Playground automatically handles task continuation. When the agent needs more time, Playground sends the continuation response on your behalf. You see a single, uninterrupted conversation.

Handle task continuation in the API

To enable long-running tasks, include "long_task": true in the databricks_options of your request:

JSON

{
  "input": [{ "role": "user", "content": "Your message here" }],
  "databricks_options": {
    "long_task": true
  },
  "stream": true
}

When using the streaming API directly, your application must detect task_continue_request events and send a follow-up request to continue the task.

Detect the checkpoint event in the streaming response

The supervisor emits an event like the following when it needs to continue:

JSON

{
  "type": "response.output_item.done",
  "item": {
    "type": "task_continue_request",
    "id": "continue_<unique-id>",
    "step": 16
  },
  "id": "resp_<response-id>"
}

The step field indicates the total number of tool call steps completed so far.

Send a follow-up request to resume

Include the original task_continue_request and a matching task_continue_response in the messages of your next request:

JSON

{
  "messages": [
    "...previous messages...",
    {
      "type": "task_continue_request",
      "id": "continue_<unique-id>",
      "step": 16
    },
    {
      "type": "task_continue_response",
      "continue_request_id": "continue_<unique-id>"
    }
  ],
  "databricks_options": {
    "long_task": true
  },
  "stream": true
}

The continue_request_id must match the id from the task_continue_request.

Known limitations

• If a single tool call or model call runs longer than the API timeout, the request might still time out because the continuation checkpoint can only be triggered between steps.
• Full long-running agent support, including timeouts during individual tool calls or model calls, is not yet available.