> ## Documentation Index
> Fetch the complete documentation index at: https://docs.parallel.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Follow-up Tasks

> Trigger Task API enrichment or deep research from a monitor event

<div className="sr-only" aria-hidden="false">
  For AI agents: a documentation index is available at [https://docs.parallel.ai/llms.txt](https://docs.parallel.ai/llms.txt). The full text of all docs is at [https://docs.parallel.ai/llms-full.txt](https://docs.parallel.ai/llms-full.txt). You may also fetch any page as Markdown by appending `.md` to its URL or sending `Accept: text/markdown`.
</div>

Monitor events can feed directly into a Task API call to go deeper on any detected signal. Use follow-up tasks to enrich a signal with structured fields or launch a full research report—without any manual handoff. The flow is the same in both cases: receive a webhook, fetch the event, pass its `output.content` as the `input` and its `event_id` as `previous_interaction_id` to a new Task Run.

Passing `event_id` as `previous_interaction_id` carries the monitor event's full context forward into the Task run, preserving provenance through every step of the chain. See [Interactions](/task-api/guides/interactions) for details.

## Scenario 1: Structured Enrichment on a Detected Event

Use this when you want to extract machine-readable fields from a detected event—for example, pulling `company`, `amount`, and `round` from a funding announcement.

<CodeGroup>
  ```bash cURL theme={"system"}
  # 1. Fetch the event
  EVENT=$(curl --silent \
    --url "https://api.parallel.ai/v1/monitors/${MONITOR_ID}/events?event_group_id=${EVENT_GROUP_ID}" \
    --header "x-api-key: $PARALLEL_API_KEY")

  OUTPUT=$(echo $EVENT | jq -r '.events[0].output.content')
  EVENT_ID=$(echo $EVENT | jq -r '.events[0].event_id')

  # 2. Enrich with structured extraction
  curl --request POST \
    --url https://api.parallel.ai/v1/tasks/runs \
    --header 'Content-Type: application/json' \
    --header "x-api-key: $PARALLEL_API_KEY" \
    --data @- <<EOF
  {
    "input": "$OUTPUT",
    "processor": "base",
    "previous_interaction_id": "$EVENT_ID",
    "output_schema": {
      "type": "json",
      "json_schema": {
        "type": "object",
        "properties": {
          "company": { "type": "string", "description": "Company that raised funding" },
          "amount_usd_millions": { "type": "number", "description": "Amount raised in USD millions" },
          "round": { "type": "string", "description": "Funding round, e.g. Series A" },
          "lead_investor": { "type": "string", "description": "Lead investor name" }
        }
      }
    }
  }
  EOF
  ```

  ```python Python theme={"system"}
  import os
  from parallel import Parallel

  client = Parallel(api_key=os.environ["PARALLEL_API_KEY"])

  # 1. Fetch the event
  result = client.monitor.events(
      monitor_id,
      event_group_id=event_group_id,
  )
  event = result.events[0]
  output_content = event.output.content
  event_id = event.event_id

  # 2. Enrich with structured extraction
  task_run = client.task_run.create(
      input=output_content,
      processor="base",
      previous_interaction_id=event_id,
      task_spec={
          "output_schema": {
              "type": "json",
              "json_schema": {
                  "type": "object",
                  "properties": {
                      "company": {"type": "string", "description": "Company that raised funding"},
                      "amount_usd_millions": {"type": "number", "description": "Amount raised in USD millions"},
                      "round": {"type": "string", "description": "Funding round, e.g. Series A"},
                      "lead_investor": {"type": "string", "description": "Lead investor name"},
                  },
              },
          }
      },
  )
  run_result = client.task_run.result(task_run.run_id, api_timeout=3600)
  print(run_result.output.content)
  ```

  ```typescript TypeScript theme={"system"}
  import Parallel from "parallel-web";

  const client = new Parallel({ apiKey: process.env.PARALLEL_API_KEY });

  // 1. Fetch the event
  const result = await client.monitor.events(monitorId, {
    event_group_id: eventGroupId,
  });
  const event = result.events[0];
  const outputContent = event.output?.content;
  const eventId = event.event_id;

  // 2. Enrich with structured extraction
  const taskRun = await client.taskRun.create({
    input: outputContent,
    processor: "base",
    previous_interaction_id: eventId,
    task_spec: {
      output_schema: {
        type: "json",
        json_schema: {
          type: "object",
          properties: {
            company: { type: "string", description: "Company that raised funding" },
            amount_usd_millions: { type: "number", description: "Amount raised in USD millions" },
            round: { type: "string", description: "Funding round, e.g. Series A" },
            lead_investor: { type: "string", description: "Lead investor name" },
          },
        },
      },
    },
  });

  // wait for task to complete and log result
  const runResult = await client.taskRun.result(taskRun.run_id, { timeout: 3600 });
  console.log(runResult.output.content);
  ```
</CodeGroup>

See [Task Enrichment](/task-api/examples/task-enrichment) for full details on structured extraction, output schemas, and polling.

## Scenario 2: Deep Research on a Detected Event

Use this when you want a comprehensive report on a detected signal—for example, analyzing the strategic implications of a regulatory ruling or a competitor announcement.

<CodeGroup>
  ```bash cURL theme={"system"}
  # 1. Fetch the event
  EVENT=$(curl --silent \
    --url "https://api.parallel.ai/v1/monitors/${MONITOR_ID}/events?event_group_id=${EVENT_GROUP_ID}" \
    --header "x-api-key: $PARALLEL_API_KEY")

  OUTPUT=$(echo $EVENT | jq -r '.events[0].output.content')
  EVENT_ID=$(echo $EVENT | jq -r '.events[0].event_id')

  # 2. Launch deep research
  curl --request POST \
    --url https://api.parallel.ai/v1/tasks/runs \
    --header 'Content-Type: application/json' \
    --header "x-api-key: $PARALLEL_API_KEY" \
    --data @- <<EOF
  {
    "input": "Research the following event in depth and summarize its strategic implications: $OUTPUT",
    "processor": "ultra",
    "previous_interaction_id": "$EVENT_ID"
  }
  EOF
  ```

  ```python Python theme={"system"}
  import os
  from parallel import Parallel

  client = Parallel(api_key=os.environ["PARALLEL_API_KEY"])

  # 1. Fetch the event
  result = client.monitor.events(
      monitor_id,
      event_group_id=event_group_id,
  )
  event = result.events[0]
  output_content = event.output.content
  event_id = event.event_id

  # 2. Launch deep research
  task_run = client.task_run.create(
      input=f"Research the following event in depth and summarize its strategic implications: {output_content}",
      processor="ultra",
      previous_interaction_id=event_id,
  )
  print(f"Run ID: {task_run.run_id}")
  # Poll for result or use a webhook
  run_result = client.task_run.result(task_run.run_id, api_timeout=3600)
  print(run_result.output.content)
  ```

  ```typescript TypeScript theme={"system"}
  import Parallel from "parallel-web";

  const client = new Parallel({ apiKey: process.env.PARALLEL_API_KEY });

  // 1. Fetch the event
  const result = await client.monitor.events(monitorId, {
    event_group_id: eventGroupId,
  });
  const event = result.events[0];
  const outputContent = event.output?.content;
  const eventId = event.event_id;

  // 2. Launch deep research
  const taskRun = await client.taskRun.create({
    input: `Research the following event in depth and summarize its strategic implications: ${outputContent}`,
    processor: "ultra",
    previous_interaction_id: eventId,
  });

  console.log(`Run ID: ${taskRun.run_id}`);
  // wait for task to complete and log result
  const runResult = await client.taskRun.result(taskRun.run_id, { timeout: 3600 });
  console.log(runResult.output.content);
  ```
</CodeGroup>

Deep research runs are asynchronous. Poll `GET /v1/tasks/runs/{run_id}` or use a Task webhook to receive the completed report. See [Deep Research](/task-api/examples/task-deep-research) for full details.

## Related

* **[Interactions](/task-api/guides/interactions)**: How `previous_interaction_id` chains context across API calls.
* **[Task Enrichment](/task-api/examples/task-enrichment)**: Structured data extraction at scale.
* **[Deep Research](/task-api/examples/task-deep-research)**: Multi-step research reports from natural language prompts.
* **[Research Basis](/task-api/guides/access-research-basis)**: Citations and reasoning on Task API outputs.
* **[Events](./monitor-events)**: Event model and retrieval options.