Load Automation Anywhere data to Microsoft Fabric

Automation Anywhere Control Room API is a RESTful interface for managing and administering the Automation 360 RPA platform. Everything needed to build a working Automation Anywhere → Microsoft Fabric pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Automation Anywhere to Microsoft Fabric pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Automation Anywhere API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Automation Anywhere API at a glance

These values come from the Automation Anywhere API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Automation Anywhere API?

All requests must include either an 'X-Authorization' header with the JWT token or an 'Authorization' header with a 'Bearer ' value. Note that 'Authorization: Bearer' is supported as of Automation 360 v.27 and later.

1. Get your credentials

1. Log in to the Automation Anywhere Control Room as an administrator. 2. Navigate to Administration > Roles and ensure the target user has a role with the 'Generate API-Key' permission enabled. 3. Log in as the user assigned this role. 4. Click the user profile icon (usually bottom-left) and select 'My settings'. 5. Click the 'Generate API-Key' button to create the key and copy it to your clipboard.

2. Add them to .dlt/secrets.toml

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[sources.automation_anywhere_source]
api_key = "REPLACE_ME"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Automation Anywhere data can I load into Microsoft Fabric?

These are the Automation Anywhere endpoints dlt can load into Microsoft Fabric:

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How do I load only new Automation Anywhere records?

The Automation Anywhere API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

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{"name": "repository_files", "endpoint": {
    "path": "v1/repository/filefolder/list",
    # Replace with a field that increases on every write.
    "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Automation Anywhere pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /v2/authentication and /v2/credentialvault/credentials from the Automation Anywhere API into Microsoft Fabric:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def automation_anywhere_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<your_control_room_url>",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "repository_files", "endpoint": {"path": "v1/repository/filefolder/list", "data_selector": "list"}},
            {"name": "users", "endpoint": {"path": "v1/usermanagement/users/list", "data_selector": "list"}}
        ],
    }
    yield from rest_api_resources(config)


def load_automation_anywhere_to_fabric() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="automation_anywhere_pipeline",
        destination="fabric",
        dataset_name="automation_anywhere_data",
    )
    load_info = pipeline.run(automation_anywhere_source())
    print(load_info)


if __name__ == "__main__":
    load_automation_anywhere_to_fabric()

Run it with python automation_anywhere_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Automation Anywhere data in Microsoft Fabric?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("automation_anywhere_pipeline").dataset()
df = data.repository_files.df()
print(df.head())

SQL:

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SELECT * FROM automation_anywhere_data.repository_files LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Automation Anywhere to Microsoft Fabric pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Automation Anywhere loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Automation Anywhere data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack