- Einleitung
- Erste Schritte
- Prozessmodellierung mit BPMN
- Grundlagen der Prozessmodellierung
- Öffnen der Modellierungsarbeitsfläche
- Modellierung Ihres Prozesses
- Ausrichten und Verbinden von BPMN-Elementen
- Autopilot for Maestro (Vorschau)
- Prozess-Repository
- Prozessmodellierung mit Fallverwaltung
- Entwerfen eines persistenten Schemas für eine Fallentität
- Definieren von Fallschlüsseln (system vs. extern)
- Festlegung von Aufgaben-E/A und Write-Back-Vereinbarungen
- Austrittsregeln und Frühphasenbeendigung
- Modellierung von primären und sekundären Phasen
- Auslösen eines Falls über Data Fabric
- Implementieren von Personen und Berechtigungen auf Phasenebene
- Festlegen von SLAs und Regeln für die automatisierte Eskalation
- Konfigurieren einer Nachbearbeitungsschleife (erneuter Eintritt)
- Verwalten von Live-Fallinstanzen: Anhalten, migrieren und wiederholen
- Wörterbuch für die Fallverwaltungskomponente von Maestro
- Process modeling with Flow
- Erste Schritte
- Kernkonzepte
- Node reference
- Built-in nodes
- Skript
- HTTP-Anfrage (HTTP Request)
- Data Transform
- Entscheidung
- Umschalten (Switch)
- Schleife
- Verzögerung (Delay)
- Zusammenführen
- Beenden
- Terminate
- Mock
- Manual Trigger
- Scheduled Trigger
- Mitarbeiter
- Business Process Model and Notation (BPMN) process
- RPA-Workflow
- API-Workflow
- Human Task
- Agent tools
- Analyze Files
- Batch-Transformation
- Zusammenfassen
- Connector nodes
- Build guides
- Best Practices
- Referenz (Reference)
- Prozessimplementierung
- Debugging
- Simulieren
- Veröffentlichen und Aktualisieren von agentischen Prozessen
- Häufige Implementierungsszenarien
- Extraktieren und Validieren von Dokumenten
- Prozessabläufe
- Prozessüberwachung
- Prozessoptimierung
- Referenzinformationen
Benutzerhandbuch zu Maestro
Was es tut
Batch Transform is a built-in tool that processes a CSV file row by row and adds AI-generated columns to it. You give it a source CSV, a task that describes what to do for each row, and the columns you want to add. It returns a new file with those columns populated.
You attach Batch Transform to an Autonomous Agent as a tool. The agent calls the tool while reasoning, passing it the source file and the task, and uses the transformed file in the rest of its work.
The node is labeled Batch Transform in the palette, in the built-in tools category.
When to use this
Use Batch Transform when an agent needs to enrich or classify the rows of a CSV in bulk, and each row needs the same AI-driven treatment.
Typical tasks:
- Classify each row into a category and write the label to a new column.
- Extract a value from a free-text field into its own column.
- Score, summarize, or tag each row based on its existing columns.
For pulling structured data out of a single document rather than transforming tabular rows, connect a Document extraction resource instead. To analyze the content of files that are not row-oriented CSVs, use the Analyze Files built-in tool.
Adding the tool
Add the tool to the canvas, then connect it to an agent.
- Select an Autonomous Agent node, or add one if your flow doesn't have it yet.
- Open the node palette and select the Built-in tools category.
- Select Batch Transform. Flow adds the node to the canvas.
- Draw an edge from the Batch Transform node to the agent's tool handle.
- Select the Batch Transform node to open its properties panel and configure it.
Result: The Batch Transform tool is connected to the agent. The agent can now call it while reasoning.
Configuration reference
Configure these fields in the Batch Transform Settings section of the properties panel.
| Feld | Erforderlich | Standard | Beschreibung |
|---|---|---|---|
| Source (file) | Ja | Keine | CSV file to transform. Reference a file produced upstream in the flow, or one the agent supplies when it calls the tool. |
| Batch Transform Task | Ja | Keine | Instructions the tool applies to each row. Describe what to analyze, what to extract, and how to populate the output columns. |
| Output columns | Ja | Keine | Columns the tool adds to the file. Define at least one column with a name and description, and add up to 10 columns. |
| Spaltenname | Ja | Keine | Header for the new column in the output file. |
| Beschreibung | Ja | Keine | Content the column should contain. The tool uses this value to decide what to write for each row, so be specific about the expected format. |
| Web search grounding | Nein | Aktiviert | Whether the tool can use web search to ground or enrich its results. Select Disabled to use only the source file and task. |
Eingaben und Ausgaben
Eingabe
Batch Transform takes its input from the fields you configure: the source CSV, the task, the output columns, and the web search grounding setting. Because it runs as a tool, the calling agent supplies these values when it invokes the tool during a run.
Ausgabe
Batch Transform returns a file: the source CSV with the output columns appended and populated. Access it at $vars.<nodeName>.output, where <nodeName> is the name of the Batch Transform node.
$vars.batchTransform1.output
$vars.batchTransform1.output
If the tool fails, the node populates an error object instead. Refer to Error handling for the error shape and how to route on it.
{
"output": "file. The source CSV with the configured output columns added and populated.",
"error": "object. Populated only on failure. See Error handling."
}
{
"output": "file. The source CSV with the configured output columns added and populated.",
"error": "object. Populated only on failure. See Error handling."
}
Beispiel
Triage a CSV of support tickets
An agent receives a CSV of support tickets and needs each ticket categorized and prioritized before it routes them.
Connect a Batch Transform tool to the agent and configure it as follows.
Source (file): the tickets CSV, for example a file passed in from the start of the flow.
Batch Transform Task:
For each ticket, read the Subject and Body columns. Classify the ticket into exactly one category: billing, technical, or account. Then assign a priority of high, medium, or low based on the urgency expressed in the text.
For each ticket, read the Subject and Body columns. Classify the ticket into exactly one category: billing, technical, or account. Then assign a priority of high, medium, or low based on the urgency expressed in the text.
Output columns:
Category: one of billing, technical, or account, based on the ticket's subject and body.Priority: one of high, medium, or low, reflecting the urgency of the ticket.
When the agent calls the tool, Batch Transform returns the original CSV with a Category and a Priority column added for every row. The agent then uses that enriched file, available at $vars.batchTransform1.output, in the rest of its task.