Set up a complete customer organization with queues, schemas, validations, duplicate detection, email notifications, and UI configuration:

1. Create two new queues: Invoices and Credit Notes.
2. Update schemas w.r.t. schema specification (Invoices with 15 fields including line items table, Credit Notes as-is)
3. Add a computed field "The Net Terms" to Invoices queue (Due Date - Issue Date → Net 15/30/Outstanding)
4. Implement duplicate document detection on Document ID
5. Add business validations: total amount cap, line items sum check, quantity × unit price check
6. Add email notification extension on document status change to 'to_review'
7. Update Invoice queue UI settings to display 8 key fields
8. Verify setup by uploading a sample invoice twice (testing duplicate detection)

What This Demonstrates:

• Queue & Schema Setup: Creates queues with detailed field specifications including line items tables
• Computed Fields: Adds derived fields with business logic (date difference categorization)
• Duplicate Detection: Configures document-level deduplication with user-facing messages
• Business Validations: Implements multi-rule validation (amount caps, sum checks, arithmetic checks)
• Email Notifications: Sets up templated email alerts triggered by document state changes
• UI Configuration: Customizes queue column display for operational efficiency
• End-to-End Verification: Validates the entire setup with real document uploads

This example showcases the agent's ability to set up a production-ready organization from scratch - all from a single conversational prompt.

Automatically analyze and document all hooks/extensions configured on a queue:

Briefly explain the functionality of every hook based on description and/or code one by one for a queue `2042843`.

Store output in extension_explanation.md

What This Does:

• Lists all hooks/extensions on the specified queue
• Analyzes each hook's description and code
• Generates clear, concise explanations of functionality
• Documents trigger events and settings
• Saves comprehensive documentation to a markdown file

This example shows how the agent can analyze existing automation to help teams understand their configured workflows.

Set up a complete document splitting and sorting pipeline with training queues, splitter engine, automated hooks, and intelligent routing:

1. Create three new queues in workspace `1777693` - Air Waybills, Certificates of Origin, Invoices.
2. Set up the schema with a single enum field on each queue with a name Document type (`document_type`).
3. Upload documents from folders air_waybill, certificate_of_origin, invoice in `examples/data/splitting_and_sorting/knowledge` to corresponding queues.
4. Annotate all uploaded documents with a correct Document type, and confirm the annotation.
    - Beware document types are air_waybill, invoice and certificate_of_origin (lower-case, underscores).
    - IMPORTANT: After confirming all annotations, double check, that all are confirmed/exported, and fix those that are not.
5. Create three new queues in workspace `1777693` - Air Waybills Test, Certificates of Origin Test, Invoices Test.
6. Set up the schema with a single enum field on each queue with a name Document type (`document_type`).
7. Create a new engine in organization `1`, with type = 'splitter'.
8. Configure engine training queues to be - Air Waybills, Certificates of Origin, Invoices.
    - DO NOT copy knowledge.
    - Update Engine object.
9. Create a new schema that will be the same as the schema from the queue `3885208`.
10. Create a new queue (with splitting UI feature flag!) with the created engine and schema in the same workspace called: Inbox.
11. Create a python function-based the **`Splitting & Sorting`** hook on the new inbox queue with this settings:
    **Functionality**: Automatically splits multi-document uploads into separate annotations and routes them to appropriate queues.
    Split documents should be routed to the following queues: Air Waybills Test, Certificates of Origin Test, Invoices Test

    **Trigger Events**:
    - annotation_content.initialize (suggests split to user)
    - annotation_content.confirm (performs actual split)
    - annotation_content.export (performs actual split)

    **How it works**: Python code

    **Settings**:
    - sorting_queues: Maps document types to target queue IDs for routing
    - max_blank_page_words: Threshold for blank page detection (pages with fewer words are considered blank)
12. Upload 10 documents from `examples/data/splitting_and_sorting/testing` folder to inbox queues.

What This Demonstrates:

• Queue Orchestration: Creates 7 queues (3 training + 3 test + 1 inbox) with consistent schemas

• Knowledge Warmup: Uploads and annotates 90 training documents to teach the engine

• Splitter Engine: Configures an AI engine to detect document boundaries and types

• Hook Automation: Sets up a sophisticated webhook that automatically:

  • Splits multi-document PDFs into individual annotations
  • Removes blank pages intelligently
  • Routes split documents to correct queues by type
  • Suggests splits on initialization and executes on confirmation

• End-to-End Testing: Validates the entire pipeline with test documents

This example showcases the agent's ability to orchestrate complex workflows involving multiple queues, engines, schemas, automated hooks with custom logic, and intelligent document routing - all from a single conversational prompt.