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Overview

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Tool Name

delegate_work

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Purpose

The delegate_work tool facilitates the distribution of tasks to other bots or workers. It is designed to enable efficiency in workflows by offloading specific processes to targeted resources for execution, allowing parallel progression across tasks.

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Functions Available

_delegate_work: Delegates a defined task, allowing customization of retries, timeouts, and optional callbacks.

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Key Features & Functions

Delegate Tasks

Assigns tasks to specific bots, managing retry logic, timeouts, and optional callback triggers.

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Input Parameters for Each Function

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_delegate_work

Parameters

NameDefinitionFormat
promptClear instructions describing the task to delegate (required).String (required)
target_bot(Optional) Name or ID of the bot to handle the task. If blank, retrieves a list of available bots.String
max_retries(Optional) Number of retries if errors occur (default: 3, range: 1–10).Integer
timeout_seconds(Optional) Time in seconds to allow task completion before retry (default: 300). Minimum: 1.Integer
callback_id(Optional) Unique identifier for triggering follow-up actions upon task completion.String

Genbot Tip When delegating critical tasks, set a higher max_retries and suitable timeout_seconds to handle transient failures (e.g., network hiccups).

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Use Cases

  1. Parallel Task Execution

    • Delegate independent tasks like database queries or report generation to multiple bots for parallel progress.

    • Example: One bot extracts warehouse usage stats while another prepares daily usage reports.

  2. Retry Mechanism for Task Consistency

    • Automatically retry tasks that fail or encounter transient errors, up to a set limit.

    • Example: Handling intermittent connection errors for database queries with a max_retries of 5.

  3. Chained Follow-Ups with Callbacks

    • Use callback_id to trigger additional processes after a delegated task completes.

    • Example: Once a bot retrieves financial data, another bot (via callback) generates a performance dashboard.

  4. Dynamic Task Assignment

    • Distribute tasks to available bots for load balancing, specifying none in target_bot to list and choose dynamically.

    • Example: A free bot is assigned user engagement routines, while busier bots handle compute-heavy tasks.

IMPORTANT: Overzealous delegation to a single bot may overload it—consider distributing tasks among multiple bots to optimize performance.

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Workflow/How It Works

  1. Step 1: Define the Delegated Task

    • Craft a clear prompt detailing the required steps or actions.

  2. Step 2: Identify the Target Bot

    • Specify target_bot or leave it blank to select from a list of available bots.

  3. Step 3: Set Retries & Timeouts

    • Adjust max_retries and timeout_seconds to handle potential failures or delays.

  4. Step 4: Execute & Track

    • Invoke _delegate_work; if callback_id is used, track follow-up workflows once tasks complete.

  5. Step 5: Monitor Results & Logs

    • Observe logs or updates from the assigned bot regarding success, retries, or errors.

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Integration Relevance

  • Workflow Extensions: Combine with process_manager_tools to define delegatable tasks, logging their execution.

  • Collaborative Workflows: Works with project_manager_tools to offload subtasks or dependencies to various bots.

  • Snowflake Operations: Cooperates with snowflake_tools for delegating large computations or advanced data queries.

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Configuration Details

  • Ensure bots have the permissions and capabilities to execute delegated tasks.

  • Keep instructions (prompt) concise yet complete to minimize confusion or misinterpretation.

  • Set max_retries and timeout_seconds realistically, balancing reliability with time constraints.

NOTE: For best results, confirm that the target bot or pool of bots is actively running and can handle additional delegated tasks in a timely manner.

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Limitations or Notes

  1. Task Dependencies

    • Tasks needing prerequisites should confirm that all resources are in place before delegation.

  2. Callback Complexity

    • Overly intricate callback chains can create debugging challenges or workflow slowdowns.

  3. Bot Availability

    • Delegated tasks may fail or be delayed if the chosen bot is offline or already overloaded. Use dynamic listing to improve success.

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Output

  • Delegation Responses

    • Confirmation when tasks are successfully assigned, or error messages if delegation fails.

  • Retry & Timeout Logs

    • Details on attempts, retry counts, and final outcomes.

  • Callback Triggers

    • Notifications or logs when follow-up actions specified by callback_id are launched.

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Example on GenesisAPI

User: Delegate to Investigator to research potential data sources for the NUMBER_OF_ORDERS column in the DEMO_CUSTOMER_TGT table.
Save the research results in the git repository at: demoresearch/{table_name_field_name}_demoresearch.txt

Genbot: Here's the plan to delegate this task to the Investigator bot:
1. Provide Context to Investigator: 
   I'll share the details of the target column NUMBER_OF_ORDERS in the DEMO_ANALYTICS.PUBLIC.DEMO_CUSTOMER_TGT table. 
   - Target Table: DEMO_CUSTOMER_TGT 
   - Target Column: NUMBER_OF_ORDERS 
   - Source Data: Limited to DEMO_RAW.JAFFLE_SHOP and DEMO_RAW.STRIPE schemas. 
2. Pre-create Git Placeholder: 
   I'll create a placeholder file in the repository at demoresearch/DEMO_CUSTOMER_TGT_NUMBER_OF_ORDERS_demoresearch.txt.
3. Delegate to Investigator: 
   I'll instruct the Investigator bot to research potential data sources and save the results in the specified Git location.
Let me proceed with these steps now! 🚀
🧰 Using tool: GitAction...
🧰 Using tool: DelegateWork...
      🤖 Investigator: Identifying target column for analysis
      🤖 Investigator: Searching for relevant tables and columns
      🤖 Investigator: Identified target column and planned steps
      🤖 Investigator: Searched metadata; found ORDERS and CUSTOMERS tables
      🤖 Investigator: Analyzed metadata; found ORDERS, CUSTOMERS, and PAYMENT_SUMMARY tables
      🤖 Investigator: Analyzed ORDERS, CUSTOMERS, and PAYMENT_SUMMARY tables; documented findings
      🤖 Investigator: Conducted metadata search in DEMO_RAW database, found relevant tables and columns
      🤖 Investigator: Analyzed ORDERS, CUSTOMERS, and PAYMENT_SUMMARY tables; saved findings to Git
      🤖 Investigator: Identified key columns in ORDERS, CUSTOMERS, and PAYMENT_SUMMARY tables; documented findings
The task has been successfully delegated to the Investigator bot, and the research findings for the NUMBER_OF_ORDERS column have been documented and saved in the Git repository at the specified path:
File Path: demoresearch/DEMO_CUSTOMER_TGT_NUMBER_OF_ORDERS_demoresearch.txt 
📂 The Investigator bot has analyzed the relevant tables and columns, focusing on the ORDERS table in the JAFFLE_SHOP schema as the primary source. Let me know if you'd like to review the findings or proceed with the next steps! 🚀

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Example on Slack

In this example, we asked the Req Project Manager Genbot to delegate a task to the Investigator Genbot. The Investigator Genbot then researched potential data sources for the NUMBER_OF_ORDERS column in the DEMO_CUSTOMER_TGT table. A Git placeholder was also created at demoresearch/DEMO_CUSTOMER_TGT_NUMBER_OF_ORDERS_demoresearch.txt to store findings.

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