AI-powered Policy Drafting Agent

Agent Description:

The AI-powered Policy Drafting Agent streamlines the creation of new internal policies. It retrieves relevant existing documents based on a user’s request (e.g., “draft a hybrid work policy”). The agent then synthesizes this information with the user’s specified conditions while maintaining a formal corporate tone. Finally, it generates a structured draft policy, highlighting any new or modified clauses for easy review.

Purpose and Components
  • Purpose: To automate and streamline the creation of new internal policies by intelligently retrieving relevant context from existing documents and combining it with new, user-specified conditions into a formal, structured draft.
  • Components:

    • Document Retriever: An agent that fetches and parses existing company policies based on a user's request.

    • Policy Drafter: An agent that synthesizes the retrieved information with new requirements to write a formal policy draft.

    • API Connector (GET): A tool used to fetch the raw policy document data from a source.

Supported Capabilities

  • Fetching existing company policy documents from a data source via a GET request.

  • Reviewing a user's input (e.g., "Draft a hybrid work policy").

  • Identifying and retrieving relevant existing policy drafts (e.g., finding "Remote Work Policy" and "Attendance Policy" for a "hybrid work" request).

  • Extracting and preprocessing key sections from reference documents (Policy Name, Purpose, Guidelines, etc.).

  • Generating a summarized JSON context that combines extracted sections with the new user preferences.

  • Constructing a new, structured policy draft that includes standard sections (Title, Purpose, Scope, Key Provisions, etc.).

  • Maintaining a formal corporate language and legal tone.

  • Highlighting new or modified clauses in the final output.

  • Returning the final policy draft in structured text or JSON for review.

LLM Used

  • GPT_4O_MINI

    Note: To learn more about the LLM and to modify its behavior, refer to the Configuring LLM settings section.

Sub-Agents

1. Reference Document Retriever

  • Role: Reference Document Fetcher

  • Scope: Retrieve documents that serve as input for drafting new policies.

  • Description: This agent initiates the workflow. It takes the user's request (e.g., "draft a hybrid work policy") and uses the GET tool to fetch the company's existing policy documents. It identifies the most relevant policies (like "Remote Work Policy"), extracts key sections, and combines this information with the user's new conditions into a summarized JSON context for the next agent.

  • LLM Used: Default (Inherits from parent agent).

  • Tools Used: Request - Get

2. Policy Drafting Generator

  • Role: Drafter

  • Scope: Takes raw content from reference documents and creates a structured draft suitable for internal review.

  • Description: This agent receives the processed context from the Retriever. It compares the user's new conditions with similar clauses in the existing policies and then constructs a new, formal policy draft. It ensures the draft maintains the correct corporate language, includes all standard policy sections, and highlights any new or modified clauses for easy review.

  • LLM Used: Default (Inherits from parent agent).

  • Tools Used: None

Tools Used:

  • Request - Get Tool: Fetches existing company policy documents from a specified JSON endpoint.

Note: For details on modifying the Tools, refer Tools Library section.
Agent Workflow Behavior Summary

  1. A user provides a prompt to the Reference Document Retriever (start node), such as "Draft a hybrid work policy with 2 days WFH."

  2. The Retriever uses the Request - Get tool to fetch all existing policy documents.

  3. It identifies relevant policies (e.g., "Remote Work Policy," "Attendance Policy"), extracts key sections, and combines them with the user's new requirements ("2 days WFH") into a single JSON context.

  4. This context is passed to the Policy Drafting Generator (end node).

  5. The Generator synthesizes all the information to construct a new, complete policy draft, written in a formal tone and highlighting the new clauses. This final draft is returned to the user.

Sample Questions:

  • Draft a hybrid work policy with flexible timing and 2 days WFH per week.

  • Create a new 'Social Media Usage' policy for employees, using the 'Code of Conduct' as a reference.