AI-driven Complaint Resolution Agent

Agent Description:

The AI-driven Complaint Resolution Agent automates first-line customer support for common issues. It receives user complaints, searches a connected knowledge base (via API) for matching problems and predefined solutions, and returns a polite, professional response. If no relevant match is found, it provides a generic fallback message, ensuring consistent and efficient query handling.

Purpose and Components
  • Purpose: To automate the first line of customer support by intelligently matching incoming complaints to a knowledge base of resolutions, enabling instant and consistent answers to common problems.
  • Components:

    • Complaint Checker: An agent that ingests the user query and searches a dataset for a matching complaint.

    • Resolution Generator: An agent that takes the matched resolution (or a "no match" signal) and formats a professional, user-friendly response.

    • Data Connector: A tool to fetch the complaint resolution dataset from an external source.

Supported Capabilities

  • Accessing and parsing a complaint resolution dataset (via a GET request).

  • Extracting complaint entries and their corresponding solutions.

  • Comparing a user's query against the dataset using keyword or semantic matching.

  • Passing a matched complaint and its resolution to the next step.

  • Formatting a pre-defined resolution into a polite, professional customer-facing message.

  • Generating a generic, polite "fallback" response if no match is found (e.g., "We are reviewing your issue...").

  • Outputting the final response to the user.

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. Complaint Checker

  • Role: Customer query checker

  • Scope: Matches user complaints with entries in the PDF dataset.

  • Description: This agent is the starting point. It takes the user's incoming complaint and uses its 'Request - Get' tool to access a dataset of known complaints. It then parses this dataset and attempts to find a match for the user's issue. If a match is found, it passes the complaint and its specific resolution to the Resolution Generator. If not, it informs the next agent to use a generic reply.

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

  • Tools Used: Request - Get

2. Resolution Generator

  • Role: Resolution generator

  • Scope: Provides a user-friendly answer using the dataset.

  • Description: This agent receives the information from the Complaint Checker. If it receives a matched resolution, it formats it into a polite and professional message for the customer. If it's informed that no match was found, it generates a standard fallback response, such as "We are currently reviewing your issue and will get back to you shortly," and outputs this as the final answer.

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

  • Tools Used: None

Tools Used:

  • Request - Get Tool: Accesses a remote JSON file (acting as the "PDF dataset") containing the list of complaints and resolutions.

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

  1. A user submits a complaint, which is received by the Complaint Checker (start node).

  2. The Complaint Checker uses its 'Request - Get' tool to fetch the complaint resolution dataset.

  3. It compares the user's query to the dataset.

  4. It then passes its findings (either a matched resolution or a "no match" notice) to the Resolution Generator.

  5. The Resolution Generator (end node) formats the appropriate response (either the specific solution or a generic fallback message) and presents it to the user.

Sample Questions:

  • My package hasn't arrived yet.

  • I received the wrong item in my order.