Array ( [0] => Array ( [_id] => 67222a1ab66e260024317675 [name] => Knowledge Gap Analysis Agent [description] => The Knowledge Gap Analysis Agent is designed to improve the effectiveness of the knowledge base by identifying recurring support issues that need to be adequately addressed in existing articles. Using generative AI, this agent analyzes patterns in support tickets, customer inquiries, and feedback to detect topics or issues frequently encountered by the support team. By recognizing these gaps, the agent generates a list of suggested updates or new articles that can fill these informational voids, ensuring that common queries are more effectively addressed.

The Knowledge Gap Analysis Agent helps customer support teams stay proactive in knowledge management, enhancing self-service options for customers and reducing the volume of repetitive inquiries. By continuously analyzing support data and refining its suggestions based on customers' evolving needs, this agent plays a key role in maintaining a comprehensive, up-to-date knowledge base. This approach not only improves customer satisfaction by providing them with accurate information but also boosts the support team's efficiency by reducing the need for repeated explanations of common issues.

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The FAQ Generation Agent automates the creation of FAQs by analyzing resolved helpdesk tickets from various platforms. Utilizing a Large Language Model (LLM), it extracts pertinent questions and answers, refines existing entries, and integrates new information, ensuring accurate and up-to-date FAQs.

Challenges the FAQ Generation Agent Addresses

Helpdesk agents struggle to keep FAQ knowledge bases current as manual updates are error-prone and time-consuming, often leading to outdated content, inconsistencies, and delays. Additionally, FAQs may not reflect recent product changes, policies, or customer issues promptly. These challenges are compounded when integrating real-time insights from enterprise platforms like CRM and customer support systems.

The FAQ Generation Agent enhances self-service support by automating the integration of relevant new questions into FAQ knowledge bases. The agent minimizes human effort, ensures accuracy, and maintains operational efficiency by utilizing a Large Language Model (LLM) to identify and evaluate FAQs and analyze helpdesk interactions. This continual refinement of FAQs reduces repetitive inquiries, streamlines customer support, and improves user satisfaction, keeping the knowledge base relevant and effective.

How the Agent Works

The FAQ Generation Agent is designed to automate the creation and updating of FAQs based on helpdesk ticket interactions across multiple platforms. Utilizing the capabilities of an LLM, this agent analyzes the content of closed tickets to extract essential questions and answers, ensuring that the FAQs remain relevant and comprehensive. Below, we detail the agent's workflow, from the initial analysis of helpdesk tickets to the ongoing enhancement of the FAQ repository.

Step 1: Closed Ticket Inputs and Initial Analysis

This initial step begins with an API call to access resolved helpdesk tickets on the specified platform.

Key Tasks:

• API Call: The agent makes API calls to retrieve multiple resolved tickets from the associated helpdesk platforms within a specified period.
• Input Collection: The agent extracts relevant information from the tickets. This includes data such as field summaries, user queries, and responses from expert personnel.

Outcome:

• Processed Ticket Data for FAQ Generation: The outcome of this step is a compiled dataset of comprehensive and relevant information that will serve as the foundation for identifying new FAQs.

Step 2: Processing Resolved Tickets and Comments

In this step, the agent processes tickets and their associated comments to create a comprehensive helpdesk interaction dataset. This involves utilizing two nested loops: the outer loop handles the resolved tickets extracted from the previous step, and the inner loop manages comments associated with each ticket.

Key Tasks:

• Task Summary Extraction: Each ticket is processed in sequence to extract the user's question from the ticket's summary.
• Comment Processing: A nested loop then processes each comment within the ticket, appending it to the related question to build a cohesive conversation history.
• Dataset Aggregation: The combined data of task summaries and associated comments are aggregated into a unified conversation dataset, representing the entire interaction thread for each ticket.

Outcome:

• Refined Dataset: A refined dataset containing each ticket's summary and appended comments, providing a complete view of each interaction. This dataset is essential for accurately updating and generating relevant FAQs.

Step 3: FAQ Extraction

In this step, the agent uses an LLM to extract potential FAQ questions and answers from the dataset associated with the extracted tickets.

Key Tasks:

• FAQ Identification: Using a predefined prompt, the agent interacts with the LLM to identify relevant FAQ questions from the dataset.
• Question Generation: The LLM then generates a list of potential questions, along with their corresponding answers, based on the information in the dataset.

Outcome:

• FAQ Extraction: A curated list of relevant FAQ questions and answers is generated from the ticket conversations.

Step 4: Knowledge Base Context Comparison and Update

In this step, the agent first compares extracted FAQs with the existing knowledge base to identify duplicates and recognize new or improved entries, then updates the knowledge base accordingly.

Key Tasks:

• Context Matching: The agent queries the knowledge base to determine if a newly identified FAQ question already exists. This prevents duplication and ensures the relevance of the content.
• New Question Addition: If the question is new, it is added to the knowledge base along with its corresponding answer, expanding the repository with fresh and relevant information.
• Answer Evaluation: For existing questions, the agent uses the LLM to assess whether the newly generated answer is more accurate or informative than the current one in the knowledge base.
• Update or Retain: The agent replaces old entries with new answers if they provide better clarity or information. If the new answer does not improve upon the existing one, the original entry is retained. This ensures that the knowledge base remains accurate, relevant, and comprehensive.

Outcome:

• Knowledge Base Context Comparison: The agent ensures that only new questions and answers are added to the knowledge base.
• Update Knowledge Base: The FAQ knowledge base is updated, ensuring accuracy and relevance.

Step 5: Continuous Improvement Through Human Feedback

After updating the FAQ knowledge base, the agent integrates feedback from the helpdesk team to continuously refine the accuracy and relevance of the FAQs.

Key Tasks:

• Feedback Collection: Users can provide feedback on the clarity, accuracy, and relevance of the FAQ entries based on their interactions with customers and personal expertise.
• Feedback Analysis and Learning: The agent analyzes the feedback to identify common issues and areas where FAQ entries may be lacking or misaligned with user needs, pinpointing opportunities for refining its content generation process.

Outcome:

• Adaptive Enhancement: The agent continuously refines its FAQ generation capabilities, ensuring it adapts to evolving user queries and the practical insights of the users. This ongoing learning process is essential for maintaining high standards of clarity and usefulness, enhancing the agent's effectiveness over time and improving overall customer support quality.

Why Use the FAQ Generation Agent?

• Time Efficiency: Automates the repetitive task of manual FAQ generation and updates, saving significant time for support teams.
• Enhanced Knowledge Base Accuracy: Ensures the FAQ repository remains current, providing precise and relevant answers to users.
• Improved User Experience: Reduces unresolved queries and enhances user satisfaction with a well-maintained FAQ system.
• Reduced Support Overhead: Minimizes the workload on support teams by automatically addressing recurring questions.
• Continuous Improvements: Leverages LLM capabilities to provide better context-aware FAQs, ensuring they remain relevant to evolving user needs and preferences.

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Customer Service

Knowledge Gap Analysis Agent

Identifies recurring support issues missing from the knowledge base, highlighting areas for documentation updates.

Customer Service

FAQ Generation Agent

Automatically generates FAQs from helpdesk tickets and resolutions, creating accessible answers to recurring support issues and questions.