The Sentiment Analyzer Agent, powered by Agentic AI, calculates a comprehensive sentiment score for incoming tickets. By analyzing the context and emotions beyond the literal meaning of words, it identifies distinct customer sentiments such as happiness, sadness, anger, trust, and more. This in-depth understanding enables support reps to prioritize critical issues, addressing them proactively before they snowball, and ensuring a more personalized and effective customer support.
The Escalation Predictor Agent predicts the likelihood of customer escalation and enables support reps to take proactive actions to minimize the repercussions of such cases.
Case Prioritization: By learning patterns and relationships between input
factors, such as historical data, case created, case modified, average wait time, priority,
and comments exchanged, it identifies and prioritizes critical issues before they
escalate.
Case Routing: The pre-built Intelligent Routing Agent facilitates faster
resolutions by routing cases to the most suitable support rep, ensuring the right expertise
is available from the start. This proactive approach significantly reduces turnaround time
(TAT), leading to better CX.
Agent Helper provides a comprehensive view of a customer’s case timeline, capturing a detailed history of the user's interactions leading to case creation, including self-serve query searches and links. This is followed by meticulously detailed, bulleted points for each user-support agent conversation, accompanied by sentiment analysis around every support agent action over time. This complete, on-the-go case information allows support reps to deliver hyper-personalized support and ensures a smooth transition if the case is transferred to another agent.
The Summarization Agent utilizes Agentic AI and LLM technology to automatically generate both brief and detailed summaries of customer interactions. By building on insights from the case timeline, it provides a precise overview of the case’s current status, including user and agent actions. This capability enhances support agents' productivity by reducing manual note-taking, improves accuracy by capturing critical details precisely, and speeds up case resolution with clear, actionable summaries.
The Auto Generation Agent, with the power of Agentic AI and LLMs, auto-generates initial responses for incoming cases by analyzing case fields and information sources. This ensures prompt replies, significantly reducing customer wait times and enhancing their overall experience. To further improve response quality and fuel hyperpersonalization, support agents have the option to edit or regenerate responses via AI Editor.
The AI Editor offers support agents a suite of powerful tools—Search and Writing—to fine-tune AI-generated responses. The Search tool provides real-time access to relevant resources, links, and summarized content, allowing agents to enrich replies with accurate information on the spot. The Writing tool enables agents to customize responses by adjusting tone, clarity, and format, whether it’s generating, summarizing, expanding, or rephrasing content to suit the customer’s specific context. With AI Editor, agents can effortlessly enhance automation while maintaining the personal touch that customers value.
Agent Helper allows support agents to set the tone for automated responses based on different parameters, like formality, assertiveness, emotion, etc. This enables them to move beyond pre-written templates and craft messages that resonate precisely with the specific customer interaction and their unique needs. With this level of granular control, agents feel more efficient and empowered to drive positive customer experiences.
Once support agents are equipped with a comprehensive understanding of cases, they are positioned to take decision actions. Agent Helper’s Knowledge Discovery Agent facilitate the process by intelligently curating a list of top articles that were instrumental in resolving similar tickets. This helps agents to locate the information they need swiftly, resulting in significantly reduced turnaround time (TAT) and mean time to resolve (MTTR).
The Knowledge Discovery Agent further streamlines the case resolution process by tapping into the case archives to identify past cases with thematic and technical similarities to the current issue. By accessing these relevant historical cases, support agents can efficiently draw from previously effective solutions, enhancing their ability to resolve customer inquiries swiftly and expediting case closure.
Agent Helper’s Swarming Collaborator Agent encourages collaboration and knowledge sharing among support agents. It ensures that one agent works on a ticket from start to finish. Should they get stuck at some point, they can instantly create a dedicated channel for the issue using built-in Slack integration. There, they can seek the expertise of SMEs or collaborate with their team members to deliver more relevant and faster resolutions.
Agent Helper offers support agents the option to engage in a conversational search with the Assist Bot Agent. Instead of navigating through multiple tabs, support reps can pose questions directly and receive the most relevant information through an intuitive, conversational interface. Additionally, they can directly incorporate links shared by the bot into their responses with a single click, leading to streamlined case resolution.
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