What is an AI Agent for customer service?

A fully autonomous customer service agent that can manage all customer interactions from start to finish – without human intervention – using natural language, business context, and product knowledge. AI Agents can seek information, make decisions, and take action to comprehensively resolve even the most complex queries and requests.

Why are they different from chatbots?

AI Agents represent a major transformation from old-school chatbots. They don’t deflect, they resolve. They’re conversational, engaging, intelligent, and able to adapt to the context of every customer’s situation. They’re always on and instantly available, driving high-quality outcomes for customers at any scale. They can also take action, like issuing refunds, updating orders, and changing account settings.

Customers get what they need, fast. Support teams get time back to focus on more strategically important work. Businesses get support that scales without scaling cost. They mark a shift from automation being just a cost-saving tool to automation as a resolution engine that drives real outcomes and value. When integrated well, they elevate the entire support experience and allow teams to rethink the role – and value – of support in the business.

Benefits of using an AI Agent

The key benefits of adopting an AI Agent revolve around customer experience and operational efficiency:

• 24/7 support AI Agents are available around the clock, in any language, on any channel. This ensures your customers receive timely, conversational responses, regardless of where they are in the world.

• Fast resolutions at scale Customers don’t need to wait hours, days, or even weeks to get an answer or resolution to their question.

• Increased self-service across every channel, including phone, email, chat, social, etc. Adding more ways for customers to get answers on their own at any time and on any channel boosts satisfaction, shortens time to resolution, and helps drive activation by making high-quality support instantly accessible.

• Enhanced human efficiency By enabling the AI Agent to handle the bulk of customer queries (both informational and complex), you can do more with less, and at a lower cost. Human agents can dedicate time to new focus areas, like optimizing the AI system and proactively driving customer success.

• Competitive advantage AI Agents provide a differentiated support experience, so you can offer better support than your competitors and drive customer happiness, loyalty, and retention.

AI Agents create significant business value: reduced support costs, increased resolution speed, and improved customer experiences. They’re a strategic investment for modern support teams.

How do AI Agents work?

AI Agents:

• Deeply understand customer intent Using large language models (LLMs) and advanced natural language understanding.

• Resolve queries and personalize interactions Using customer data, conversation history, and real-time context.

• Generate contextually accurate answers Using a technique called retrieval-augmented generation (RAG), AI Agents pull relevant content from your support knowledge base and other defined sources to craft precise, brand-aligned responses.

• Know when to hand over to a human When AI Agents aren’t able to resolve a query, they know the right time to get a human in the loop and can seamlessly hand conversations over to the right teammate.

• Continuously improve through feedback They create feedback loops, surfacing content gaps and failure points so teams can close them proactively. This reflects a closed-loop system where AI is both operational and diagnostic.

It’s important to note that not all AI Agents are built the same way. For example, Fin is built on a proprietary Fin AI Engine™, which is a patented AI architecture purpose-built for the scale and complexity of customer service. At its core is a layered system of custom-trained fin-cx models that refine every query, retrieve and rerank the most relevant content, and optimize responses for accuracy, speed, and reliability. It includes:

• A custom retrieval-augmented generation (RAG) system, which understands the question, identifies, and ranks content by accuracy and relevance.

• Multi-stage validation, which checks every answer for quality, accuracy, and policy fit.

• Modular sub-agent architecture, which breaks each query into specialized tasks, each handled by a tailored LLM sub-agent to deliver the highest quality answers.