Understanding How Virtual Agents Work

Anwesha Roy

Why virtual agents are so useful 

Understanding How Virtual Agents Work

Back in 2018, Gartner had predicted that 25% of all customer service operations will become automated within the next two years, replacing manual efforts with virtual customer assistants. While we may not have hit the 25% number, research suggests that customer-facing virtual agents are well on their way to becoming a mainstay for contact centres. Deloitte found that 13% of companies have fully deployed AI-enabled virtual advisors/chatbot technology, with another 59% in the pilot stage. Going by these current numbers, AI could become part of 95% of all customer interactions by 2025. 

At the heart of this emerging trend lies virtual agent technology and its revolutionary potential in customer service automation.  

What are Virtual Agents?  

Virtual agents can be defined as an AI-enabled assistant that can autonomously address customer queries, doubts and grievances entirely through a conversational experience that uses a natural language like English. Virtual agents act and appear just like a live agent, but do not require any human intervention. The conversational flow, script, and decision-making triggers are pre-programmed into the algorithm and the AI incrementally improves with every interaction.  

Virtual agents are different from bots, in that they have far more extensive cognitive capabilities. They mimic human personality and tone of voice, they can understand complex queries, and they have names or even animated avatars to recreate a personal touch.  

How Do Virtual Agents Work? Key Technologies

  • A conversational interface – The front end of a virtual agent appears as a conversational interface, which can be either voice or text-based. The customer types in or speaks out their query in their natural language, the agent responds, the customer reacts, and the interaction continues until issue resolution or redirection to a live agent
  • Natural language processing – The virtual agent is capable of natural language understanding (NLU) and natural language generation (NLG) to convert customer queries into a machine-readable format and the resulting response back into a human-readable format
  • Text and sentiment analytics – The agent can recognise important words and phrases to understand your exact query and intent. It is even capable of detecting mood undercurrents like anger or frustration, using the appropriate script accordingly. The analytics data is fed into a backend system to power 360-degree customer intelligence for future interactions
  • Speech analytics – Voice-based virtual agents use speech analytics instead of or in addition to NLP to make sense of human uttered speech
  • AI and ML – Keeping in mind that natural language processing is an AI technique, virtual agents also use ML to become incrementally more accurate. If the customer corrects the agent the first time, it learns from the error and avoids making the same inference the next time around. Another AI technique used by virtual agents is optical character recognition (OCR), helpful when extracting the text out of a customer screenshot, almost as if a human was reading it 

Today, virtual agents are still considered experimental technology and only a complement for live conversations. With advancements in AI and ML, this would gradually change and relegate all basic conversations to an automated medium.  



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