How to Build a Real Time Agent Assist Voice Agent for Call Centers

In customer service today, every second matters. Real time agent assist is transforming how call centers operate. Instead of analyzing calls after they end, real time agent assist gives agents live support during conversations, leading to faster resolutions, less stress, and happier customers. Take for example the company Clue. This rising AI company offers an undetectable desktop app that analyzes anything on an agent's screen to provide contextual insights and answers as well as real-time sales insights. objection handling suggestions and product knowledge during sales calls. Here's a simple demo of a real-time agent assist app. Below we have a streaming transcript of an active conversation between a support representative and a customer. — Help you today. — Hi Alexis, this is John. I'm calling to check on the status of my lens replacement. — On the right, the real-time agent assist provides real-time recommendations based on what it knows about the customer, prior interactions, and internal business data. All this helps a human agent stay confident and efficient, allowing them to focus on empathy and problem solving, not information gathering. With real-time agent assist, companies see 20 to 25% reductions in average handle time or AHT, 30% improvements in first call resolution, FCR, and 50% less after call work. The multiplier effect compounds better agent retention, improved service, and higher customer satisfaction. At the heart of real-time agent assist is a simple but powerful workflow. The customer's audio is transcribed in real time to an automatic speech recognition engine. Multiple AI models then analyzes the conversation using natural language processing to surface key terms, customer sentiment, and checks for compliance for required disclosures or prohibited language. A decision engine then checks existing knowledge bases and suggests next best actions. The human agent sees this instantly through their interface. Accurate transcription is critical here because everything downstream depends on it. Now, let's walk through the code of the simple demo so you can build your own real-time agent assist system. Information regarding company policies, product support information, and customer data need to be readily accessible to the real-time agent assist. This can be from a collection of text or PDFs, records from databases, or information from ragm servers and tools. When a call is connected, we start with the transcription using Assembly AI's streaming API to capture the customer's voice in real time. A multi- channelannel audio stream, one for the customer and another for the support representative, will be sent to Assembly AI via websockets. Next, the composite transcript from these streams is analyzed by an open source model like Llama 4 or Quen 3 running on inference providers such as Cerebras or Gro. This delivers the reasoning power needed to suggest responses or detect intent at low latency. The real-time agent assist is made up of several sub agents, one of which is a recommendations agent. It considers all the available internal knowledge about company policies, product details, customer details, and the current transcript to make uh suggestions on what the human support representative should do next. Together, these components power a seamless real-time agent assist system for supporting call centers. With Assembly AI, you can build real time agent assist systems that deliver real impact in call centers. Get started today by visiting assemblyai. com. For more content on working with voice AI technologies, like, subscribe, and check out our other