Quiq Voice Assist Brings Agentic AI Into Live Customer Calls

What happened

Quiq has launched Voice Assist, an agentic AI tool designed to support human agents during live customer calls. The tool listens in on conversations in real time, surfaces contextual prompts and guidance, and can also take actions on the agent's behalf — such as pulling up account information or completing backend tasks — without the agent needing to switch screens or pause the conversation.

Unlike passive call-assist tools that simply transcribe or suggest scripted responses, Voice Assist is positioned as agentic: it can act autonomously within a call to execute steps a human agent would otherwise have to do manually, aiming to reduce handle time and cognitive load while the conversation is still live.

Why it matters

Voice Assist reflects a broader shift in contact-centre AI from advisory copilots toward tools that take direct action during the interaction itself. For technology and operations leaders, this changes the calculus on what "assistance" means in a live-call environment: rather than agents consulting a knowledge base or bot suggestion after the fact, the system can execute tasks in parallel with the conversation, potentially compressing the gap between customer request and resolution.

This matters for digital transformation roadmaps because it moves agentic AI from back-office automation into the frontline, real-time customer interaction — a higher-stakes environment where trust, accuracy and hand-off design all need to be carefully managed.

The Renascence take

The interesting story here isn't the automation itself — it's what happens to the human agent's role once an AI can act mid-conversation on their behalf.

Most coverage of agentic AI in contact centres focuses on efficiency gains, but the real design challenge is trust calibration: agents need to know when to lean on the system and when to override it, and customers need to sense continuity, not a hidden hand steering the call. Operators piloting tools like this should treat the first months as a behavioural experiment — tracking not just handle time, but whether agents' confidence and judgement erode or sharpen, and whether customers can tell (or care) that part of the call was AI-driven. Get that governance layer wrong, and you risk trading a small efficiency gain for a slow leak in service quality and agent ownership.