Agent Assist

How agent assist works

During a call, the audio is transcribed in real time. For chat and email the text is already there. A language model reads the conversation as it develops and decides what would help the agent right now:

• Knowledge surfacing: when the customer mentions a lost card, the card replacement article and its steps appear without the agent searching. This depends on a clean, current knowledge base.
• Suggested responses for chat and email that the agent edits and sends.
• Next-best action: a prompt to offer a payment plan, check eligibility or verify identity before discussing account details.
• Compliance reminders: a required disclosure that has not been read yet, flagged before the call ends.
• Form filling: account details, addresses and reasons picked from the conversation and dropped into CRM fields.
• Call summary: a draft note and disposition at the end of the call for the agent to confirm, which cuts after-call work.

Where it helps most

Agent assist does the most for agents who know the least. A tenured agent already knows where the article is; a new hire in week two does not. That makes it useful in operations with high attrition and large, frequent training classes.

The benefits contact centers commonly look for:

• Faster ramp. New agents reach target handle time and quality sooner, which shortens speed to proficiency and the time spent in nesting.
• Shorter hold and wrap times, because searching and note-writing shrink.
• More consistent compliance on regulated calls.
• Lower stress. Agents who feel supported on hard calls are less likely to quit in the first 90 days.

Results vary widely by call type, knowledge base quality and how well the tool is tuned. Run a controlled pilot, with similar agents on the same queue with and without the tool, before committing to a business case.

Agent assist vs speech analytics vs AI coaching

• [Speech analytics](/glossary/speech-analytics/) works after the call, across many calls, to find patterns and score quality.
• Agent assist works during the call, for one agent, to help with this customer now.
• [AI coaching](/glossary/ai-coaching/) works between calls: simulated practice conversations, feedback on recorded calls and targeted lessons.

The three usually share the same transcription and language model layer. Centers get the most from them when insights move between them, for example when analytics shows that agents miss a step on refund calls, a real-time prompt is added for that moment and a short practice scenario is assigned to agents who keep missing it.

Rolling it out well

1. Fix the knowledge base first. Agent assist surfaces whatever content exists. Outdated or contradictory articles get served faster, not better.
2. Start with two or three high-volume call types and a small pilot group, including new and tenured agents.
3. Keep prompts sparse. A screen full of pop-ups distracts agents and slows calls. Show one suggestion at a time and let agents dismiss it.
4. Measure adoption: how often suggestions are used, ignored or edited. Low use usually means low relevance.
5. Ask agents. Floor feedback finds wrong prompts faster than any dashboard.
6. Update training. New-hire classes should teach agents to use the tool and to check its suggestions, not to depend on it blindly.

Risks and governance

Generated suggestions can be wrong. Agents must stay responsible for what they tell customers, and suggested responses in regulated industries should be restricted to approved content. Keep a log of what the tool suggested and what the agent did.

If agent assist data is used to evaluate or discipline agents, treat it like any other performance data: tell agents what is captured, validate accuracy and keep a human in the decision. Recording consent rules and data protection laws such as GDPR apply to the underlying transcripts, and requirements vary by state and country.