AI agent assist for live calls: when copilots pay off

I write this from the supervisor's chair, the person who listens to calls afterward, scores them, and coaches the rep who gave a wrong answer on a Tuesday afternoon. From that seat, "agent assist" is a second screen that tries to keep a human rep accurate while the customer is still on the line. Whether that is worth paying for depends on what is actually going wrong on your calls.

I also need to be plain about one thing. Communicate is a chat-based platform. It runs an AI agent on your website and in messaging, and it hands conversations to people through a shared inbox.

It does not answer phone calls and it does not coach reps during live calls. This guide explains the category so you can judge it, and it points to our product only where chat is the real answer.

What agent assist is, and what it is not

Agent assist keeps the human in the conversation. A rep takes the call as usual. In a side panel, the software transcribes the conversation as it happens, works out what the customer wants, and shows candidate answers, policy snippets, or next steps.

The rep decides what to say. When the call ends, the tool drafts a summary and disposition so the rep does not write one from memory.

A customer-facing bot is a different product. It talks to the customer directly, with no person between them unless it hands off. The failure modes differ too.

A bot that gets a refund policy wrong tells the customer something false in its own voice. An assist tool that gets it wrong shows a rep a bad suggestion, and a rep who knows the policy can ignore it.

Most teams do not have to pick one. A bot can resolve the simple volume before it reaches a person, and an assist tool can help the people who get the hard cases. The useful question is where your pain is.

If reps are slow and inconsistent, assist helps. If reps are drowning in repetitive questions, automation helps more.

How a real-time assist system works on a call

The mechanics are similar across vendors. Audio streams to a speech-to-text service, which produces a running transcript with speaker labels. A language model, or a combination of search and a model, reads the latest turns and decides whether to act.

It might detect an intent, such as a cancellation request, and retrieve the matching policy or procedure from a knowledge base.

Google documents this pattern in its Agent Assist product. Its documentation describes a machine learning service that offers suggestions to human agents during customer conversations, drawing on the business's own uploaded data, and its concepts pages list features such as proactive generative knowledge assist, summarization, smart reply, sentiment analysis, and supervisor assist. The Agent Assist documentation is the primary source.

Amazon documents a comparable design. In the admin guide for Amazon Q in Connect, AWS describes a generative AI assistant that detects customer intent during calls and chats and gives agents responses and suggested actions, with links to relevant articles. For voice calls, it states that Contact Lens conversational analytics must be enabled, while chat does not need it.

The same page notes that the product was formerly called Amazon Connect Wisdom, and AWS product names change often, so confirm current naming before you buy.

Two details from those documents are worth carrying into any vendor conversation. First, the assist depends on your own knowledge base. The suggestion is only as good as the articles it can retrieve.

Second, the voice version depends on a separate analytics layer for live transcription, which affects both cost and privacy review.

What the research shows about productivity

The best-known public study is "Generative AI at Work" by Erik Brynjolfsson, Danielle Li, and Lindsey Raymond, published as NBER Working Paper 31161 in April 2023 and revised in November 2023. It studied 5,179 customer support agents who were given a conversational assistant that suggested responses. The abstract reports that access to the tool increased productivity, "as measured by issues resolved per hour, by 14% on average."

The result by experience level matters more to a supervisor than the average. The same paper reports a 34% improvement for novice and low-skilled workers and minimal impact for experienced and highly skilled workers. In other words, the tool narrowed the gap by lifting the weakest performers.

Read the limits as carefully as the headline. This was a chat-based setting, not live voice. It tested one company's deployment of one assistant.

It measured issues resolved per hour, which is not the same as customer satisfaction or accuracy. Nothing in it tells you what a particular vendor's product will do on your calls.

Even so, the pattern gives a practical hypothesis for your own pilot. If your team has high turnover or long ramp times, assist should help most for new hires. If your reps are all veterans, expect a smaller effect, and expect it to show up in consistency and summaries rather than speed.

This ties to handle time, which most contact centers track. Our guide to reducing average handle time covers where the minutes go on a call. Assist mainly attacks two of them, searching for an answer and writing the after-call note.

What reps and managers say they want

Community discussion is anecdotal, but it shows what practitioners worry about. In an r/CustomerService thread about tools for tax-relief reps, a commenter, u/GratefulAlonzo2223, wrote: "I would look for something that does more than search a knowledge base when the rep asks a question." The same commenter said the useful part is when the system understands the live conversation and brings up the right information on its own.

That comment names a real product difference. Basic knowledge search waits for the rep to type a query, which is slow in the middle of a call. Proactive assist listens and offers an answer when it detects the topic.

The second takes the search step away, and it also risks distraction if the suggestions are wrong or arrive late.

The same commenter added that they would care less about whether the tool sounds impressive and more about where it gets its answers from. For a regulated topic such as tax relief, I agree. A confident suggestion with no visible source is a liability.

Ask any vendor to show the source article for every suggestion, so the rep can verify it in a glance. The hallucination guide explains why grounding in your own documents matters.

I am quoting one thread, not a survey. The thread had a few dozen comments, and the author's experience may not match yours. Treat it as a prompt for questions to ask vendors, not as evidence about the market.

The features worth evaluating

Vendors list many features. Supervisors should weigh a short set that maps to real call problems. Use the list below as a scoring sheet for demos, and ask each vendor to show every item on your own recorded calls, not a prepared demo.

• Live transcription accuracy on your calls, including accents, product names, and noisy lines.
• Intent detection that fires early enough to help, and does not fire on every sentence.
• Knowledge suggestions that cite a source article the rep can open.
• Next-step prompts tied to your process, such as verification steps before an account change.
• After-call summaries with editable fields, and a way to push them into your CRM or helpdesk.
• Sentiment signals for supervisors, with a clear rule about whether reps see them.
• Supervisor tools such as live listen, whisper prompts, and escalation alerts.
• Controls for redaction of card numbers and personal data in transcripts.

Latency deserves its own test. A suggestion that arrives ten seconds after the customer finished speaking is useless on a fast call. Measure the delay from the end of the customer's sentence to the suggestion appearing, on at least twenty real calls.

Where assist pays off, and where it does not

Assist tends to help most in four situations. Your reps handle complex products with many policies. You hire often and ramp slowly.

Calls follow regulated scripts where one missed disclosure is costly. Or after-call work eats a large share of each rep's day.

It helps less when the main problem is volume. If thousands of calls ask where an order is, a copilot makes each call a bit faster, while automation can remove the call. It also helps less when the knowledge base is poor, because retrieval from bad articles produces confident bad suggestions.

A third case is the one supervisors underrate. Assist can raise consistency, which improves quality scores even when speed does not change. If you run a quality program, read our guide on support quality assurance with AI to see how automated scoring and live assist can share the same rubric.

Privacy, recording consent, and what the law says

Live assist means a third-party system processes the audio or transcript of every call. That raises questions that the demo will not mention. I am not a lawyer, and this section lists what to ask counsel, not what the law requires in your case.

Start with recording and transcription consent. Rules vary by country and, in the United States, by state. Some jurisdictions allow recording when one party consents, and others require all parties to consent.

A live transcript is generally treated like a recording for these purposes, so a greeting that tells callers the call may be recorded or monitored is a common starting point. Confirm the wording your jurisdiction needs with counsel.

Next, look at where the data goes. Ask where audio and transcripts are stored, for how long, and whether the vendor uses them to train models. Ask whether card numbers and other sensitive details are redacted before storage.

Payment details spoken on a call are a particular hazard, since a transcript can capture them in plain text.

Retention and redaction are not unique to voice. Our guides on PII redaction for support and AI support data retention cover the controls to request from any vendor, and the GDPR guide covers the European angle.

What the EU AI Act says about live-call tools

If you operate in the European Union, the AI Act touches this category in two places. Both are worth a short read, and neither is a reason to avoid the category. Check the current application dates with counsel, because timelines and guidance have been moving.

Article 50(1) is the transparency rule. As published on the AI Act Explorer, it requires providers to ensure that AI systems intended to interact directly with natural persons are designed so that "the natural persons concerned are informed that they are interacting with an AI system." The duty does not apply where this is obvious to a reasonably well-informed, observant, and circumspect person. The page states that Article 50 applies from 2 August 2026.

Read that carefully against the two products. A customer-facing voice bot interacts directly with callers, so it clearly falls under the disclosure idea. An agent assist tool talks to your rep, not to the caller, so the direct-interaction duty reads differently.

Whether your specific setup is in scope is a question for counsel, not for this article.

The second rule is more relevant to assist products with mood scoring. Article 5(1)(f) prohibits using AI systems to infer emotions of a natural person in the areas of workplace and education institutions, except for medical or safety reasons. The same source says this prohibition has applied since 2 February 2025.

If a tool scores the emotional state of your own reps, and not only callers, ask your vendor and your lawyer how they handle this. Our EU AI Act guide for support teams goes deeper.

Whether sentiment scoring of customers on a call counts as emotion inference under the Act is also an open question to put to counsel. I have not found a definitive answer in the primary text, so I will not assert one here.

Disclosure is also good practice outside the EU. Our piece on AI disclosure in customer support covers when and how to tell customers that AI is involved.

How to run a pilot you can trust

A supervisor's best defense against a vendor pitch is a controlled pilot. The design below takes four to six weeks and needs no special tooling beyond what your quality team already uses.

• Pick two comparable groups of reps, one with assist and one without, matched on tenure and call type.
• Record baseline numbers before the pilot starts: handle time, quality score, after-call work time, and repeat-contact rate.
• Score a fixed number of calls per rep per week with the same rubric, and have scorers blind to which group the call came from where possible.
• Log every suggestion that reps accepted, edited, or ignored, plus any that were wrong.
• Ask reps for weekly feedback in two sentences. Fatigue from bad suggestions shows up here before it shows in metrics.
• Set a stop rule in advance. If wrong suggestions cause a compliance miss, pause the pilot.

Judge the result on more than speed. A tool that cuts handle time by a minute but raises error rates is a bad trade. Watch for rep over-reliance too.

If reps stop thinking and read suggestions aloud, a single wrong suggestion spreads across many calls.

Many of the measures here come from our support KPIs guide. Use the same definitions in the pilot as in your normal reporting so results are comparable.

Costs and what to ask a vendor

Pricing for assist products is usually per seat per month, per conversation minute, or a platform fee plus usage. I will not quote list prices for the big cloud products, because they vary by region, usage tier, and contract, and I could not verify current figures from a primary source for this guide.

What you can do is model your own cost. Count your concurrent reps, average call minutes per month, and the share of calls you would route through assist. Ask each vendor for a quote on that exact profile, including transcription, the language model, storage, and any analytics layer that the voice feature depends on.

• Which parts are billed per minute, and which per seat?
• Is live transcription included, or is it a separate service with its own meter?
• What does an integration with our CRM and telephony system cost to set up?
• Can we export transcripts and suggestions if we leave?
• Are there minimums or commitments before the pilot ends?

For a broader view of cost drivers across AI support products, see our AI customer support cost breakdown.

If your customers are on chat, not the phone

Everything above applies to voice. Many small and mid-size teams find most of their volume arrives through chat and email, and for those teams the sensible first step is putting an AI agent in front of the chat queue and letting people handle what it cannot.

That is what Communicate does. Your AI agent answers from your data sources on your site and messaging channels, hands off to your team in the shared inbox with the full conversation, and can take approved actions. It is a customer-facing agent, so it is the other half of the comparison table earlier, and it does not listen to calls or coach reps in real time.

If you already run phone support and want to automate part of it, read the voice AI customer support guide first, and the handoff guide for how a bot should pass a case to a person.

Common failure modes to watch for

• Suggestions without sources. The rep cannot tell a policy from a guess.
• Late suggestions. The call has moved on by the time the panel updates.
• Alert fatigue. Too many prompts train reps to ignore the panel, including the one that matters.
• Stale knowledge. The tool cites an old refund rule because nobody updated the article.
• Hidden surveillance. Reps learn that the tool scores them, trust drops, and the best people leave.
• Over-trust. New hires accept every suggestion because they cannot judge it.

Most of these are process problems, not model problems. Keep the knowledge base current, keep the panel quiet, tell reps exactly what is measured, and keep a human accountable for every answer.

Preparing your knowledge base before any pilot

Assist tools retrieve from your documents, so the preparation work is the same whether the output goes to a rep or a customer. Teams that skip it blame the tool when suggestions disappoint. Spend a week on the sources first and the pilot will tell you something real about the product.

Begin with the twenty call reasons that account for most of your volume. For each, find the article a good rep would use. If there is none, write one.

If there are three, merge them. If the article is a long policy document with the answer buried in the ninth paragraph, split it so that one page answers one question.

Write for retrieval. State the rule in the first sentence, name the conditions, and put dates on anything time-bound. A suggestion drawn from "Refunds are available within 30 days of delivery for unused items" is easy for a rep to say aloud.

A suggestion drawn from a paragraph of legal phrasing is not.

Our guides on structuring a knowledge base for AI and finding knowledge gaps describe the same discipline for chat agents, and the work transfers directly. A gap that makes a bot fail also makes an assist panel go quiet at the worst moment.

A hypothetical call, step by step

The following walkthrough is an illustration I constructed to show the sequence, not a record of a real customer or a measured result. It shows where assist helps and where the rep still carries the call.

A customer calls to cancel a subscription because of a billing error. The transcript shows the words "cancel" and "charged twice." The tool detects two intents, cancellation and a billing dispute, and surfaces two articles. One is the cancellation procedure.

The other is the duplicate-charge policy, which includes the verification steps required before issuing a refund.

The rep reads the duplicate-charge article first, because it addresses the customer's upset. The rep verifies identity as the article requires, then processes the refund. The tool prompts the cancellation steps only after the rep has resolved the billing complaint, since the rep chooses the order.

At the end of the call, the tool drafts a summary with the reason, the refund amount the rep stated, and the cancellation outcome.

The rep edits the summary because the draft recorded the wrong refund date. That edit is the point. The summary saved five minutes of typing, and the rep's review caught the error before it reached the record.

A workflow that lets reps submit summaries without reading them removes the safeguard.

Notice what the tool did not do. It did not decide to issue the refund, did not speak to the customer, and did not pick the order of topics. Those remained the rep's judgment, which is why the risk profile differs from a bot.

Rollout and coaching after the pilot

If the pilot succeeds, roll out in stages. Start with the group that benefits most, which the research suggests is newer reps, then widen. Keep a feedback channel open, and treat every wrong suggestion as a knowledge-base ticket to fix.

Coaching changes too. Supervisors used to coach on what reps said. With assist, you can also coach on how reps used the suggestions.

A rep who ignores correct suggestions needs different help from one who accepts wrong ones. The accept, edit, and ignore log from the pilot becomes a coaching dataset.

Be open with reps about what the tool records. Say which signals supervisors see, whether suggestions feed performance reviews, and how long the data stays. Reps who feel watched tend to work around the tool, and reps who understand it tend to report problems early.

Finally, revisit the build-versus-buy question once a year. Contact-center platforms add assist features steadily, and your own needs change. Our build versus buy guide offers a framework for the decision that applies equally to a copilot.

Metrics that mislead during an assist pilot

Pilots go wrong when the scorecard rewards the wrong thing. Four metrics deserve suspicion, and a supervisor should name them before the vendor does.

Average handle time is the obvious one. A shorter call can mean a faster answer, or it can mean the rep rushed the customer off the phone. Pair it with repeat-contact rate and quality score, so a call that ends quickly but returns tomorrow counts as a loss.

Suggestion acceptance rate is the second. A high rate may show that suggestions are good, or that reps trust them too much. Review a sample of accepted suggestions for accuracy, and compare acceptance among novices and veterans.

If only novices accept everything, check whether they can tell good from bad.

Summary completion is the third. A tool can fill every field and still record the wrong details. Audit a sample of summaries against the call audio each week during the pilot.

Customer satisfaction is the fourth, and the most tempting. Survey response rates are low and noisy, so a small change in scores over four weeks rarely means anything. Use it as a guardrail against collapse and not as proof of improvement.

If you want a fuller metric set, the CSAT benchmark guide and the customer effort score guide explain what each measure can and cannot tell you.

Questions for your telephony and CRM teams

A copilot sits between your phone system, your knowledge base, and your CRM. Each connection has an owner who should answer questions before you sign anything.

• Telephony: how does call audio reach the vendor, and does it pass through your recording system or around it?
• Telephony: can we keep the current greeting and consent wording unchanged, or does the vendor require a new one?
• CRM: which fields can the summary write, and does a human approve each write?
• Knowledge: who updates the articles when a policy changes, and how quickly does the tool re-index them?
• Security: what certifications does the vendor hold, and can we review them under NDA?
• Exit: what happens to stored transcripts if we cancel?

Answers that take weeks to arrive are an answer in themselves. A vendor that cannot explain its audio path or retention plainly during the sales process will be harder to question after you have signed.

Conclusion

Agent assist is a good fit for phone-heavy teams with complex policies, high turnover, or heavy after-call work, and the public research suggests the biggest gains go to newer reps. It does not fix poor knowledge, and it carries consent and transparency questions that deserve a lawyer's time before you sign. If most of your customers reach you through chat, start with an agent that answers them directly and hands off cleanly.

You can see Communicate pricing and try that path without any telephony work.

Frequently asked questions

What is agent assist?

It is software that supports a human rep during a live conversation. It transcribes or reads the exchange, suggests answers and next steps from your knowledge base, and drafts a summary afterward. The rep stays in control and the customer still talks to a person.

How is agent assist different from a chatbot?

A chatbot talks to the customer directly. Agent assist talks to the rep. An error from a chatbot reaches the customer unfiltered, while an error from an assist tool reaches a trained person who can reject it.

They solve different problems and teams often use both.

Does agent assist reduce handle time?

Often it can, mainly by removing search time and after-call writing. The NBER study found a 14% average gain in issues resolved per hour, but in chat, not voice. Measure your own handle time before and after, because vendor claims are not a substitute.

Who benefits most, new reps or experienced ones?

The NBER paper found a 34% improvement for novice and low-skilled workers and minimal impact for experienced and highly skilled workers. If you hire often, expect the benefit to concentrate in new hires. Experienced reps may gain more from summaries than from suggestions.

Does it work on live phone calls?

Yes, vendors such as Google and Amazon document real-time assist for voice. It requires live transcription, and AWS states that Amazon Q in Connect needs Contact Lens conversational analytics for calls. Test accuracy and delay on your own recordings before you commit.

Do I need to tell callers their call is being transcribed?

Usually you should, and in some places you must. Recording and monitoring rules vary by country and, in the United States, by state, and some states require every party to consent. Ask counsel for the wording and apply it in your call greeting.

Does the EU AI Act apply to agent assist tools?

Possibly, depending on the tool. Article 50(1) covers AI systems that interact directly with people, which fits a customer-facing bot better than a rep-facing copilot. Article 5(1)(f) restricts emotion inference in the workplace.

Ask counsel how each applies to your specific features.

Is sentiment scoring of reps allowed in the EU?

Article 5(1)(f) prohibits using AI to infer the emotions of people in the workplace, with exceptions for medical or safety reasons, and has applied since 2 February 2025. Scoring rep emotion is therefore a high-risk feature to review with counsel before enabling it.

What data does an assist tool store?

That depends on the vendor. Typical stores include audio, transcripts, suggestions, and summaries. Ask where each is held, for how long, who can read it, and whether it is used for model training.

Insist on redaction of card numbers and other sensitive details.

How do I stop it from giving wrong advice?

Ground it in your own current articles, require a visible source for every suggestion, and keep reps trained to verify. Review ignored and corrected suggestions weekly. The better your knowledge base, the better the suggestions, and a stale article produces a stale answer.

How long should a pilot run?

Four to six weeks is a reasonable minimum. That gives time for reps to learn the tool, for novelty to fade, and for you to score enough calls per rep. Use matched groups and a fixed rubric, and write down your stop rule beforehand.

What should I measure in a pilot?

Track handle time, after-call work time, quality score, repeat-contact rate, and the share of suggestions accepted, edited, or wrong. Add rep feedback. Do not judge on speed alone, since faster calls with more errors are a worse outcome.

Will agent assist replace reps?

It is designed to make reps more effective, not remove them, and the main public evidence shows productivity gains for the same workforce. A customer-facing bot is the category aimed at removing volume. Whether either changes headcount is a business decision, not a product feature.

Is it worth it for a small team?

Often not at first. Seat pricing, integration work, and telephony setup add up, and a team of a few people may see more benefit from a better knowledge base and a chat agent. It becomes more attractive as headcount, turnover, and policy complexity grow.

What should I look for in a demo?

Ask the vendor to run on your own recorded calls and show the source article for each suggestion. Measure delay from the end of the customer's sentence to the suggestion. Check redaction, summary editing, and exports.

Be cautious of polished scripted demos.

Can I use agent assist for chat and email too?

Yes, most products support chat, and some support email. Chat is simpler because it needs no transcription. Amazon's documentation notes that Contact Lens is not required for chat, which lowers both cost and privacy burden compared with voice.

Does Communicate offer agent assist or voice?

No. Communicate does not answer phone calls or provide live-call coaching. It provides a customer-facing AI agent for chat and messaging, a shared inbox for human handoff, data sources, and actions.

If you need live-call assist, evaluate a contact-center platform.

What is the best first step toward agent assist?

Fix your knowledge base. Every assist tool retrieves from it, so unclear or outdated articles produce poor suggestions. Then record your baseline metrics, pick two matched groups of reps, and ask two vendors to run the same test on your calls.

How does assist relate to quality assurance?

They can share a rubric. Live assist nudges reps toward the right behavior during the call, and quality scoring checks it afterward. Using one definition of a good call across both avoids a situation where the tool and the scorecard push in different directions.

Can a bot and agent assist work together?

Yes. A bot can resolve simple conversations and hand the rest to a person with the context attached. The rep then uses assist on the harder case.

Design the handoff carefully so the customer does not repeat themselves, and keep a clear path to a human.