Agent assist AI is software that works alongside a human customer service agent, suggesting draft replies, condensing a case's history into a few lines and finding the right procedure or product answer with its source. It is one of the most controlled ways to use AI in customer service: the agent, not the assistant, talks to the customer and makes the final call. Speed improves while judgement and accountability stay with people.
Key takeaways
- Agent assist never talks to the customer; it gives a human agent draft replies, case summaries and sourced answers, and the agent decides.
- In an NBER study of 5,179 support agents, a generative AI assistant raised issues resolved per hour by 14% on average and 34% for novices.
- The assistant's four core jobs are suggested replies, case summaries, sourced knowledge search and after-call CRM updates.
- Rollout takes six steps: map case types, consolidate knowledge, connect systems, require human approval, limit personal data and run a measured pilot.
- Success is measured before and after the pilot through handling time per case, repeat contact rate and the share of suggestions used unchanged.
What actually happens on an agent's screen?
Picture the support desk of a B2B manufacturer. An agent opens an email: the customer is writing for the third time about the same delivery problem, the earlier thread sits in a colleague's inbox, the warranty clause is on page 14 of a PDF and the order details live in the ERP. Before writing a single line, the agent has to visit four screens and piece the story together.
A seasoned agent does this from memory; a new starter interrupts a colleague or sends an incomplete answer. The problem is rarely missing knowledge but scattered knowledge: the answer exists, just not in front of the person who needs it.
This guide covers the assistant that strengthens the agent, not the bot that replaces it. For answers without an agent, read our customer service chatbot guide; for reviewing recorded calls at scale, see our piece on call centre speech analytics.
How much does agent assist AI improve productivity?
One of the most detailed field studies of agent assist was published in 2023 by the US National Bureau of Economic Research (NBER) and run inside a real support operation:
In a study of 5,179 customer support agents, access to a generative AI assistant increased issues resolved per hour by 14% on average, and by 34% for novice and low-skilled agents, with minimal impact on experienced, highly skilled staff. — NBER, Brynjolfsson, Li and Raymond, "Generative AI at Work"
According to the NBER study, the tool also improved customer sentiment and employee retention. The lesson: agent assist carries your best agents' know-how to your newest ones, instead of leaving it in a few experienced heads.
Repeat contact deserves its own line in the agent assist business case. According to Microsoft's Telstra customer story, the Australian telecoms company built a tool that condenses a customer's history into one sentence for contact-centre agents. The company says 90% of agents who tried it reported saving time and working more effectively, and those calls needed 20% less follow-up contact. These were results from a pilot of around 100 agents.
Expectations for AI investment should stay realistic. The Stanford HAI AI Index Report 2025, citing McKinsey data, reports that 49% of organisations using AI in service operations saw cost savings, but most of them put those savings below 10%. Value comes from placing the assistant in the right step of the workflow.
AI in customer service: agent assist AI versus chatbots and speech analytics
AI in customer service usually takes one of three forms, and even on the customer-facing side a chatbot and an AI customer agent are not the same thing. Which one you start with depends on where the bottleneck sits:
| Criterion | Chatbot (self-service) | Agent assist AI | Speech analytics |
|---|---|---|---|
| Who talks to the customer? | The bot | A human agent | A human agent |
| When does it work? | During the conversation, instead of the agent | During the conversation, beside the agent | After the call ends |
| Best suited to | Repetitive questions answered in documents | Cases needing judgement, flexibility or history | Quality assurance, coaching, trend analysis |
| Risk of a wrong answer | Reaches the customer directly | Filtered by the agent before sending | Stays at report level |
| Main metric | Self-service resolution rate | Handling time per case, repeat contact | Quality score, complaint drivers |
The biggest advantage of agent assist is its risk profile: every suggestion passes through a person, so agent assist can be used on complaints and key account correspondence you would never hand to a bot. How a live chat team and AI can work together on a website is covered in live chat vs AI assistant.
What four jobs does an agent assistant do?
Suggested replies. Using a large language model (LLM), the agent assistant reads the incoming message, the customer's history and the relevant procedure, then drafts a reply. The agent edits it, adjusts the tone and sends it. Drafts must be grounded in your own documents; we explain how in our article on RAG for enterprise LLMs.
Case summaries. A long email thread or a transferred case is reduced to a few lines answering what the customer wants, what has been tried and what they are waiting for. When a case changes hands, the customer does not have to start again.
Knowledge search. The agent asks in plain language, "Which situations void the warranty on this product?", and the assistant returns the answer with its source. The agent must be able to check the source before passing it on. The wider problem of scattered information is covered in our guide to AI enterprise search.
After-call work. Once the case closes, the assistant categorises it, writes the wrap-up note and fills in the fields to be saved to the CRM. Records become more consistent too.
How do you roll out agent assist? Six steps
- Map your case types. From the last three months of tickets, list the 15–20 most common case types and the step in each where agents lose the most time.
- Consolidate your knowledge. Procedures, product documents, non-price commercial terms and model answers should live in one current source. Two conflicting documents mean two conflicting suggestions.
- Connect your systems. For the assistant to see orders, invoices and past tickets, it needs API integration with your CRM, ERP and email platform. Why a single customer history matters is covered in our CRM and sales pipeline guide.
- Make human approval a rule. No suggestion should reach a customer without an agent's approval, and answers without a visible source should not be used. For other ways to cut fabricated answers, see reducing AI hallucinations.
- Limit personal data. Support correspondence contains names, addresses and order details. In Turkey this falls under KVKK, the Personal Data Protection Law, which works much like the GDPR; decide early what the assistant may access, where data is processed and how long it is kept. We summarise the legal framework in AI and KVKK.
- Measure with a small pilot. Start with one team and a handful of case types. Compare handling time per case, repeat contact rate and the share of suggestions used unchanged against your pre-pilot baseline; our method for measuring AI project ROI helps with the numbers.
Watch for one more thing during an agent assist pilot: agents trusting suggestions blindly. In a 2023 Harvard–BCG experiment with consultants (Harvard Business School working paper "Navigating the Jagged Technological Frontier"), those using AI on a task outside its capabilities were 19 percentage points less likely to reach the correct answer. Training should spell out when a suggestion needs to be challenged.
How we deliver agent assist at Digital Bridge
We do not sell agent assist as an off-the-shelf package. During discovery we map your support channels, case types, where knowledge lives and where agents lose time, then prepare a written proposal covering scope, phases and cost.
We build the core of the assistant the same way as our enterprise LLM assistant projects: grounded in your own documents and always citing its source. Through AI integration we place it inside the screen your agents already use, whether that is the CRM, the helpdesk or the email panel, so nobody has to learn a new application.
The pilot runs with one team and a limited set of case types, with success measures agreed up front. At the end we report plainly where it helped and where it did not. Once agent assist is working, the same knowledge source can power an NLP chatbot for the questions you are happy to hand straight to customers. To see where this fits in a wider AI roadmap, read where to start with AI in business.
Starting on the email help desk with Smart360
In many B2B firms, customer service is really a shared mailbox: support@, orders@ or info@. In that case your email platform can take on part of the job before you build an agent assistant from scratch. SmartMail (SmartMail.com.tr), part of Digital Bridge's Smart360 family, is, as of 2026, an AI-assisted business email service that manages shared and personal mailboxes in a single web panel.
A concrete scenario: a customer replies at the end of a long thread. The agent reads the SmartMail message summary, asks the message a question such as "Which order is the customer referring to?", and drafts the reply with writing assistance. Without any rules to write, incoming mail is sorted into content types such as first contact, invoices and payments, quotes and tenders, and official letters.
In SmartMail, a reply to an overseas customer can be translated into any of 30 languages, and the totals, due date and document number on an attached invoice PDF are read without downloading the file. We go deeper into drafting replies in AI email reply drafting and into sorting the inbox automatically in AI email classification and triage.
For hand-offs within a SmartMail team, the assignment board shows who a job has been given to, team notes and attention flags let agents leave each other notes on a case, and the action log on every message shows who did what. An agent writing a sensitive reply on the company's behalf can use "Send with approval" to submit it for sign-off before it goes out.
If procedures and product documents are stored in SmartFiles, the agent can ask questions of a single document or a whole folder. To buy SmartMail, use smart360.com.tr/satin-al?urun=smartmail. We cover the permissions side of shared inboxes in our guide to shared mailbox management.
If customer service also runs through website chat as well as email, SmartOperator, the AI customer agent in the same family, brings the agent-assist approach described here into the live support screen. For the team member who takes over a conversation, the AI prepares a draft reply from the conversation, the knowledge base and the catalogue; the draft is not sent to the visitor but lands in the reply box, with the points to check flagged. In SmartOperator, internal notes are visible only in the panel and never reach the visitor, and canned replies are picked in the message box with "/".
Your next step
Take 50 support cases closed in the last month and roughly mark how long the agent spent finding information, reading history and writing the reply in each. The step that eats the most time is your assistant's first job. Bring that list to us via our contact page and we will scope a pilot with you in a discovery call. More articles are on our artificial intelligence topic page.