January 30, 2026 • 11 min read
Agent Assist in 2026: A Definition, Use Cases, & Best Practices
Director of Content & Market Research
January 30, 2026
When generative AI burst into the enterprise, contact centers became ground zero.
Much of the AI innovation centered on automating customer contact, as organizations sensed the opportunity to cut costs.
However, AI solutions are also augmenting the human customer service agent role, enabling reps to respond to customer queries with more knowledge, skill, and efficiency.
These AI solutions fall under the umbrella term “Agent Assist”.
Statistics from the National Bureau of Economic Research (NBER) show that when customer service agents utilize agent-assist solutions, their productivity increases by an average of 14%.
However, that percentage will likely climb higher as contact center agent-assist use cases continue to mature.
What Is Agent Assist?
Agent-assist solutions deliver real-time support and guidance to contact center reps before, during, and after customer interactions.
Contact centers have leveraged agent-assist solutions since the late 1990s, when the introduction of Caller Line Identity (CLI) technology made “screen pop” possible.
Screen pop allowed contact centers to display customer details on the agent’s screen at the start of an interaction, eliminating the need to search through separate systems.
Since then, agent-assist technology has evolved considerably, with advancements in machine learning and generative AI unlocking exciting new use cases.
Today, CRM and CCaaS providers often package these together within an Agent Assist offering or inside a virtual assistant.
There are also specialized use cases offered by voice AI and workforce engagement management (WEM) vendors, which are explored - alongside the core use cases - below.
10 Agent Assist Use Cases
The evolution of large language models (LLMs) has brought many new agent-assist use cases to contact centers. Here are ten examples.
1. AI Replies
Across digital channels, AI can draft personalized customer responses based on knowledge base content, best practices guides, and data stored within the CRM.
From there, agents may review replies for accuracy and tone, make edits, and send the response to the customer.
The risk is that agents may spend more time editing the suggested reply than they would writing it themselves, or worse, send it without adequate review.
For this reason, investing in knowledge management and engaging agents throughout the deployment are essential best practices.
2. AI Information Gathering
After a customer signals their contact reason in the IVR, AI can interact with them to collect information pertinent to their intent and pass that through to the rep at the start of an interaction.
As a result, the rep doesn’t have to hold the line as the customer scurries around searching for crucial information during the interaction. Instead, that information is on the screen in front of them from the get-go.
“I recently saw a great example where the AI handled identification and verification, gathered context, and then passed a clean, structured summary to the agent. That’s real harmonization between AI and humans.”
3. AI Note-Taking
AI note-taking applications log key points raised by the customer during a conversation, which agents can refer back to as they try to resolve the query.
As agents don’t need to take notes themselves, they can actively listen to customers without fear of missing vital information.
The next evolution is note-takers that don’t just capture content but take action, completing actions and follow-up tasks automatically. That’s an exciting prospect.
“We’re moving toward a world where Agent Assist handles data capture in real time, prompting advisors to confirm details like email addresses and automatically recording them. Eventually, advisors won’t need traditional CRM data-entry fields at all.”
4. AI Guidance
AI can track the flow of a live customer conversation and consult the knowledge base to provide next-best-action prompts or reminders, like: “Don’t forget to say this!”
While this use case can add value, contact centers must ensure that guidance cards don’t parrot every step of a resolution flow and become noise. Instead, they must be iterative and intentional to make sure they genuinely add value.
That usually starts with a quality monitoring initiative that considers: where are agents slipping up? Is it policy understanding, contract terms, or product knowledge? Pop-ups should target those gaps.
Lastly, AI guidance can flag live revenue opportunities, surfacing additional product information to help agents secure the sale.
5. AI Summaries
After a customer interaction, contact center agents typically log a contact summary in the CRM system, tag it with a disposition code, and run follow-up tasks, like sending a confirmation email.
Agent-assist solutions can help automate these after call work (ACW) processes, with auto-summarization - or “AI summaries” - now widely deployed.
These summaries work by transcribing the conversation and parsing it through an LLM with a custom summarization prompt, so each summary is uniform in structure.
“It’s almost table stakes now and generally well adopted by advisors,” said Corfield.
6. AI Transfers
When a call is transferred, AI can generate a tailored summary of what’s been discussed and where the conversation stalled, allowing the next agent to pick up seamlessly.
This capability isn’t limited to human-to-human handoffs. When an AI agent interaction breaks down, the same warm escalation can provide live agents with full context.
Agents may also receive additional signals, such as the customer’s sentiment at the point of transfer, so they can re-engage with the right level of empathy.
7. AI Coaching
AI coaching tools evaluate a customer interaction and share feedback shortly after when an agent didn’t follow a best practice or missed a customer cue.
Yet, these tools aren’t only about seeking improvement opportunities; they can also commend excellent interactions and highlight positive patterns in agent performance. That real-time recognition can help boost morale.
AI coaching is the next evolution from sharing sentiment analysis insights with reps, which is an agent-assist use case that had mixed success. After all, agents already know if an interaction didn’t go to plan, and having that relayed back to them without context isn’t helpful.
Instead, sentiment analysis becomes useful when applied retrospectively to understand patterns, such as where reps diffuse tension versus where they escalate it.
8. AI Role Plays
Agents can engage in simulated conversations with AI customers in a safe setting to improve their service skills.
For instance, they may replay a difficult interaction and practice alternative ways to handle it. Alternatively, they may simulate scenarios around a new product launch, so agents can build confidence before speaking with customers.
Comfort and success come from repetition; it becomes muscle memory. If you can create that muscle memory without putting agents in front of live customers, they’re far more confident when it counts.
Platforms like AmplifAI and Reddy.io already offer this software, with companies such as Morgan & Morgan utilizing simulation and coaching capabilities.
Soon, this use case will become more mainstream, especially as more prominent workforce engagement management (WEM) providers offer this tech. That’s starting to happen, with Centrical releasing AI simulations.
9. AI Accent Modification
Alongside the traditional agent-assist use cases, voice AI providers - including Krisp, Sanas, and Tomato.ai - have deployed technology to alter the accents of agents.
In doing so, their AI either “translates” agents’ voices into a generic American accent or “softens” their natural accent.
The use case raises ethical questions: are you erasing someone’s identity, or enabling clearer communication by choice?
Nonetheless, prominent global BPOs, including Everise, Teleperformance, and Transcom, have implemented the technology.
Teleperformance even claims that the use case helped one of its consumer electronics clients increase its Net Promoter Score (NPS) by 26%.
10. AI Live Translation
AI translation solutions enable agents and customers to communicate, even when they speak different languages.
Historically, these tools relied on speech-to-text transcription, conventional translation engines (such as Google Translate), and text-to-speech to deliver responses in the customer’s native language. However, noticeable delays often made the experience feel awkward, and limited adoption.
Today, many of the same providers developing accent modification technologies are introducing near-real-time AI translation tools purpose-built for contact centers. Check out the example below.
This progress points to a future where global organizations can extend 24-hour support by enabling teams in different countries to assist customers beyond traditional 9-to-5 hours.
Best Practices for Deploying Agent Assist
Each agent-assist use case brings its unique requirements. Nevertheless, the following best practices are worth considering when deploying any version of the technology.
Start with the Outcomes You Want to Achieve
Leaders should choose agent-assist use cases based on where they’re struggling most and where the potential impact is greatest.
For instance, organizations aiming to improve sales conversion should prioritize Agent Assist for sales. Those aiming to build smarter, more agile employees should focus on training and live guidance.
There’s no one-size-fits-all approach; success depends on clearly defining the problem and the business impact to be achieved.
Involve Agents Early
Agent Assist is an agent-facing technology, so the frontline team needs to be involved early. A rollout should never be the first time they encounter the tool, yet too often, it is.
When this happens, agents can feel overwhelmed by a constant stream of prompts, which is especially challenging when they’re already listening, speaking, reading, and processing information simultaneously.
That’s why early involvement is critical. Engaging agents in defining use cases, managing knowledge, and testing pilots helps secure buy-in and significantly increases the likelihood of a successful rollout.
Track User Adoption of Agent Assist
Tracking the adoption of agent-assist tools is critical. Before worrying about revenue or efficiency gains, service leaders need to understand how people are using the tool.
Are they ignoring assistance, following it blindly, or interacting with the output? Supervisors need to know, so they can ensure their teams are leveraging the solution as intended.
Applications like KYP.ai can help by tracking AI usage, tasks, and productivity differences across users. That opens the door to better learning, adoption, and ROI.
Over time, however, CCaaS and CRM vendors may embed such reporting natively within their platforms, making such insights easier to access and act on.
“I’ve been speaking with vendors about embedding adoption metrics into auto-summarization, so leaders can see who’s using it as-is, who’s heavily editing it, and who’s simply submitting it without changes. That visibility matters because when you speak to advisors, you often find some are rewriting the AI summary almost entirely. At that point, you’re eroding the time savings it’s meant to deliver.”
Establish Communication & Feedback Loops
Alongside tracking user adoption, strong communication and feedback loops help add context to how agent-assist tools are impacting the employee experience. Establishing super-user groups is an excellent tactic here.
After all, reps need a way to pinpoint what’s working and what isn’t, and that feedback must flow back not only to supervisors but also to the vendor’s product team.
Don’t Underestimate Knowledge Management
Contact centers consistently underestimate knowledge management. Every Agent Assist deployment creates challenges in knowledge disparities, documentation cleanup, and iteration.
There are now AI solutions that can scan successful conversation transcripts and spin up new knowledge content.
However, there’s no easy button or fast path. Service leaders need people who are willing to review, test, and refine that content and continuously feed the system.
Agent Assist forces organizations to clean up their knowledge assets and align them with digital content. That work can be significant - breaking down long PDFs into bite-sized, usable content - but it’s necessary. The starting point should always be your scorecards: what knowledge are advisors expected to have, and where are the gaps?
Adapt Your Quality Assurance Strategy with Agent Assist
Agent Assist presents an opportunity to rethink what service quality means.
For instance, if Agent Assist is providing knowledge in real time, should analysts still score agents on recalling facts? Or should they focus more on confidence, clarity, and soft skills?
In this sense, quality frameworks need to evolve alongside Agent Assist. “And one thing I strongly advise against is pushing QA scores straight to advisors without context,” added Corfield. “That’s rarely productive.”
Expect Agent Assist to Be a Hands-On Initiative
Timelines for time-to-value are often too optimistic. Contact centers should bake in more time than they need, make iterations, and they’ll achieve value over time.
“Many organizations aren’t failing because the technology doesn’t work; it’s because it’s taking longer than expected.”
How to Choose an Agent Assist Provider
Almost every CCaaS and CRM provider now offers an agent-assist portfolio. While some specialized use cases, like AI accent modification, still require a niche vendor, the agent-assist offerings from both types of providers are expanding rapidly.
That leaves contact centers to leverage one agent-assist portfolio or another. The decision will likely come down to which solution they wish to use as their primary interface for customer service.
In 2025, 47.9% of organizations relied on their contact center platform as the primary agent interface, compared with 43.2% using the CRM.
Metrigy research predicts this will shift by the end of 2027, with only 32.4% still using the contact center and 53.4% adopting the CRM as the main interface.
Growing demand for deeper front- and back-office integrations is driving this trend, and a likely consequence is that more organizations will adopt the agent-assist solutions built into their CRM, helping agents avoid constant system hopping.