AI Agent Assist: Real-Time Insights for Smarter CX

Leadership is putting the pressure on to cut costs. You’re expected to hit aggressive revenue targets, but you need to drive more sales with fewer agents. Now what?

You’re deep in the throes of researching ways to ingest AI, when your CEO chimes in… “We should use AI more for outreach.”

As if you’d never thought of that.

You don’t want to spill your budget on a full platform revamp, but need to find ways to make your existing agents faster and more thoughtful with outreach.

A great potential starting point? AI agent assist.

Here, we’ll breakdown AI agent assist—versus traditional tools, showing the benefits, how you can use it, and what to look for in a potential solution. Let’s get into it.

What is AI Agent Assist?

AI agent assist uses machine learning, large language models (LLMs), and natural language processing (NLP) to analyze ongoing customer interactions and deliver detailed insights, suggestions, and next steps directly to human agents (in real time).

AI agent assist serves as both a coach during live interactions, and as an assistant for each of your human agents pre- and post-interaction.

Industry Leader Thoughts on AI Agent Assist

Raj Mukherjee, an analyst at Gartner, notes, “AI Agent Assist is transforming customer support by enabling agents to deliver faster, more accurate service.” 
CX expert Susan Doyle stated, “The key to successful AI adoption is integrating tools that empower agents without overwhelming them.”

What Does AI Agent Assist Help With?

1. Live Call/Chat Guidance and Recommendations

Think of it this way… When your human agents are on the line with a contact, the AI assistant is also listening in, but never interacts directly with the contact.

Metaphorically, the AI agent is the other person in the room writing notes and holding them up for your human agents to act off of during their conversation.

For example, a customer may reach out with a technical issue—something that requires reconnaissance. AI agent assist can analyze the conversation as it happens and suggest the most relevant troubleshooting steps, product details, and developer documentation. 

All your human agent has to do is relay that to their contact.

2. Pre and Post-Interaction Tasks

AI agent assist does a lot around each interaction, as well. They supply human agents with details and prep needed before calls and chats—general and historical contact data, product usage, etc.

As interactions are completed, AI agent assist also summarizes calls, notes of needed follow-up, sentiment analysis, and more.

Automated Call Summarization

After every interaction, your AI assist will auto-generate call summaries highlighting:

  • Customer issues discussed
  • Whether those issues were resolved (and how)
  • Actions needed (and by whom)

This information is centrally logged, ready to be auto-retrieved later when this contact is reached by one of your agents.

Contextual Knowledge Base Integration

AI agent assist integrates with customer knowledge bases, ensuring that agents have access to the most up-to-date information without needing to switch between multiple systems or interfaces.

Whether you have a knowledge base built in Snowflake, Tableau, Segment—agent assist can tap in directly, serving as the bridge between your database and your agents.

Sentiment Analysis

AI-powered sentiment analysis helps agents understand and recall the emotional tone of customer interactions, without having to do any manual logging.

When they check back to do their follow up, this sentiment analysis will be there, ready to go.

How AI Agent Assist Differs from Traditional Tools

AI agent assist is different from traditional tools in that it:

  1. Automatically delivers the information to the agents—no searching or manual work required.
  2. Delivers information in real-time, serving as a proactive coach.
  3. Lives within existing agent desktops and workflows, not in a separate tool.

Traditional CX tools rely on more static workflows, and can have different data or workflows siloed across separate tools.

Benefits of AI Agent Assist

AI agent assist reduces response times, allows for more accurate answers, and enables agents to focus on solving more complex problems (whether that complexity is informational or emotional).

Faster Resolution Times

According to Hubspot, “62% of customers would rather hand out parking tickets than repeat themselves,” when interacting with the companies they purchase from.

Oh my.

This one speaks for itself. If you give your agents the information they need to act quickly and not re-ask questions:

  1. Their conversations will resolve much quicker.
  2. Your customers are going to love you for it.

Your agents will have contextually relevant information across historical communication logs, contact details, and anything that may arise in real time—product details, plan details, technical troubleshooting, and so on.

Enhanced Agent Productivity

Companies report up to a 30% increase in agent efficiency post-AI implementation (Performix Biz).

With more help from AI, the more time your human agents can spend on the tasks and customer interactions that drive higher value.

Contact data entry, call summarizations, follow-up scheduling—all of these menial tasks, gone, plus not having to manually sift through multiple tools to find information about contacts.

Improved Customer Satisfaction

Businesses see a 27% improvement in CSAT scores after deploying AI agent assist tools (Convin).

It’s simple math, here. If your agents have better information available to them, and can act much quicker, your customers get a better experience. Better customer experiences lead to higher CSAT and NPS scores, and more revenue converted.

Interactions become much more thoughtful. No needing to refill in context, or re-ask questions. Just a human being being human.

Cost Efficiency for Contact Centers

Long-term, this is the biggest payoff.

By streamlining workflows and reducing the time spent per interaction, companies can lower operating costs, while better allocating their resources to handle peak times.

Implementing AI agent assist across your team can help multiply the output of each of your agents without having to hire a single person.

Real Life Use Cases of AI Agent Assist

Aside from the basic day-to-day coverage, let’s break down some real life scenarios where you’ll see immediate relief from ingesting AI agent assist.

1. Handling High-Volume Customer Queries

AI Agent Assist is particularly useful during peak periods.

As queries flood in, your agents can hop on the line with full context and address questions head on. Once each call is wrapped up, they can go straight to their next contact, knowing that AI is taking care of the necessary follow-up.

On certain CX platforms—Regal, for example—your agents won’t have to leave their desktop platform to keep hammering through outreach.

2. Supporting New Agent Onboarding

Instead of taking additional people-power to train new agents, you can supplement onboarding with AI coaching.

This frees up extra hours for coaching agents on higher value prompts, messaging, and cross-channel personalization—instead of just how to get their numbers up.

3. Improving Compliance and Quality Assurance

In regulated industries like finance, insurance, or healthcare, compliance can slip through the cracks of a conversation time and again. It’s natural.

Real-time AI coaching helps ensure certain guardrails are never crossed.

A newly trained agent might not realize they’re crossing a HIPAA boundary, for example. With guardrails set up, AI agent assist will always know where the line lies—and inform your human agents (in real-time) as to not cross those lines.

AI agent assist serves as a large factor in fully automating your quality assurance processes.

Concerns You Might Have

1. Initial set up and training.

The question here is, are you trying to scale?

Yes, it might cost a couple extra dollars over the course of a few months to get automated workflows up and running. In the long run, your new operating efficiency will save you significantly more.

Modern, cloud-based CX tools don’t require much lift, if any, from your engineering team.

And, training is fairly easy since a lot of the work is being automated.

2. Data privacy and security.

The right tools will always offer you the ability to set guardrails as strictly as you’d like—for the sake of HIPAA, CCPA, or other compliance issues.

Furthermore, most, if not all of the LLMs and databases that AI agent assist tools are built upon are GDPR and SOC2 compliant.

If you have concerns, always ask your potential provider to confirm they’re enforcing the proper protocol.

AI Agent Assist Solutions: Top Considerations

Functionality and strength of service can range across different agent assist providers. There’s a few things to consider when choosing an AI agent assist solution:

  1. Level of integration: Is the agent assist siloed for individual human agents or centralized across your entire team? I.e. are you providing each human agent with their own outreach buddy, or can you centrally integrate the agent assist platform into your shared outreach hub? This is especially important for enterprises.
  2. Level of AI capabilities: Some tools may only have AI that supplements outreach tasks, without deeper analytical or workflow automation capabilities. Also: how customizable are the automation features available to you?
  3. Low/no-code: Centralized CX platforms can provide AI agent assist through low or no-code platforms accessed directly by your agents. Other tools may require investment from your engineering team to integrate and make continuous updates

Why AI Agent Assist Is Essential for Modern Contact Centers

AI agent assist might be the foot in the door (of AI) your organization needs to begin the buy-in process and really start seeing direct ROI and efficiency gains.

As you introduce more automation to your contact center team, you introduce more room for flexibility as your team scales—more automation means more ways to test, more levers you can push or pull to drive direct ROI and CSAT growth.

So, the next time your CEO starts “informing you” about the use of AI in contact centers, give them a lesson on AI agent assist and how it could be the start of your next internal CX revolution.

AI Agent Assist with an AI phone agent that never clocks out

Frequently Asked Questions

How does an AI assistant support agents during live customer conversations?

It analyzes live calls and chats with ML/LLMs/NLP and surfaces context, recommendations, and next steps to the human agent in real time. It does not interact with the customer directly; it acts as a behind-the-scenes coach.

What pre- and post-interaction tasks can this technology handle?

It preps agents with contact history and product usage before outreach, then auto-summarizes issues, resolutions, and follow-ups after interactions. It can also log sentiment analysis to inform future engagement.

In what key ways does this differ from traditional customer service tools?

It proactively pushes relevant information to agents in real time within their existing desktop workflow, reducing manual searching. Traditional tools are more static and often silo data across separate systems.

What measurable impact can organizations expect from implementing it?

Teams commonly see higher productivity and lower operating costs by streamlining workflows and reducing time per interaction. Reports in the article cite up to a 30% increase in agent efficiency after AI adoption.

What should teams evaluate when choosing an AI agent assist solution?

Check whether it supports centralized, team-wide integration; the depth and customizability of its AI and automation; and whether it offers low/no-code setup versus requiring ongoing engineering effort.

Founded in 2020, Regal is an enterprise voice AI agent platform for contact centers. Regal helps businesses build, deploy, and manage autonomous AI agents across sales, support, and operations teams.

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It’s the holiday season and you’d like to hit your December revenue goals. Outbound B2C sales during the holidays can be a major performance driver – if you're prepared and you have the right tools in place. Use these seven tips to get your outbound B2C sales in shape for holiday success.

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Regal.io Raises $38.5M in Series A Funding

Today we are excited to announce that based on the success our customers are seeing using Regal.io, we have raised $38.5 million in Series A funding led by Emergence Capital to continue to invest in our teams and products.

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Announcing our Integration with mParticle

Our integration with mParticle enables you to create a unified customer profile and connect other first party customer data.

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What is a Journey Builder?: A Complete Guide

Say goodbye to batch processing, complicated SQL queries or custom engineering to decide who to call. Journey builders help B2C create more timely, relevant and personalized outreach to customers.

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Goodbye Regal Voice, Hello Regal.io

Friends and supporters, We are excited to announce our updated branding and website. It better captures what we stand for.

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Announcing our Integration with Twilio Segment

We are excited to announce our integration with Twilio Segment, allowing you to seamlessly connect your first-party customer data with Regal.io and 200+ other integrated tools.

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Milestone: $1 Billion Revenue Driven for Our Customers

We are thrilled to announce that Regal.io has driven over 20M conversations between our customers and their end users, leading to $1 billion in revenue driven to date.

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The RAG Playbook: Structuring Scalable Knowledge Bases for Reliable AI Agents

Learn how to structure a knowledge base that keeps AI Agents accurate and on-script. Discover why human-style KBs fail, and apply best practices like single-topic chunking, concrete instructions, and explicit outcomes to reduce hallucinations and scale reliable RAG performance.

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How to Use SIP to Integrate Regal Voice AI Agents with your Contact Center Software

Learn how SIP integration enables enterprises to connect Regal Voice AI Agents to existing CCaaS platforms, pass call context in real time, and deploy voice AI without infrastructure changes.

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Demystifying AMD: How Answering Machine Detection Really Works

Regal's advanced AMD algorithm uses multiple factors to determine if a human answers your call. Learn more about the different techniques available.

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February 2026 Releases

In February, we made AI agents sound and feel more natural, even when handling complex, multi-step conversations with conditional logic. These updates give you the control to build AI agents that don't just follow a script: they adapt, respond, and engage naturally with each customer. 

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The Future of Voice AI in 2026: What the Data Is Telling Us

The Regal team discusses new research on where Voice AI agents are headed, and why this transformation is far bigger than just cutting costs.

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