Agent Assist in AI: Real-World Use Cases That Make Work Easier

Imagine you're on a call with an upset customer. You're trying to pull up their account, recall return policies, and find the right words to calm them down.

Now, imagine a system quietly working alongside you, pulling up relevant information, suggesting next-best actions, and flagging compliance reminders, all in real time. That’s agent assist technology in action.

Unlike traditional chatbots or static scripts, agentic AI systems like agent assist uses **conversational AI to provide real-time, context-aware support during live interactions.

It listens, learns, and guides your agents in the moment, reducing resolution time, improving response quality, and helping your team handle even the trickiest queries with confidence.

Want to improve your customer service? In this blog, we’ll show how agent assist technology cuts resolution time, boosts customer happiness, and helps your support team handle tough problems with clear advice.

How Agent Assist Technology Works In 3 Steps

Agent assist technology might feel like magic to your support agents, but there’s real science behind how it works. It uses real-time interaction data to analyse conversations, recommend next-best actions, and continuously learn from outcomes.

According to IBM, agent assist tools can reduce average resolution time by up to 26%^[1], making your customer support faster, smarter, and more consistent.

Let’s unpack how it actually functions step by step.

1. Real-time analysis that understands customer intent as it happens

As an interaction unfolds, whether it’s over voice or chat, the agent assist system actively monitors the conversation. It processes keywords, emotional tone, and intent using advanced language models to understand the full context.

This real-time data analysis allows the system to anticipate customer needs, helping agents respond quickly without asking repetitive questions or searching for clues mid-conversation.

2. Intelligent recommendations that guide agents through live interactions

Once the system interprets the conversation, it instantly suggests next-best actions such as a templated reply, a help article, or a prompt to escalate.

These insights appear directly within the agent’s workspace, eliminating the need to toggle between tabs or dig through a knowledge base. It’s this just-in-time delivery of information that helps agents reduce resolution time and handle queries more confidently.

3. Continuous learning that makes every interaction smarter

Agent assist technology improves with use. It captures feedback from agents, tracks successful resolutions, and adapts its recommendations based on what consistently works.

Over time, the system becomes more accurate, more personalised, and better aligned with your team's workflows, turning it into a dynamic support partner that grows with your business.

Agent assist vs. chatbots vs. autonomous AI

Agent assist, chatbots, and autonomous AI are often lumped together, but they’re not interchangeable. Each plays a very different role in customer support and operations.

While they may all fall under the AI umbrella, the level of autonomy, complexity of tasks handled, and human involvement vary significantly. If you’ve ever found yourself wondering which is which (or when to use what), this breakdown clears it up:

If you're unsure which AI layer your support team needs, GrowthJockey helps enterprise leaders scope, test, and launch the right kind of solution.

Real-world use cases of agent assist technology in action

Sure, agent assist technology sounds cool on paper. But what does it actually do when the pressure’s on? When customers are waiting, agents are overwhelmed, and the queue isn’t getting any shorter?

Turns out, quite a lot. From slashing resolution time to helping agents focus on real conversations (not just clicking around), here’s how two very different companies made agent assist work for them.

1. Alibaba improves resolution speed and satisfaction with agent assist

When you’re handling over 200,000 support queries a day, even a few seconds saved per interaction adds up. That’s exactly what Alibaba^[2] aimed for when it introduced ICS-Assist, a tool designed to guide agents in real time with smart, situation-aware suggestions.

Instead of scrolling through massive knowledge bases, agents got instant prompts tailored to each customer’s issue.

This led to a 16% jump in customer satisfaction, a 25% boost in coverage, and fewer repeat queries thanks to faster, more accurate responses. For a company at that scale, those small improvements delivered a massive impact.

2. Verizon boosts sales and cuts call time using Google‑powered agent assist

Support calls used to be a cost centre for Verizon^[3]. But with the rollout of a Google-powered agent assist tool across its 28,000-strong service team, that changed fast.

The system draws from 15,000+ internal documents to recommend not just solutions, but upsell opportunities and smarter responses, live during the call.

Call times dropped. Agent ramp-up time got shorter. And perhaps most impressively, Verizon saw a nearly 40% increase in sales conversions. With agents spending less time digging for answers, they had more time to actually connect and convert.

How modern agent assist systems learn from human agents

Most people assume agent assist tools are only as good as the scripts they follow. But the best systems today learn by observing real conversations, especially the nuanced, human parts that can’t be templated.

Here’s how that learning happens on the floor:

• It watches what works: The system analyses thousands of past conversations to find patterns. What kind of response led to a resolved issue? What phrasing got a “thank you” instead of a frustrated sigh? This helps the tool fine-tune its suggestions to actually match what real people respond to.

• It listens when agents push back: Every correction, skipped suggestion, or edit is a quiet signal. Maybe the answer was outdated, maybe it lacked empathy. These moments feed back into the system, helping it refine what to surface the next time a similar issue comes up.

• It pays attention to tone. Some queries aren’t just “tech issue, send fix.” They’re full of frustration, confusion, or even panic. Advanced conversational AI can now detect emotion, pacing, and stress levels to offer better support in sensitive situations (think billing disputes or cancellations).

• It learns from the smart detours: Say an agent swaps “As per policy…” with “Let me make this right for you,” and the customer responds better. The system picks that up and adjusts future suggestions.

• It starts sounding more like your brand: As the system picks up on your agents’ language and style, its suggestions begin to reflect the tone your customers already trust, whether that's sharp and direct, warm and empathetic, or something in between.

So no, agent assist isn’t just an AI whispering answers in your ear. It’s a tool that’s learning your style, watching your best people in action, and slowly becoming the kind of teammate you’d actually want on your support floor.

How to design agent assist prompts effectively in 4 steps

Even the smartest agent assist technology is only as good as the prompts that power it. If the guidance it gives is vague, outdated, or poorly phrased, your agents will ignore it, and the system stops being useful.

Well-crafted prompts help reduce resolution time, improve agent confidence, and deliver more consistent customer experiences.

Here’s how to design effective prompts that actually support your team in the flow of work:

1. Understanding user intent

Begin by mapping out what customers are actually asking and what actions agents typically take in response. Agent assist performs best when prompts align with these tasks and needs.

For instance, if a customer says, “I still haven’t received my order,” a prompt like “Offer tracking link and check delivery status in CRM” is far more useful than a generic “Apologise for the delay.” Align prompts with intent, and your system becomes a true support partner, not a pop-up machine.

2. Contextual information integration

Great support isn’t one-size-fits-all, and your agent assist technology should reflect that. When a high-tier customer reports a billing issue, the system should factor in their plan type, past complaints, or any open tickets before suggesting a response.

Instead of prompting “Send billing link,” a more effective prompt might be: “Customer is on Premium Plan. Acknowledge priority status, then offer a billing breakdown.” Integrating customer context turns static suggestions into personal, high-impact help.

3. Response clarity and actionability

No agent wants to see vague prompts like “Be empathetic” in the middle of a tough call. Agent assist prompts should offer specific language or next steps that agents can actually use.

Think: “Say: I understand how frustrating this must be, let me check your last update,” not “Acknowledge frustration.” The more direct and usable the response, the faster agents can move, and the lower your resolution time drops.

4. Testing and refinement

Don’t dump 50 prompts into your system overnight. Start small, monitor usage, and let the data (and your agents) guide the process. If agents keep skipping a prompt like “Offer 10% discount” during delivery complaints, it may be irrelevant or badly timed.

Track which prompts speed up resolution time, which are edited often, and which lead to positive outcomes. Use this feedback loop to continuously refine your agent assist system so it evolves with your team and your customers

If this is starting to feel like a lot of theory and not enough traction, GrowthJockey can help you turn it into a working system, with real prompts, real agents, and real results.

What does agent assist mean for your business and how to build it right

Agent assist isn’t about bolting AI onto your support team and calling it a win. It’s a system that needs thoughtful prompt design, real-time context, and constant learning loops.

When those pieces click, it reduces resolution time, supports faster onboarding, and gives every agent the confidence to handle edge cases without second-guessing.

But designing prompts and plugging in automation is just one part. What actually drives impact is how the system evolves: are agents using the suggestions? Are prompts improving outcomes over time? Are edge cases being fed back into the loop?

That’s the part most teams skip and where we come in.

At GrowthJockey, we approach agent assist like a new venture. We embed them inside large teams, identify what’s broken in real workflows, and co-build systems that actually improve them.

Using our Intellsys.ai platform, we’ve helped enterprise teams go from half-baked pilots to fully adopted, high-impact AI solutions that scale.

If you’re serious about building agent assist that sticks, let’s do it the way real startups do: test fast, learn faster, and launch with proof.

FAQs on agent assist in AI

1. What is the feature of agent assist?

Agent assist gives agents real-time ideas, access to information, and guided answers to help them solve customer problems more quickly and correctly.

2. What does an agent assistant do?

An agent assist system monitors conversations, analyses context, and offers relevant information and response suggestions to support human agents during customer interactions.

3. What does agent assist mean?

Agent assist refers to AI technology that enhances human agent capabilities by providing intelligent recommendations and information during customer service conversations.

4. What are the features of AI agent?

AI agents offer autonomous decision-making, learning capabilities, natural language processing, and integration with business systems for comprehensive customer support.

1. resolution time by up to 26% - Link ↑
2. Alibaba - Link ↑
3. Verizon - Link ↑