What are AI agents? An all-in-one guide [Definition, process,benefits]

There’s no escaping the AI hype these days. It pervades the air we breathe.

AI is already in chatbots, recommendation engines, voice assistants, you name it. But when it comes to customer service?

Support agents are still manually grappling with irate customers firing off tickets. Teams are unable to keep up with the flood of incoming tickets.

The problem isn’t just that customers demand faster support—it’s that they expect smarter support. They want solutions now, and they want them personalized.

Unfortunately, most companies are still playing catch-up, relying on legacy support models that simply can’t keep pace. It’s a recipe for churn.

AI has the capability to change all this, but only if we start using it to its full potential.

And what does full potential look like? Predicting problems before they happen. Scaling support in ways human teams never could. Providing seamless, personalized customer service that not only solves issues but keeps customers loyal for the long haul.

That’s the promise that AI agents hold.

This guide will walk you through what AI agents are, how they work, and why they’re becoming a critical part of the future for AI in customer service. Let’s dive in.

What are AI agents?

AI agents are software programs that use artificial intelligence to process and act on customer inquiries without human intervention. Unlike simple AI chatbots that follow scripted rules, AI agents can understand context and process vast amounts of data to make decisions for accomplishing predefined goals.

This isn’t the stuff of the future—it is here and now. In fact, Gartner predicts that by 2028, one-third of interactions with GenAI services will invoke action models and autonomous agents for task completion.

So, are AI agents here to replace human agents? The answer, like many answers in life, lies somewhere between yes and no.

For instance, an AI agent can handle 1000s of inquiries any time of the day (or night) and ensure personalized responses for each. It’s a task human agents simply can’t scale to handle efficiently.

But, AI agents aren’t equipped to replace the human elements that are intrinsic to customer support. When it comes to situations that require emotional intelligence and nuanced judgment, like dealing with difficult customers, human agents are indispensable.

Instead of thinking of AI agents as replacements, consider them force multipliers. Together, they form a tour de force: AI for efficiency, humans for depth.

So, it’s not AI vs. human—it’s AI + human.

How do AI agents work?

AI agents work by combining artificial intelligence technologies such as natural language processing (NLP) machine learning (ML), large language models (LLMs), and data analysis into a seamless process to operate autonomously, learn continuously and adapt to ever-changing environments.

Here’s a detailed breakdown of how they operate:

1) Goal initialization: Setting the objective

Every AI agent starts with goals set by human programmers and leaders to guide the AI agent’s entire decision-making process. These goals could be as simple as answering customer questions or as complex as optimizing a supply chain.

Example: The AI agent might be programmed to decrease response times in customer support by 50% or flag potential billing issues before they occur.

2) Perception: Gathering information from environment

AI agents gather real-time data, whether it’s customer inquiries, website activity, or system performance metrics. Every click, every question, every issue reported becomes valuable input.

Example: If a customer asks, “Why is our data not syncing?” the AI agent instantly perceives this question and understands it as a potential system issue requiring deeper exploration.

3) Data processing: Making sense of inputs

Once the AI agent has perceived its environment, it processes that information using NLP to understand the intent behind the input. This might involve the agent processing customer complaints, identifying frequent issues, and even understanding the tone of the conversation.

Example: Going back to the previous example, if the customer asks a query like “data not syncing,” the AI agent grasps the context of a failed system integration and what actions might resolve it.

4) Decision-making: Selecting the best action

Once the AI agent has all the necessary data, it engages its decision-making capabilities. Based on predefined rules, learned experiences, and probabilistic models, it decides the best course of action. If a solution worked before, they are likely to prioritize it again, but if new data suggests a better approach, they can pivot.

Example: If the AI agent recognizes that the syncing issue stems from exceeding an API limit, it could decide to guide the user through resolving the problem or escalate it to a human if the situation requires deeper troubleshooting.

5) Executing selected action: Taking the necessary steps

Once a decision is made, the AI agent acts. Action could mean providing a solution to a customer, automating a task, or adjusting a process within a system.

Example: In customer service, this might be generating a response like, “Your data sync failed due to exceeding your API call limit. Would you like to increase your API quota or adjust your sync frequency?” In a more technical environment, an AI agent managing a cloud infrastructure might automatically adjust server resources to handle increased traffic based on real-time monitoring.

6) Feedback loop: Learning from outcomes

Continuous learning is what makes AI agents shine. Every time they take an action, they assess the outcome. Did the issue get resolved successfully? Was the customer satisfied? Did a particular solution outperform others? This feedback is critical because it allows the AI agent to improve its decision-making over time.

Example: If a customer repeatedly encounters the same issue, the AI agent not only remembers the customer’s previous interactions but adjusts its approach to provide faster, more efficient resolutions in the future. This learning happens in real-time, allowing AI agents to become more intelligent with every use.

7) Adaptation and refinement: Becoming more efficient over time

AI agents are adaptive—they evolve over time. As they gather more data and process more feedback, they refine their strategies. This is particularly powerful in complex, dynamic environments like customer support, where customer needs and behaviors change over time.

Example: An AI agent deployed for customer success might notice that customers in a certain industry are constantly asking for a specific feature. The agent can adjust its responses, preemptively guiding future customers through that feature to reduce future tickets.

Types of AI agents

AI agents are categorized into 5 key types, namely simple reflex agents, model-based agents, goal-based agents, utility-based agents, and learning agents. These agents differ in how they perceive their environment, adapt to changes, and take action.

Each type is designed to tackle different problems, ranging from simple, predefined tasks to complex decision-making processes. Let’s see each of these types of AI agents in detail:

1) Simple reflex agents:

Simple reflex agents are the most basic form of AI. They operate on a straightforward “condition-action” rule set, responding directly to the current situation based on what they perceive. They don’t have memory or the ability to learn, meaning they can’t make decisions based on past experiences or anticipate future states.

2) Model-based reflex agents:

These AI agents can make decisions based on what’s currently happening and what has happened before. By maintaining an internal state of the environment, they can infer information that may not be immediately visible, which is crucial in more complex scenarios where the full picture isn’t readily available.

3) Goal-based agents:

These AI agents aren’t simply reacting—they’re actively pursuing goals. They search for action sequences that reach their goal and plan these actions before acting on them. For instance, say a goal-based AI agent is given the goal of managing a customer’s onboarding process. If a new customer hasn’t completed the steps needed to fully integrate with the platform, the AI agent can proactively reach out, offering personalized tutorials or support.

4) Utility-based agents:

Utility-based agents not only pursue goals but also evaluate how desirable each outcome is. This is done through a utility function, which helps the agent measure how successful or beneficial each decision will be. When faced with multiple possible actions, a utility-based agent chooses the one that maximizes the outcome based on a calculated preference scale.

5) Learning agents:

These agents don’t rely solely on pre-programmed knowledge but adapt by learning from their experiences. They incorporate feedback mechanisms that allow them to refine their understanding of the environment and improve their performance.

5 key benefits of AI agents

AI agents can help you increase customer satisfaction and lower your churn rate by improving workflows across teams in your organization. Whether you’re a customer support professional, software developer, sales rep, or product manager, AI agents can help you execute your tasks better.

Here are some problems that members from different teams face:

• Support Managers scramble to diagnose the root causes of service level agreement (SLA) breaches.
• Support Agents face long, tedious onboarding processes.
• Developers lose precious time sifting through mountains of documentation to start a new product enhancement.
• Sales Reps often feel unprepared for demo calls, and
• Product Managers struggle to prioritize issues when planning sprints.

These challenges are real. They slow you down. They frustrate your team. But with AI agents, the game changes entirely. Here’s how:

1) AI agents improve operational efficiency

AI agents have the ability to analyze enormous amounts of data in real time, helping you drive business efficiency. They can instantly surface patterns—flagging recurring issues, performance bottlenecks, or even individual customer complaints that might be driving the SLA breach.

Let’s say an API outage is causing delays across the board. Instead of manually piecing together this puzzle, the AI agent scans through the ticket backlog and identifies the specific technical failures that caused the delay, delivering a detailed report to your dashboard.

Not only does the AI agent find the problem faster, but it also proactively suggests solutions, allowing the Support Manager to take immediate corrective action for the SLA breach and even provide predictive support to customers.

2) AI agents enhance onboarding processes

Onboarding a new Support Agent requires lengthy documentation reviews, shadowing senior agents, and spending days or even weeks learning the nuances of the company’s processes. All this time spent learning could be better spent solving customer problems, right?

Here’s where AI agents step in with instant, real-time guidance. Instead of sifting through training manuals, new agents can ask the AI: “How do I escalate a ticket in this situation?” With its genAI search capabilities, AI agent not only provides the correct protocol, but it also explains why that protocol is in place.

3) AI agents accelerate development workflows

For a Developer starting work on a new product enhancement, it often feels like you’re dropped into an endless sea of data—bug reports, customer feedback, previous code iterations—it’s all a lot to take in. Finding the information you need to kickstart the project is time-consuming and, frankly, frustrating.

But AI agents can help developers cut through the clutter. Instead of searching through thousands of support tickets and customer reviews, your AI agent gathers the most relevant data for you. It identifies the top customer complaints regarding the feature, shows you related bug fixes, and even highlights the most common usage patterns. This empowers you to start coding faster and deliver improvements on time.

4) AI agents streamline sales preparation

For a Sales Rep, the stakes are always high in a second or third call with prospects. You’ve already hooked the prospect’s interest, but now you need to drill down into their specific pain points and demonstrate exactly how your product can solve their problems.

But preparing for these calls by poring over CRM data, past emails, or demo call notes, especially when you have a packed schedule, can be overwhelming.

But AI agents can automatically compile all relevant insights—previous interactions, product usage metrics, and even the prospect’s most viewed website pages. In an instant, you have a comprehensive overview of what matters most to your prospect.

The result? Your sales pitch becomes hyper-relevant to the customer’s needs, increasing your chances of closing the deal.

5) AI agents improve sprint prioritization

For a Product Manager, sprint planning is often the most challenging part of the job. You’re constantly balancing feature requests, bug fixes, and customer feedback, all while trying to make sure your team isn’t overloaded. It’s a balancing act, and without the right tools, it’s easy to feel like you’re spinning plates.

That’s where AI agents come in. An AI agent can analyze incoming tickets, customer requests, and team performance metrics, providing you with a data-backed roadmap for your sprint.

Let’s say a specific bug has been affecting multiple high-value customers. AI agents will automatically prioritize this issue, ensuring it gets fixed in the next sprint. On the other hand, if a new feature request is gaining momentum based on customer feedback, the AI will highlight it, helping you align your sprint goals with customer demand.

The future of customer service is agentic

Andrew Ng, renowned computer scientist and a pioneer in machine learning, had this to say about AI agents in a lecture: “The set of tasks that AI can do will expand dramatically because of agentic workflows.”

Given the promise it holds, organizations worldwide are eager to adopt AI agents. A Capgemini Research Institute study reveals that 82% of surveyed organizations intend to integrate them within 1–3 years.

So, it’s evident that our future work experience will be one where AI agents work autonomously on our behalf. AI agents will be the way by which modern organizations will support customers, build products, and drive revenue.

To enable this profound change, AI agents will require a platform that provides context by unifying data and information around products and customers and make workflow integrations seamless. And that platform is DevRev.

At the heart of DevRev is its robust knowledge graph, in which data from legacy systems is continuously being fed in real time to an interlinked network of customer, product, employee, work, user, and session records. This organization of structured and unstructured data connects the work you do to its relationship with your product and customers.

DevRev’s knowledge graph is the data layer powering its AgentOS platform, which connects humans and AI agents on one unified system.

Agents are armed with the data and tools they need to perform a wide range of tasks efficiently, and humans are provided with a modern application interface to both define the skills of their agents effortlessly and collaborate with them in real time.

The result? You can reduce customer ticket resolution times by up to 50% and boost agent productivity.

Ready to enter the future with DevRev’s AI Agents? Book a demo now!