AI agents are software systems that use artificial intelligence to understand a goal, decide how to reach it, and take action, often across several steps and systems, with limited human direction. Unlike a chatbot that answers questions from a script, an AI agent can interpret what someone is asking for, look up or update information in connected tools, and complete a task.
In business, AI agents are most visible in customer-facing work. An AI agent might answer a customer's call, confirm their identity, check an order, book an appointment, or pass the conversation to a person with a summary of what has already happened. Behind the scenes, AI agents can also help employees by drafting follow-ups, logging activity, and surfacing information during a conversation.
This guide explains how AI agents work, how they compare to chatbots and agentic AI, the main types of AI agents, and examples of how organizations use them today.
Dialpad Resolve
Customer-facing voice and digital AI agents that resolve, not just respond.
How do AI agents work?
Most AI agents follow the same basic loop: they take in information, decide what to do, act, and check the result before deciding what comes next.
Understanding the request
An AI agent starts by interpreting what it receives, whether that is a spoken request on a phone call, a chat message, or data from another system. Large language models help the agent understand natural language, so a customer can say "I need to move my appointment to next week" instead of navigating a menu.
Deciding what to do
Next, the agent works out how to reach the goal. That can mean asking a follow-up question, choosing which system to check, or breaking a request into steps. Businesses shape these decisions with instructions, knowledge sources, and guardrails that define what the agent should and should not do.
Taking action in connected systems
What separates an AI agent from a chatbot is the ability to act. Through integrations with tools like a CRM, scheduling system, help desk, or order database, an AI agent can look up records, update information, and complete transactions as part of the conversation.
Checking the result and handing off when needed
After each step, the agent evaluates whether the goal is met. If it is, the agent confirms the outcome. If the request falls outside what it is set up to handle, a well-designed agent hands the conversation to a person along with the context gathered so far.
A simple example
Here is how that loop might look when a customer calls about an order:
The customer explains they want to check on a recent order.
The AI agent verifies the customer's identity.
It looks up the customer's recent orders in the order system.
The customer confirms which order they mean.
The agent shares the current status and confirms nothing else is needed.
If the order turns out to be damaged or missing, the agent can route the conversation to a support specialist, who sees the full conversation instead of starting from scratch.
AI agents vs. chatbots: what's the difference?
The main difference between an AI agent and a chatbot is that a chatbot answers questions, while an AI agent works toward a goal and can take action to reach it. Traditional chatbots follow scripted flows or pull answers from a knowledge base. AI agents understand open-ended requests, keep track of context across a conversation, and use connected systems to get things done.
Chatbot | AI agent | |
|---|---|---|
Primary job | Answer questions | Complete tasks and resolve requests |
How it decides | Scripts, decision trees, or keyword matching | Interprets intent and plans next steps within guardrails |
Handling unexpected requests | Often stalls or transfers | Can ask follow-up questions and adapt |
Access to other systems | Limited | Looks up and updates information in connected tools |
Multi-step requests | Rarely | Yes, such as verify, look up, update, and confirm |
Channels | Usually web chat | Voice and digital channels |
Handoff to a person | Often without context | Can pass along a summary of the conversation |
Chatbots still have a place, especially for simple FAQs and intake. Many teams use both, with chatbots covering basic questions and AI agents handling requests that need understanding or action. You may also see the term virtual agent, which is often used for automated assistants that handle customer conversations.
AI agents vs. agentic AI
Agentic AI is the broader approach: AI designed to pursue goals, make decisions, and take action with limited human direction. AI agents are the systems that put agentic AI to work. Put simply, agentic AI describes the capability, and an AI agent is a specific application of it, such as an agent that answers customer calls or schedules appointments.
The distinction matters when evaluating vendors. A platform that describes itself as agentic should be able to show agents that complete tasks in real systems, not just generate text.
Types of AI agents
AI agents are usually grouped in two ways: by how they make decisions, which is how computer science classifies them, and by the job they do in a business.
Types of AI agents by how they make decisions
Type | How it decides | Business example |
|---|---|---|
Simple reflex agent | Responds to the current input using fixed rules | Sending an after-hours message when a call comes in outside business hours |
Model-based reflex agent | Uses rules plus a running picture of the situation | Remembering what a caller already shared so it doesn't ask twice |
Goal-based agent | Chooses actions that move toward a defined goal | Working through the steps needed to reschedule an appointment |
Utility-based agent | Weighs options to pick the best outcome | Offering the appointment slot that best fits the customer's stated preferences |
Learning agent | Improves its performance based on feedback and new data | Getting better at recognizing request types as its configuration is refined over time |
Most modern AI agents used in business combine several of these approaches. Some systems also coordinate several specialized agents, such as one that verifies identity and another that handles billing, which is often called a multi-agent system.
Types of AI agents by business function
Customer service AI agents handle common support requests like order status, account updates, and troubleshooting across voice and digital channels. Support is often where teams start with AI agents for customer service, because requests are frequent and outcomes are easy to measure.
AI voice agents answer and manage phone calls using speech recognition and natural language understanding. They can greet callers, understand why they are calling, complete tasks like scheduling, and transfer to a person when needed.
Sales AI agents can respond to inbound inquiries, ask qualifying questions, and book meetings with the right rep, so leads get a fast response even when the team is busy.
AI agents for employees support internal work, such as summarizing conversations, drafting follow-ups, logging activity in a CRM, or surfacing relevant information during a live call.
AI agent examples
AI agents look different in every industry, but the pattern is the same: a high-volume, repeatable request that once required a person can now be handled from start to finish, with a person available when the situation calls for one. Here are examples of how organizations can use AI agents.
Healthcare: scheduling new patient appointments
A patient calls or chats to find a new doctor. The AI agent collects their name, contact information, and insurance provider, checks the practice's calendar, and offers available times. It can book an appointment and send a confirmation without the front desk stepping in.
Retail and e-commerce: order status and cancellations
A customer asks to cancel an order. The AI agent verifies their identity, finds the order, and checks whether it has already shipped or entered production. If the order can still be canceled, the agent confirms and processes the cancellation. If not, it explains why and shares next steps.
Automotive: booking service appointments
A driver calls a dealership to schedule maintenance. The AI agent identifies the vehicle and the service needed, offers open time slots, and books the appointment, routing questions about repairs or pricing to the service team.
Real estate: answering listing questions and booking tours
A prospective buyer asks about homes in a specific neighborhood and price range. The AI agent searches current listings, shares options that fit, and schedules a tour with an agent, collecting pre-qualification details along the way.
Staffing: resolving payroll questions
A traveling nurse calls on a Friday because a paycheck looks wrong. The AI agent collects their name, phone number, and email, logs the details to the agency's applicant tracking system, and confirms that the issue has been recorded, so the payroll team can follow up with complete information.
Legal: client intake
A potential client contacts a law firm after hours. The AI agent asks about the type of matter, collects contact information, and routes the inquiry to the right practice area, so attorneys start the next day with organized intake details instead of a stack of voicemails.
How do AI agents work with human agents?
AI agents work best as part of a team, not as a replacement for one. They handle predictable, well-defined requests, while people handle exceptions, sensitive situations, and conversations that need judgment or empathy. The handoff between the two is where many customer experiences are won or lost.
When an AI agent escalates, the person who picks up should not have to start over. With Dialpad AI Agents, the human agent can see context from the conversation so far. Depending on the interaction, that can include the reason for the contact, the customer identifier, why it escalated, the steps already attempted, customer sentiment, verification status, and the outcome the customer is asking for. Customers are less likely to repeat themselves, and agents can pick up where the AI agent left off.
Because Dialpad AI Agents and Dialpad Support for contact centers both run on Dialpad's AI platform for customer experience, conversations handled by AI and by people are captured in one place. That gives leaders a fuller picture of what customers need and how well each request is being handled.
Benefits and limits of AI agents
AI agents can bring clear benefits when they are applied to the right work:
Faster resolution for routine requests, because customers don't wait in a queue for simple tasks.
Support outside business hours, so customers can get help at night and on weekends.
More time for complex conversations, since agents spend less time on repetitive requests.
Consistent handling of common workflows across channels.
They also have limits worth planning for. AI agents depend on the quality of the information and systems they can access. They can make mistakes, especially with ambiguous or unusual requests. And they need clear guardrails about what they can and cannot do. That is why teams test agents before launch, keep humans in the loop, and review conversations regularly. In Dialpad, Guardian, the AI safety layer, monitors AI Agent activity to help reduce data exposure risk and flag harmful or out-of-scope content.
How to get started with AI agents
A practical place to start is with the requests your customers make most often. Your conversation history already shows what your contact center can automate. Dialpad's Skill Mining analyzes transcripts from human-handled calls and recommends AI agent skills, along with the share of calls each skill could handle.
From there, pick one well-defined workflow, connect the systems it depends on, test it against realistic conversations, and measure results before expanding. Starting narrow and building on what works is the foundation of effective customer service automation.
Put AI agents to work with Dialpad
See how Dialpad AI Agents resolve routine requests across voice and digital channels and hand off to your team with full context.