What Is an AI Agent? How It Works and Business Examples
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- 11 hours ago
- 7 min read
AI agents have become one of the most talked-about applications of artificial intelligence, but the term is still often confused with chatbots and traditional automation. The main difference is what happens after a request is made. A chatbot primarily responds with information, while an AI agent can determine what needs to happen next and take actions across different tools or applications to complete a task.
What Is an AI Agent?
An AI agent is an AI system that can understand a goal, determine the steps required to achieve it, and take those actions through connected applications or tools without requiring detailed instructions at every stage.
This ability separates AI agents from conventional AI assistants. A chatbot, for example, could explain how a customer can change their subscription plan. An AI agent could actually process the request by checking the customer's account, applying the change, and confirming that the new plan is active.
How Do AI Agents Work?
AI agents typically follow a recurring process whenever they receive a task:
Receive a goal or instruction: The agent receives a task in natural language, such as "process this customer's request to upgrade their subscription."
Create a plan: The system determines the smaller steps required to complete the goal.
Take action: The agent performs those steps using available tools, such as checking email, searching documents, querying a database, or updating another application.
Evaluate the result: The system checks whether its actions were successful and adjusts the next step if necessary.
The planning and action stages are what distinguish AI agents from conventional chatbots, which usually complete their role once a response has been generated.
For example, suppose a customer contacts support to upgrade to a higher subscription tier. An AI agent could read the request, verify that the customer's account is active, calculate the prorated billing difference, update the subscription in the database, send the revised invoice by email, and confirm through chat that the new plan is active.
If something goes wrong, such as a payment method failing verification, the agent can stop the process and escalate the case to a human employee with the actions it has already completed.
Main Components of an AI Agent
Several capabilities work together to enable an AI agent to take action:
Large language model (LLM): Understands instructions, context, and natural language.
Reasoning: Determines how a larger goal can be divided into smaller, logical tasks.
Access to tools or applications: Allows the agent to interact with other systems, transfer data, and perform actions.
Memory or context: Retains relevant information from previous steps so later actions remain consistent with the task.
Without access to external tools, the system is closer to an advanced chatbot. The ability to interact with other systems and perform actions is what allows an AI agent to move beyond simply generating a response.
AI Agent vs Chatbot
The main difference between an AI agent and a chatbot is what happens after the conversation.
An AI chatbot for customer service, for example, can answer questions using company documentation. But when a customer needs account information changed or another process completed, an AI agent can continue beyond the conversation and perform the required actions.
For a more detailed comparison, read our guide to AI agents vs. chatbots.
Chatbot | AI Agent | |
|---|---|---|
Primary role | Answer questions and provide information | Complete a defined goal or task |
Interaction | Usually responds to user prompts | Can continue working across multiple steps |
Tool use | May have limited integrations | Uses tools and applications to perform actions |
Decision-making | Follows fixed, rule-based response paths | Can decide the next step and adapt mid-task |
Example | Explains how to request a refund | Checks eligibility, processes the refund, and confirms completion |
AI Agent vs Agentic AI
AI agents and agentic AI are closely related, but the terms do not describe exactly the same thing. An AI agent is an individual system designed to pursue a goal and perform actions. For example, an AI agent might schedule an interview or process a customer request.
Agentic AI is a broader approach to building AI systems that can reason, plan, use tools, coordinate tasks, and operate with greater autonomy. An agentic system may consist of one AI agent or multiple agents working together across more complex workflows.
Benefits of AI Agents for Business
AI agents are particularly useful for processes that currently require employees to perform the same sequence of manual steps repeatedly. Common benefits include:
Faster response times: Tasks can begin processing as soon as a request arrives instead of waiting in an employee's queue.
Availability outside business hours: Agents can continue performing defined tasks even when employees are offline.
Less repetitive manual work: Teams spend less time repeating the same administrative actions for every request.
Greater capacity during demand spikes: Higher request volumes do not always require a proportional increase in staff.
More consistent execution: Agents can follow predefined processes and escalation rules across repeated tasks.
Consider a team that currently spends a large portion of the day manually processing customer subscription changes. If an AI agent handles account checks, billing calculations, system updates, and confirmation messages, many of those requests could be processed automatically while employees focus on exceptions that require human judgment.
Examples of AI Agents in Business
AI agents can be used across business functions where processes are repetitive and can be divided into clearly defined steps. Here are several common examples.
AI Agents for Sales
An AI agent for sales can evaluate incoming leads from website forms, advertising campaigns, messaging platforms, or other channels using available customer information and previous interactions.
Leads that are still early in the buying process can enter an automated follow-up workflow, while higher-intent leads can be routed to a sales representative along with a summary of their needs and previous conversations. For businesses handling high lead volumes, this can reduce the amount of time sales teams spend manually deciding which prospects should receive attention first.
AI Agents for HR and Recruiting
In recruiting, an AI agent for HR can review incoming applications against job requirements and prepare summaries of candidates who appear to be a good fit.
Once a recruiter approves a candidate, the agent could check calendar availability, schedule the interview, send the invitation, and update the relevant recruiting system. The same approach can support onboarding and recurring employee requests where the process follows clearly defined rules.
AI Agents for Finance
For finance operations, an AI agent for finance can monitor transactions and compare them with established patterns, flagging unusual activity for the finance team to review. The same agent could also gather data from multiple systems and prepare recurring reports automatically, allowing the finance team to spend more time analyzing the numbers rather than collecting them.
AI Agents for Marketing
On the marketing side, an AI agent for marketing can support recurring campaign activities such as scheduling content, adjusting messages based on previous audience behavior, monitoring campaign performance, and triggering predefined actions when performance falls outside an expected range.
The marketing team still controls overall strategy, positioning, creative direction, and major decisions. The agent handles operational tasks that would otherwise require repeated manual intervention.
AI Agents for Operations and Supply Chain
To prevent stockouts, an AI agent for supply chain can continuously monitor inventory levels and demand patterns.
If the system predicts that inventory will fall below the required level, the agent could prepare a replenishment request, identify the appropriate supplier based on predefined criteria, or initiate an approval workflow before inventory runs out.
This can be particularly useful for businesses managing large numbers of SKUs, suppliers, or warehouse locations.
When Should a Business Use an AI Agent?
Not every automated process needs an AI agent. AI agents are most useful when a workflow has a clear goal but requires several actions or decisions before that goal can be completed. A good use case typically has:
A clearly defined objective
Several repeatable steps
Data that can be accessed digitally
Applications or APIs the agent can interact with
Clear rules for when human approval is required
Enough volume to justify automating the process
Processes that depend heavily on subjective judgment, unclear objectives, or sensitive decisions may still require significant human involvement. The first step is usually identifying which parts of an existing workflow can be delegated while keeping appropriate human oversight in place.
Developing AI Agents for Business
Many companies already know which processes consume the most employee time. The harder part is determining which steps can be delegated to an AI agent and how the agent should connect with existing systems without disrupting day-to-day operations.
An effective AI agent needs access to the systems and data employees already use, whether that includes a CRM, internal database, document repository, email platform, ERP, or another business application.
BI Solusi helps businesses plan and implement AI for business, including AI agents that connect with existing company data and applications. Organizations can begin with one clearly defined workflow and expand into additional use cases as the initial implementation proves useful.
FAQ
What is an AI agent?
An AI agent is an AI-powered system that can understand a goal, determine the steps required to achieve it, and take actions through connected tools or applications.
How is an AI agent different from a chatbot?
A chatbot primarily generates responses to user questions. An AI agent can continue beyond the conversation by using tools, making decisions, and performing actions required to complete a task.
What is the difference between an AI agent and agentic AI?
An AI agent is an individual system designed to perform goal-directed tasks. Agentic AI describes a broader approach to AI systems that can reason, plan, use tools, and operate with a degree of autonomy.
Do AI agents require an LLM?
Many modern AI agents use large language models for natural-language understanding and reasoning. However, an AI agent does not always need an LLM. What defines the agent is its ability to perceive information, make decisions, and act toward a goal.
Do I need an in-house team to build an AI agent?
You do not need an in-house team to build an AI agent. Many companies work with a partner such as BI Solusi to design the agent and connect it to existing systems, so internal teams do not have to build the integration from scratch.
What business functions can use AI agents?
AI agents can support sales, HR, finance, marketing, customer service, supply chain, operations, and other functions where workflows consist of clearly defined actions that can be performed digitally.
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