AI Agents: 8 Use Cases Transforming Operations

Mar 26, 2025

Kevin Bartley

Customer Success at Stack AI

As a field that spans across industries, operations are vital to enterprise companies, their processes, and their performance.

Operations touch every aspect of an organization, but some venerable use cases include supply chain management, quality control, logistics, and customer service.

At Stack AI, we’ve dealt with hundreds of operations teams, and we’ve seen a wide variety of AI agents in action.

In the following blog, we’ll showcase the top AI agents we’ve encountered in the operations field, and detail how AI agents work in general.

What is an AI Agent?

An AI agent is a software system built to function independently while working toward specific goals. Unlike traditional programs that strictly follow predefined instructions, AI agents can interpret their environment, process data, and modify their actions accordingly.

This adaptability allows AI agents to operate autonomously, solving problems and making decisions in real time without requiring continuous human input.

AI agents, AI chatbots, and AI assistants all leverage large language models (LLMs) to perform tasks. However, AI chatbots, like ChatGPT, are primarily designed to respond to direct user queries, executing tasks based on immediate input rather than working independently.

AI assistants, such as Siri and Alexa, offer slightly greater functionality, handling a variety of tasks based on spoken or written commands—such as setting reminders or playing music. However, these assistants still heavily rely on user direction and lack the capability to pursue long-term goals independently.

Compared to other AI-driven systems, AI agents possess a greater degree of autonomy, enabling them to systematically break down complex tasks into smaller steps and execute them in sequence. Unlike chatbots and assistants, AI agents manage their tasks without requiring constant user intervention.

While AI chatbots, AI assistants, and AI agents all utilize intelligent processing to carry out instructions, they differ in their level of independence. Though they share foundational technologies, their key distinction lies in how they make decisions and execute tasks. AI agents, unlike the other two, can function as independent entities, adapting to dynamic environments while striving toward long-term objectives.

How Do AI Agents Work?

AI agents follow a structured process that enables them to autonomously set goals and complete tasks. This process involves defining an objective, gathering relevant information, developing a task plan, and executing actions to achieve a desired result.

Unlike conventional programs that operate based on static instructions, AI agents can adjust their approach dynamically in response to new data and evolving conditions. Here’s an overview of how they function:

Setting a Goal – An AI agent begins by determining its objective, which may be assigned by a user or triggered by an external event. This goal could range from categorizing incoming emails to conducting complex financial analysis.
Gathering Information – Once the goal is established, the agent collects the necessary data—whether from internal databases, real-time web searches, or other sources—to make informed decisions.
Planning Tasks – The agent then outlines the sequence of actions needed to accomplish the goal, breaking it down into manageable steps. For example, an AI agent analyzing financial reports might first retrieve relevant documents, then extract key data, and finally compare results across multiple datasets.
Executing Actions – Following its plan, the AI agent autonomously carries out tasks while continuously monitoring progress. As it works, it adapts its approach in response to new information or environmental changes, ensuring it remains aligned with its objective while optimizing performance in real time.

AI agents can be categorized based on their complexity and the way they interact with their surroundings. These classifications help determine how an agent will handle tasks, from simple, rule-based actions to more advanced adaptive behaviors. Below are the primary types of AI agents:

Simple Reflex Agents – These agents react to specific inputs using predefined rules without storing past information. They work well for straightforward tasks requiring instant responses, such as spam filtering.
Model-Based Reflex Agents – More advanced than simple reflex agents, these systems rely on stored data or an internal model of their environment to make informed decisions based on current and past information.
Goal-Based Agents – These agents evaluate possible actions in relation to a specific objective, planning steps to reach a desired outcome—such as calculating the fastest route in navigation systems.
Utility-Based Agents – These systems assess different options based on a utility function, optimizing actions for maximum benefit. For example, they might be used in financial markets to select the most profitable trading strategy.
Learning Agents – The most sophisticated type, learning agents continuously improve their performance by analyzing feedback from previous actions. They are capable of adapting to changing environments, making them ideal for tasks such as advanced fraud detection.

Each AI agent type builds upon the capabilities of the previous ones, offering increasing levels of adaptability and intelligence. This range of options allows developers to select the best-suited architecture for their specific needs, whether that involves executing routine tasks or handling complex, evolving objectives.

When designing an AI agent, it’s essential to weigh the desired level of autonomy against the system’s complexity to create an effective solution tailored to a given task.

Top 8 Use Cases in Operations

AI Staffing Assistant

Department	HR
Persona	HR Associate
Problem	It is difficult to find the right employee for the right task across a company.
Solution	The AI agent finds the best employee for a project. A user uploads a document describing a project and cross-references a list of employees and skill sets.
User Interface	Chat Assistant
LLM	Azure GPT-4o
Data Sources	File upload (document), Sharepoint
Actions	An employee uploads a document. User query, document, and Sharepoint containing employees and skillsets are fed into the LLM. The LLM performs analysis and shares the employee best suited for the project.
Time to Launch	Easy
Benefits	90% reduction in time it takes to find the right department/employee Works across the company as opposed to teams only Helps companies with disruptions find the right talent quickly
Agent Workflow

Staff Training Assistant for New Employees

Department	HR
Persona	HR Associate
Problem	Answering new employee questions takes time, and involves sifting through many documents.
Solution	This AI agent answers the question of new employees, providing them with information about expenses, company policies, and more.
User Interface	Chat Assistant
LLM	Azure GPT-4o Mini
Data Sources	Knowledge Base (company documents), Knowledge Base (Sharepoint - Market information), Knowledge Base (job description of role)
Actions	Input is fed into a routing node. The node has 4 components. Broad questions about the company - this is routed to a knowledge base containing a document about company roles. Questions about personal role - this is routed to a knowledge base containing company documents Administrative questions - this is routed to a Sharepoint drive with relevant information For all other questions - this is routed directly to the LLM. All options are routed to Azure LLM. Answers to the questions are outputted.
Time to Launch	Medium
Benefits	Reduces research time from 8 hours to 15 minutes Analysts can spend more time focusing on valuable tasks Firm can invest in more companies, leading to higher profit margins
Agent Workflow

Infosec Agent

Industry	Horizontal
Persona	CIO
Problem	Finding the answers for a company’s SOC2 compliance is time consuming and the room for error is zero.
Solution	The AI agent answers questions based on a company’s SOC2 documents and provides answers.
User Interface	Batch
LLM	Open AI - GPT-4o / GPT-4o Mini
Data Sources	Documents + Search (SOC2 Documentation)
Actions	User inputs a series of questions about SOC2 into a CSV. User uploads the CSV. The Agent answers all the questions in batch based on the SOC2 documentation.
Time to Launch	Easy
Benefits	Cut time spent analyzing SOC2 documentation from 4 hours to 5 minutes Answer complex security questions automatically Avoid human-related errors in complicated topic with no room for error
Agent Workflow

AI Slackbot

Industry	Horizontal
Persona	Horizontal
Problem	Employees need a chat assistant to help them speed up their work and answer questions throughout the day.
Solution	The AI agent is designed to answer questions and assist the user — all from their Slack interface.
User Interface	Slack App
LLM	OpenAI GPT-4o mini
Data Sources	Documents + Search
Actions	User prompts the chatbot from the Slack interface. The LLM answers the questions based on a cache of documents. The output is returned as a Slack message.
Time to Launch	Easy
Benefits	Receive important notifications directly in your messenger app of choice (Slack) Set notifications for team members and team channels Avoid having to look in other platforms for important information
Agent Workflow

Customer Support Chatbot

Industry	Horizontal
Persona	Customer Support Representative
Problem	Customer support resources are limited and this leads to waiting times and upset customers.
Solution	The AI agent is a chatbot that answers questions based on the product knowledge and documents.
User Interface	Form
LLM	OpenAI GPT-4o mini
Data Sources	Docs + Search, URL + Search
Actions	User asks a question. LLM references Documents and web search to answer them.
Time to Launch	Easy
Benefits	Answer more customer support questions faster Easily deployable on websites and company platforms Minimize the need for human customer support agents
Agent Workflow

Tender Document Analysis

Industry	Construction
Persona	Analyst
Problem	Tender documents are long and complex and it takes time to analyze them for the right information.
Solution	This AI agent analyzes a tender document provided by the user and breaks down the cost and scope of the project.
User Interface	Batch
LLM	Azure GPT-4o / GPT-4o Turbo
Data Sources	File upload (tender document)
Actions	Employee uploads a document or several documents. An employee runs the batch. Financial analysis and scope of the works are returned as text.
Time to Launch	Medium
Benefits	Analyze tender documents 10x faster Procurement teams can make bids quicker Project breaks ground sooner
Agent Workflow

Database Assistant

Industry	Horizontal
Persona	SalesOps, RevOps, MarketingOps
Problem	Business users such as the sales and marketing team don’t know SQL and can’t extract important information from relational databases.
Solution	This AI agent allows business users to extract important information from Postgres by using text instead of code.
User Interface	Form
LLM	OpenAI - GPT 4-o mini (x2)
Data Sources	PostgreSQL database
Actions	The user enters a text-based prompt. The text is converted into a SQL query. The SQL query is run against the Postgres database. The data resultant from the SQL query is returned to the user.
Time to Launch	Medium
Benefits	Allows non-coders on business teams such as sales and marketing to leverage crucial databases. Saves the data team time, since they’re focusing on less requests from business users. Makes sellers and marketers more effective at attracting and converting customers.
Agent Workflow

Learn About More Operations Use Cases

The AI agents featured in this white paper handle complex operational tasks. Our list of the top eight use cases is designed to help you develop AI agents that address common operational challenges.

However, these examples represent just a fraction of the potential applications. As more teams embrace AI builder tools, AI agents will be created for countless other use cases, and we’ll continue to document them as they emerge.

Stay updated by following our blog for the latest use cases in operations. Get started with Stack AI for free today and build AI agents using a no-code interface.