Top 10 Best AI Agents Revolutionizing Technology in 2025

As generative AI continues to expand across enterprise businesses, the demand for more complex AI applications is growing. Enterprises are now turning to AI agents, or autonomous AI entities that can make critical business decisions on their own.

The emergence of AI agents in enterprises is transforming the way work is done. Rather than automating specific functions, AI agents are built to perform jobs within a company, such as customer support, document analysis, and sales prospecting.

These jobs require AI actors that can make intelligent, autonomous decisions, while also learning from experience. This allows AI agents to do jobs that have traditionally required complicated intelligence. 

AI agents represent the next frontier for enterprises that need to cut costs and increase their workforces. And that’s why, in the following blog, we’ve outlined the top 10 AI agents for enterprises. 

These AI agents span different industries, from finance, to healthcare, to logistics, and more. They cover many use cases, from investment memo generation, to call center compliance, to contract redlining. After working with hundreds of companies to build AI agents, we’ve found that these are the most impactful. 

Read the following blog to learn everything you need to know about AI agents, including the top 10 AI agents our customers are currently using. 

What Are AI Agents?

An AI agent is a type of autonomous software entity engineered to pursue defined objectives with minimal human oversight. 

Unlike traditional programs that operate strictly according to pre-coded logic, AI agents integrate perception, data analysis, and decision-making capabilities to dynamically adapt to their environment. This adaptive behavior enables them to function independently, adjusting their strategies in real time to accomplish complex tasks.

While AI agents, AI chatbots, and AI assistants all utilize large language models (LLMs) at their core, they differ markedly in terms of autonomy, operational scope, and task complexity.

AI chatbots—exemplified by systems like ChatGPT—are fundamentally reactive. Their primary function is to interpret and respond to user inputs within a conversational context. While capable of generating sophisticated responses or completing predefined tasks, chatbots lack initiative and cannot perform actions without direct prompts from the user.

AI assistants, such as Siri, Google Assistant, or Alexa, extend these capabilities by enabling voice- or text-based interactions that trigger a broader set of functions, such as managing calendars, playing media, or retrieving information. 

Despite this expanded functionality, AI assistants remain input-dependent and do not engage in autonomous, goal-driven behavior. They execute discrete, often short-term tasks and do not maintain persistent context over time or independently pursue longer-term objectives.

In contrast, AI agents represent a more advanced paradigm. These systems are designed for task decomposition, strategic planning, and self-directed execution. An AI agent can disaggregate a high-level goal into a sequence of subtasks, prioritize and schedule them, and iteratively refine its plan based on changing conditions or feedback. This makes AI agents particularly suitable for dynamic, multi-step workflows, such as autonomous research, workflow automation, or long-horizon optimization problems.

While all three—chatbots, assistants, and agents—leverage similar underlying models and natural language processing technologies, their operational architectures and autonomy levels differ significantly. 

Chatbots are constrained to stimulus-response patterns. Assistants offer a broader command interface but are still reactive. AI agents, however, are capable of sustained, autonomous operation, making decisions and executing actions over extended periods in pursuit of complex objectives.

Types of AI Agents

AI agents can be categorized based on their internal architecture and the sophistication of their interaction with the environment. These classifications reflect a spectrum of complexity, ranging from reactive systems that deliver immediate responses to intelligent agents capable of learning and adapting over time.

Below is an overview of the key types of AI agents, each suited to particular use cases and operational needs:

1. Simple Reflex Agents - At the most basic level, simple reflex agents respond directly to environmental stimuli using a predefined set of condition-action rules. They operate without memory or context, making them ideal for routine, repetitive tasks where speed and simplicity are prioritized—for instance, filtering out obvious spam emails based on keywords.

2. Model-Based Reflex Agents - These agents add a layer of sophistication by incorporating a model of the environment. They not only respond to inputs but also maintain some awareness of past states, allowing them to make more informed decisions. This added context enables smarter behavior in scenarios where immediate data alone isn't enough to determine the correct response.

3. Goal-Based Agents - Moving beyond mere reaction, goal-based agents are designed to pursue specific outcomes. They evaluate different possible actions and plan sequences of steps that will lead them toward a desired objective. An example is a navigation system that determines the most efficient route based on current traffic conditions.

4. Utility-Based Agents - These agents take decision-making a step further by incorporating a utility function to weigh the desirability of various outcomes. Instead of simply reaching a goal, they aim to optimize the result—whether that means reducing travel time, maximizing returns in a financial portfolio, or minimizing energy consumption in smart systems.

5. Learning Agents - At the top of the hierarchy are learning agents, which evolve over time by incorporating feedback from their environment. They refine their behavior through experience, allowing them to adapt to new challenges or changing conditions. These are particularly valuable in complex, dynamic domains like cybersecurity or real-time recommendation systems.

Each agent type builds upon the capabilities of the previous, offering increasing levels of intelligence and flexibility. Understanding these distinctions helps developers choose the right agent architecture for the task at hand—whether the goal is to implement a lightweight automation tool or a sophisticated, goal-driven system that can learn and adapt autonomously.

When architecting an AI solution, it's important to align the complexity of the agent with the demands of the application. Selecting the appropriate agent type can significantly influence both the performance and maintainability of the system over time.

Benefits of AI Agents for Enterprises

AI agents offer enterprise companies a number of benefits. In their ability to simulate complex intelligence, AI agents allow enterprises to automate not just tasks, but jobs within the company. This could include IT support agents, financial analysis agents, and call center representatives, among many others. 

This allows enterprises to fill their growing workforce gaps without greatly increasing payroll costs. And it allows current employees to focus on more pertinent tasks that require human cognition. This results in increased productivity and cost-savings, especially for enterprises that introduce AI agents at scale.

AI agents also lead to enhanced decision making, for both AI actors and the humans who rely on them. By leveraging methods such as predictive analytics, AI agents can make smarter, faster decisions that improve operations and outcomes. Additionally, AI agents can increase customer satisfaction with personalized services.

Use Cases for AI Agents

Here are some of the top AI agent use cases we’ve encountered in the field with our enterprise customers, organized by industry. 

Finance

As fintechs move to unseat incumbents in the financial sector, both sides are under pressure to incorporate AI agents into their workforce. The finance industry is still document-heavy, rife with manual work and data entry, and reliant on financial sub-processes that are amendable to automation. 

This is an attractive opportunity for AI agents. Finance teams use Stack AI to build AI agents not just to automate repetitive work, but to serve as key components of their business operations. Our customers have built AI Agents for KYC, income verification, bank statement analysis, and other mission-critical processes that power the day-to-day operations of finance companies. 

Operations

Operations involve a large variety of complex and manual tasks, but ones that can be automated with the right AI agent. That’s why operations teams are leveraging AI agents to generate RFPs, manage call centers, and onboard new team members. These are just a few of the jobs our customers in operations are automating.   

Healthcare 

In an industry with large quantities of paperwork and manual processes, the healthcare sector is ideal for AI agents. However, healthcare companies must by law adhere to strict security guarantees, specifically HIPAA. Any AI agent deployed in the healthcare space must adhere to these security guidelines.

Stack AI is built to meet all of the security standards in the healthcare industry, including HIPAA. Healthcare teams can build no-code AI agents from scratch using Stack AI’s drag-and-drop builder tool. We’ve seen healthcare teams create and deploy a wide variety of AI agents, ones that provide information to health workers on the frontlines, analyze medical documents, automate back office work, and more.   

Other Industries

Besides the industries we’ve already highlighted, we’ve seen our customers successfully deploy AI agents across many different sectors. This includes manufacturing, transportation, retail, energy, and a host of other sectors. AI agents will continue to transform many different industries, and we’re expecting to see more exciting use cases for AI agents emerge in the coming years.

Now that we’ve learned more about AI agents, let’s dive into the top 10 use cases we’ve encountered among our customers. 

Top AI Agents of 2025

1. Investment Memo Generator

Industry	Finance
Department	Investment Research Department
Persona	Investment Analyst
Problem	Investment memos take a long time to produce. Analysts must manually sift through documents and perform analysis.
Solution	The Investment Memo Generator automatically writes investment memos for analysts. The agent leverages web and document sources, and uses multiple LLMs to write different sections of the report.
User Interface	Form
LLM	Anthropic - Claude 3.5 Sonnet. 4 instances of Claude are used in a workflow — each has its own unique prompt.
Data Sources	Knowledge Base, web search, LinkedIn, document upload (financials), document upload (pre-diligence)
Actions	Searches the web and user documents. LLMs produce an investment memo based on the data.
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

2. Due Diligence Assistant

Industry	Finance
Department	Investment Research Department
Persona	Investment Analyst
Problem	Due diligence requires an examination of financial records before entering into a proposed transaction with another party. This process takes a long time when done manually.
Solution	The AI agent performs due diligence with LLMs, with the following inputs and outputs.
User Interface	Form
LLM	Anthropic - Claude 3.5 Sonnet, Open AI - GPT-4o
Data Sources	Web search 1 (Online Market Landscape), Web search 2 (Online Reviews)
Actions	LLMs create web search queries. Queries run through Google Search and results fed into due diligence LLM. Report is written by the LLM.
Time to Launch	Medium
Benefits	Reduces comparison time from 4 hours to 15 minutes Analysts can spend more time focusing on key tasks Firms can avoid making bad investments, saving revenue
Agent Workflow

3. 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

4. 10Q/10K Documents Extraction

Industry	Finance
Persona	Financial analysts
Problem	10-Q/10-K forms hold critical information about a company, but they take too long for investors to analyze.
Solution	This AI agent analyzes a 10-Q or 10-K form that the user uploads and reports on these findings: 1) risk and uncertainties, 2) debts and financing, and 3) performance.
User Interface	Form
LLM	Anthropic - Claude 3.5 Sonnet (x3 instances)
Data Sources	File upload (10-Q or 10-K form)
Actions	Employee uploads a 10-Q or 10-K document.  The document is fed into three different LLMs.  Each LLM summarizes a different aspect of the report.
Time to Launch	Easy
Benefits	Reduce time spent analyzing 10-Q/10-K forms from 4 hours to 5 minutes Financial analysts can do faster and more accurate assessments of companies Companies can invest in more eligible companies and produce a higher profit margin
Agent Workflow

5. RFP Response Assistant

Industry	Non-Profit
Persona	Proposal Team
Problem	Analyzing RFPs, and responding to them, is a very time-consuming task. This limits the number of RFPs a non-profit can respond to.
Solution	The AI agent automatically writes a proposal for the RFP proposal that the user uploads.
User Interface	Chat Assistant
LLM	Anthropic - Claude 3.5 Sonnet
Data Sources	Document upload (RFP), Docs + Search (past RFP responses)
Actions	User uploads RFP.  The RFP is analyzed by the LLM.  The LLM produces a response.
Time to Launch	Easy
Benefits	Respond to RFPs in 15 minutes as opposed to several hours. Eliminate the need to read dense RFPs; automate the process instead. Respond to more RFPs and land more profitable projects.
Agent Workflow

6. Contract Redlining

Industry	Horizontal
Persona	Proposal Team
Problem	Reviewing and marking up a contract with proposed changes is tedious, time-consuming, and sometimes requires specialized knowledge.
Solution	The AI agent analyzes a contract and proposes redlines and other changes.
User Interface	Form
LLM	Anthropic - Claude 3.5 Sonnet
Data Sources	Document upload (RFP), Docs + Search (past RFP responses)
Actions	User uploads contract.  The contract is analyzed by the LLM.  The LLM produces proposed changes
Time to Launch	Easy
Benefits	Reduces time for contact redlining from hours to minutes Automates complex process of analyzing and auditing contracts Allows teams to supplement contracting with AI-driven insights
Agent Workflow

7. Programmatic SEO Tool

Industry	Horizontal
Persona	SEO Strategists
Problem	Producing SEO-focused content is time-consuming and costly.
Solution	The AI agent automatically produces blogs and meta descriptions based on title and keyword that the user provides.
User Interface	Batch
LLM	OpenAI — ChatGPT 4o, GPT-4o mini
Data Sources	Web search, file upload
Actions	User uploads Title/Keyword pairs via CSV.  Batch run generates blog articles and meta descriptions.
Time to Launch	Easy
Benefits	Automatically write hundreds of blog posts all at once Adhere to SEO best-practices in all of your content  Launch thousands of pages simultaneously to supercharge SEO gains
Agent Workflow

8. Call Center QA Agent

Industry	Healthcare
Persona	Compliance Officer
Problem	Manually listening to customer support calls and identifying compliance issues is very time consuming.
Solution	This AI agent analyzes a call uploaded by the user and creates a report that assesses the customer service representative’s adherence to compliance rules.
User Interface	Form
LLM	AWS Bedrock — Claude 3.5 Sonnet
Data Sources	File Upload (Audio-to-text) - Customer Support Call
Actions	Compliance officer uploads customer support call as an audio file.  The audio file is converted into text and fed into AWS Bedrock.  The LLM analyzes the text for compliance and then details its findings in a report.
Time to Launch	Easy
Benefits	Cut time spent reviewing compliance calls from 100 hours a month to 4 hours a month. Allows compliance officers to focus on more high-functioning analysis. Enables healthcare companies to invest in life-saving medical roles instead of back office.
Agent Workflow

9. Video to Blog Post Generator

Industry	Horizontal
Persona	Marketing Manager
Problem	Converting YouTube videos into written blogs is valuable but time consuming.
Solution	The AI agent asks the user to upload a YouTube URL and converts the video into a blog post.
User Interface	Form
LLM	Anthropic - Large Language Model - Claude Sonnet 3.5
Data Sources	YouTube URL
Actions	User uploads a YouTube URL.  URL is summarized by the summarizer.  The large language model generates a blog post based on the summarization.
Time to Launch	Easy
Benefits	Convert blog post into video without requiring any manual work  Generate many different blogs very quickly as opposed to waiting weeks or months Allow content team to focus on more valuable projects
Agent Workflow

10. 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

Stack AI: The AI Agent Builder Tool for Enterprises

All of the AI agents that we’ve highlighted in this blog were built by our customers, using the Stack AI platform. Stack AI is a no-code AI builder tool that allows non-technical teams to create AI agents using drag-and-drop functionality.

With Stack AI, business teams can use their domain expertise to build AI agents that solve their specific use cases, all without coding. Using pre-built components, teams can design AI agents in minutes, creating seamless workflows of inputs, outputs, LLMs, and connected knowledge bases. 

Teams can use any LLM among all leading providers — like OpenAI, Anthropic, Google, and Meta — and choose the best model for each task. When building specialized solutions, you can also leverage integrations with AI infrastructure such as Hugging Face (community models), Cerebras (fastest inference and training services), or Azure AI (containerized, HIPAA-compliant deployment of OpenAI models).

Teams can leverage Stack AI’s template library to launch pre-designed AI agents for popular use cases. This includes investment memo agents, RFP response agents, contract analyzers, and much more. Teams can bypass the building phase and get straight to solving their use cases.

Stack AI connects to popular enterprise-grade software such as Microsoft Sharepoint, AWS S3, Salesforce, or HubSpot, among many others. As you connect your data sources, you can organize them into knowledge bases inside Stack AI.

Stack AI also maintains HIPAA, GDPR, and SOC compliance, required standards for many industries to ensure privacy and security.

With these certifications, enterprise teams can build secure and scalable AI agents that can solve for their business use cases.

Bring the Best AI Agents to Your Company

Enterprise companies are now utilizing AI agents to power business operations and perform critical decision-making. 

As seen by the AI agents in this blog, there are a wide variety of use cases that our customers are implementing, and plenty more that we’ve yet to cover. 

But in order to build these agents efficiently and securely, large companies need an enterprise-grade AI builder tool.

Stack AI offers teams enterprise security, a no-code interface, access to any LLM, and much more. The platform enables teams to build any of the AI agents highlighted in this blog, and many others as well.

Sign up for a free account with Stack AI to start building these top AI agents today. Or better yet, use one of our pre-built templates.