What Are AI Agents? Examples, Benefits, and Key Use Cases

Life is busy. And when you don’t have time to handle tasks on your own, artificial intelligence (AI) agents are a lifeline.

While chatbots or generative tools might come to mind when you think about AI agents, they go beyond simple language processing and image generation. They can make decisions, perform responsive actions, and interface with external systems, like managing your calendar or communicating with customer support platforms.

Here’s a guide to what these agents are, how they operate, and what types of AI agents are out there. We’ll also introduce Otter’s three new AI agents, designed to supercharge meetings: Otter Meeting Agent, Otter Sales Agent, and Otter SDR Agent.

What is an AI agent?

An AI agent is a versatile tool designed to handle tasks and processes autonomously. Think of it as a highly intelligent assistant that streamlines workflows, using a suite of high-tech processes to help you achieve your goals. 

10 examples and types of AI agents

As technology advances, AI agents become more specialized and diverse in their abilities to improve business operations and individual workflows. To decide on the right intelligent agent for your needs, let’s break down 10 unique examples of AI agents:

1. Simple reflex agents

As the name suggests, simple reflex agents are the most basic type. They rely on predefined rules and inputs that trigger specific actions. That means they can’t adapt to new situations, learn autonomously, or interact with other agents. 

A simple reflex agent’s main components are the sensors, the actuators, and the inputs. When condition A occurs, the agent triggers response B. For example, a simple reflex agent might be programmed to tell time (sensor) so that it can turn on a light (actuator) every night at 7 p.m. (input).

2. Model-based reflex agents

Model-based reflex agents combine real-time perceptions with memory to build a model of their environment. They analyze current conditions based on past experiences, which lets them operate effectively in changing environments. 

For instance, an intelligent security system uses sensors to detect movement, monitor activity, and build an autonomous internal model with percepts about different areas of space and the time of day. The system's sensors can then differentiate between normal activity and potential security threats, adjusting how the agent acts as it monitors movement — highlighting suspicious activity when it happens.

3. Goal-based agents

This type of AI agent designs an internal model of its environment to take action on specific objectives. Unlike simpler agents, goal-based agents evaluate potential actions and make decisions to effectively reach the best outcome. 

Consider a personal fitness app that coaches you toward your health goals. The app tracks your activities and health data to help you define new objectives, like increasing your daily step count or increasing workout intensity. Once you give it a new goal, the AI agent creates a plan to reach it.

4. Utility-based agents

Utility-based agents take goal-based assistance a step further, achieving objectives by considering a fixed set of criteria. Goal-based agents help you reach a goal, and utility-based agents analyze the highest overall benefit based on the criteria — or what will make the agent (and its user) the happiest.

Imagine a smart energy management system. Its criteria would be to maintain a comfortable temperature with a low environmental footprint and reduced costs. The agent measures and adjusts temperature throughout the day, maximizing the expected utility.

5. Learning agents

Learning agents continuously improve their performances through experience. They autonomously update their knowledge base as they interact with their environments, and this ability to learn and adapt makes them particularly effective in new and changing situations. You can set up the system to be utility or goal-based. 

A learning agent’s performance is based on four different elements: 

• Learning: The AI agent acquires new knowledge from its interactions with the environment. 
• Criticism: It analyzes its actions, assessing how well its response matches the desired outcome.
• Performance: It completes tasks based on the rules it teaches itself. 
• Problem generation: It creates different scenarios, understanding potential benefits and challenges to propose the right response. 

Think of a smart email filtering system. The AI agent learns to identify spam and prioritize important messages based on user interactions. As it receives feedback from the user about the system’s filtering accuracy, it adjusts the rules and algorithms. Over time, the system becomes better at distinguishing between relevant and irrelevant emails, providing precise organization.

6. Conversational AI agents

Conversational AI agents can understand and generate human-like text or speech. They use a combination of natural language processing (NLP), machine learning, and large language models to accomplish this.

These agents function like super-intelligent chatbots. Whereas traditional chatbots are limited to scripted responses and basic commands, conversational AI agents can interpret context, nuance, and user intent. They can adapt their responses based on past interactions and the flow of conversation, making them far more dynamic and useful in real-life scenarios.

Some conversational AI agents can also carry out tasks based on your discussion. Take Otter Meeting Agent, for example. It goes beyond simple transcription and summarization — now it actively participates in meetings by answering questions, scheduling follow-ups, and drafting emails.

7. Multi-agent systems

Multi-agent systems involve multiple AI agents that collaborate to complete complex tasks more efficiently than a single agent could. Each agent in the system may have a specific role or set of goals, but they share information to work toward a common objective.

Consider a self-driving car. It has agentic components that process and share sensor data so the vehicle can navigate safely and efficiently. In the future, self-driving cars may be able to interact with one another and with smart traffic infrastructure to optimize their routes and avoid accidents.

8. Autonomous virtual assistants

Autonomous virtual assistants are built to handle tasks from start to finish without constant check-ins or prompts. These AI-powered agents can make decisions, follow logic, and carry out complex workflows all on their own. They’re especially helpful for staying organized and saving time during a busy day.

For example, Otter can automatically attend your meetings, transcribe the conversation in real time, and send you a polished summary with action items and highlights afterward. Skip the note-taking and focus on the conversation while your assistant handles the rest.

9. AI-powered knowledge agents

AI-powered knowledge agents are designed to turn massive amounts of unstructured data into usable insights. They don’t just collect information — they actively process and organize it, then surface the most relevant points so teams can make faster, smarter decisions.

Otter functions as an AI-powered knowledge agent by transcribing and summarizing your meetings. By automatically identifying key insights and action items, it helps you move forward with clarity and focus.

Similarly, Otter Meeting Agent turns your entire meeting database into a searchable, interactive source that updates as new conversations happen. You can voice questions about what you discussed in previous meetings, and the Meeting Agent searches the database to return the right answer.

For sales teams, Otter Sales Agent adds another layer of intelligence by offering live coaching and objection handling during calls. It listens in, recognizes patterns, and helps sales reps respond with confidence, boosting close rates in the moment.

10. AI workflow automation agents

AI workflow automation agents make business operations smoother by handling the repetitive tasks that eat up time. They streamline your workflow by taking over routine processes, allowing teams to focus on higher-level work.

Otter Meeting Agent is a standout example of a workflow automation agent. As we’ve discussed, it answers questions, schedules follow-ups, drafts emails, and lets users interact with AI-powered meeting data in real time. This helps you manage daily work tasks more efficiently.

11 key AI agent use cases across industries

AI agents can be transformative in nearly any industry — even outside the typical tech world. Here are a few examples of how they work in different markets:

1. AI agents in customer service

One of the most recognizable uses of AI agents is in customer service, where AI technologies offer 24/7 support — common examples include chatbots, virtual assistants, and automated response systems. These all improve efficiency and customer satisfaction, and they’re the perfect opportunity for businesses can also analyze customer inputs and enhance the quality of service.

2. AI agents in healthcare

AI systems can do more than manage patient records, schedule appointments, and monitor health metrics. Medical professionals can program AI agents to help diagnose medical conditions through pattern recognition and personalize treatment plans based on patient data. These processes save time and help people get diagnoses and treatments faster.

3. AI agents in education

AI-powered note-taking and meeting assistants help students and educators transcribe lectures, organize notes, and summarize key information — all with an emphasis on personalization and speed. Agents with searchable text functions or integration with other platforms let students adapt notes to their learning style for an individualized experience. And transcriptions can help teachers pinpoint common questions or roadblocks to improve their lectures.

Otter for Education brings the power of AI agents to the classroom with real-time transcription, smart note organization, and easy sharing. Students can revisit lectures anytime and even learn collaboratively by tagging each other and discussing key ideas as they review the notes. Meanwhile, educators gain insights into class engagement and comprehension.

4. AI agents in sales

AI agents are transforming how sales teams connect with prospects, manage relationships, and close deals. For example, Otter Sales Agent helps reps stay on message and handle objections by analyzing live conversations and offering smart, real-time guidance. It highlights key signals like buying intent, competitor mentions, or follow-up opportunities — without interrupting the flow of the call.

Otter SDR Agent is also revolutionizing sales. As an AI-powered sales rep, it automates product demos so leads who visit your website can engage instantly without waiting for a human rep. No opportunity slips through the cracks, and buyers have a faster, smoother experience from the first click.

5. AI agents in finance and banking

In finance, AI agents are like super-sharp assistants who never sleep. They detect fraud, automate reports, monitor transactions, and even offer personalized financial advice. Banks and fintech companies use them to help customers make smarter money moves — and to streamline their own workflows. AI agents sift through and optimize data at lightning speeds, so instead of drowning in spreadsheets, finance pros can focus on big-picture tasks.

6. AI agents in e-commerce and retail

AI agents create smoother shopping experiences by predicting what customers want and when. Sellers can use these agents to manage inventory, personalize product recommendations, and keep orders flowing efficiently. Agents can also take over the bulk of customer service work, resolving issues quickly to keep customers happy.

7. AI agents in transportation and logistics

AI agents turn complex delivery networks into well-oiled machines. By analyzing routes and resources in real time, they help logistics teams deliver more efficiently — and with fewer unexpected detours. For example, AI agents can dynamically reroute shipments around traffic jams or balance warehouse workloads to avoid bottlenecks.

8. AI agents in legal and compliance

In the legal world, AI agents rely on their natural language processing abilities to analyze contracts and other legal documents at scale. They can sift through these documents faster than humans, flag inconsistencies, and spot potential compliance gaps. With AI agents handling the tedious review work, legal experts can focus on bigger issues like strategy.

9. AI agents in manufacturing

Manufacturers use AI agents to keep production lines steady and efficient. These agents proactively monitor equipment to predict when machines need maintenance, helping factories run more smoothly. They also optimize supply chains by predicting demand and automating inventory management. The result is a more agile, productive manufacturing operation.

10. AI agents in marketing and content creation

Marketers love AI agents because they take the guesswork out of content and campaign planning. Agents can integrate brand specifications with target market analytics to generate relevant, on-brand content at scale. This allows marketers to test ideas faster and personalize outreach more effectively.

11. AI agents in smart homes

Smart home technology like voice-activated assistants and robot vacuums already makes daily life more convenient. Bringing AI agents into the mix takes convenience a step further. Instead of just responding to commands, these agents anticipate your needs and coordinate with one another to create a living space that practically runs itself — just the way you like it.

3 benefits of using AI agents

Integrating AI into your operations can transform how you work. Here’s how the best AI agents make an impact:

1. Boosted productivity

AI agents automate the mundane and time-consuming tasks that drain your resources. By handling activities like data entry, scheduling, and basic customer inquiries for you, these bots give you more time and energy for creative work that requires human ingenuity. The shift to automation speeds up workflows and minimizes the risk of errors.

2. Better customer service

Good customer service is key to satisfaction, but you don’t always have the time and energy to handle inquiries effectively. Operating around the clock, AI agents handle a range of customer needs — from answering questions to providing tailor-made recommendations. This constant availability gives people prompt support, reducing wait times and enhancing satisfaction. 

Plus, AI agents can manage a high volume of clients simultaneously, freeing up time for humans to address more complex or sensitive problems.

3. Data-driven insights

Knowledge is power — but only if you know how to harness it. Certain AI agents are designed to analyze large amounts of data to create actionable insights. With advanced machine learning algorithms, they process and interpret complex information much faster than traditional, manual methods. These capabilities give you a deeper understanding of the data you’re tracking, whether that’s customer behavior or personal efficiencies, to make better decisions and uncover new growth areas.

3 challenges of using AI agents

AI agents are here to stay, but so are their growing pains. Here are three challenges to expect when you incorporate AI technologies into your workflow:

1. Technical difficulties

AI agents require fine-tuning before they’re ready to meet your needs. This might involve customizing the technology to fit specific workflows and ensuring compatibility with current software and apps — steps that take time. Technical hiccups can disrupt operations or require specialized, ongoing support, making AI’s initial adoption both challenging and resource-intensive. Plus, AI is always evolving, which means ongoing training is a must if you want to take full advantage of an agent’s capabilities.

2. Poor computing resources

If you need AI agents to perform complex tasks or handle large volumes of data, your current computer infrastructure might not have the ability. As a smaller organization or single-owner business, it’s often difficult (and expensive) to get the necessary space and equipment to support advanced systems.

3. Ethical bias

Even the most high-tech machine learning technologies aren't truly autonomous. They make decisions with the datasets they’re given, and that means AI agents can unintentionally perpetuate biases present in the data they’re trained with. 

Without careful design and regular monitoring, AI agents could make decisions or recommendations that are unethical or incorrect. Keeping tabs on agents’ outputs is an ongoing challenge, requiring software and skills not everyone has.

Elevate your productivity with Otter

Save time and increase productivity with an AI Agent to take and share meeting notes. Otter released its AI Agent for Meetings. Otter Meeting Agent can automatically join virtual meetings, participate and answer questions, record and transcribe the meeting notes, and generate an automated summary with action items so you and your team can focus on the important stuff. Otter will even share the notes via Slack or email. You worry about brainstorming big ideas, and Otter will make sure it’s all noted.