What are AI agents? Examples, types, and how they work

Sometimes it’s a relief not to have to do the busy work. It can also be satisfying to be the person who lifts the busy work from your colleagues’ lives, helping everyone achieve goals faster and focus on the work that they’re most passionate about—the work that truly moves the needle. This responsibility typically falls to those in charge of implementing autonomous AI agents and other forms of automation across the various platforms within your tech stack. It’s no small job, but also an important one. 

Fortunately, as technology advances, building and deploying agents to address your company’s needs is becoming easier, sometimes even requiring no technical expertise. A few advanced platforms offer no-code components that leverage conversational AI to deploy a team of dynamic agents that can independently analyze data and orchestrate actions across your organization. In this article, we’ll take a look at what AI agents are, how they can help, and where you can easily deploy them within your teams. 


What is an AI agent?

AI agents are sophisticated software systems used to automate and streamline workflows, and drive decisions by providing rapid insights. Intelligent AI agents function autonomously and proactively, making decisions and performing tasks without human approval—but only according to the boundaries and oversight initially set by humans. Then, not unlike us, they continue to learn and improve over time, remembering context across sessions and interaction history.

Today, software platforms can natively embed AI agents, providing the opportunity to perform specific tasks at scale—say, to provide web research, analyze documents, or create content using the data and tools that are unique to your business—or even to build your own highly specialized AI agents.

Key features of an AI agent

AI agents can be built in different ways and for different purposes. It’s important to consider the strength of these must-have features across the platforms and use cases you’re considering deploying AI agents. The most effective AI agents require these core capabilities:

What is the difference between AI agents, AI assistants, and bots?

Unless you’re an expert builder, it can be confusing to understand the differences between AI agents, assistants, and bots. Sometimes the terms are used interchangeably, even though there are key differences. Increasingly, many software platforms offer a blend of these AI capabilities. 

AI agents

AI agents act autonomously to achieve specific goals. They can perceive their environment, make decisions, and take actions without constant human direction. For example, an AI agent might be given a high-level objective like "research sales prospects” and then will independently research every sales lead by automatically gathering company information to help identify the leads that are most likely to become opportunities, providing sales teams with the context they need for a successful outreach. (This is only possible with access to the right information.) 

AI assistants

AI assistants are designed to help humans with tasks through conversation and collaboration. They respond to requests, provide information, and can perform actions when asked, but they generally work under human guidance rather than operating independently. Leading AI models like ChatGPT and Claude are common examples of an AI assistant. 

Bots

Bots are automated programs that perform tasks, and many (but not all) use AI. Bots have limited functionality and generally automate simple tasks using predefined rules that the software simply follows. Bots may follow predetermined scripts and use if-then logic to determine a next step.

As technology advances, the line between these categories may continue to blur, but the main differences come down to how independently they can operate, how sophisticated their reasoning is, and what they’re designed to accomplish.

How do AI agents work?

How an AI agent works depends on the underlying architecture and infrastructure, though a central component are the large language models (LLMs) that allow for natural language processing. In practice, AI agents typically work through a process of perception, decision-making, and action. Once built, here’s how they function:

Types of AI agents 

AI intelligent agents fall within a few different categories, depending on what they’re designed to do. If you’re wondering what are the main types of AI agents in use today, here are a few primary types that range from document analysis to image generation.

Document analysis agents

These agents specialize in processing and extracting insights from large quantities of various document types including PDFs, spreadsheets, contracts, and reports. They can identify key information, compare documents, flag anomalies, and generate summaries or analyses. 

Web search agents

These agents automatically gather information from across the internet to answer questions or research a topic. They can query multiple search engines, visit websites, extract relevant information, and synthesize findings from various sources, taking into account source credibility. 

Image generation agents

These agents create visual content based on text descriptions, style references, or other parameters. They can generate artwork, modify existing images, create product mockups, or produce illustrations for specific contexts. Advanced image agents can iterate on designs, maintain consistency across multiple images, and adapt to specific brand guidelines, regions, or artistic styles.

Custom agents

Custom agents are built to address specific parts of your organization’s workflow. These may be tailored to a specific industry, adhere to company processes, or designed to handle specialized tasks. Examples include agents for customer service workflows, inventory management, or industry-specific compliance monitoring.

Benefits of using AI agents

There are many ways to put AI agents to work—and reasons why you should. Here are three:

Increase capacity

AI agents offer always-on availability, allowing you to increase the amount of work that can be done over a shorter period of time. Often built to handle pain points in a workflow, AI agents can rapidly handle specific repetitive tasks at scale, keeping processes moving forward and potentially addressing any bottlenecks that may arise. 

Use real-time insights for decision making

Agents can process large volumes of information quickly, work across multiple systems simultaneously, and, in some cases, even collaborate with (and learn from) other AI agents to take in the most recent data, context, and problem-solve in real time. AI agents can be helpful in scenarios where requests or decisions may be time-sensitive—such as quickly triaging customer feedback.

Maintain consistency

By automating repetitive tasks, AI agents help remove the kinds of human error that come from being tired or distracted, or missing a small detail. What an agent has worked on is also easily tracked in audit trails, so you can review and ensure compliance. 

Challenges with using AI agents

Of course, as with any technology, you may run into a few challenges when implementing AI agents, especially for the first time. These can include:

Accuracy and reliability

While AI agents don’t have bad days or sleepless nights, they can make mistakes or operate outside intended parameters, particularly if you (or the vendors you work with) haven’t set guardrails or safeguards. 

How to overcome: Begin with low-risk use cases and gradually expand agent usage. Ensure that you have a plan for regularly monitoring performance, and create workflows that keep humans in the loop for anything that you define as a critical business decision.

Security and privacy risks

Data privacy, security vulnerabilities, and the potential for agents to perpetuate biases present in their training data are all concerns when it comes to any AI technology, but especially when deploying AI agents to work with sensitive information.

How to overcome: Implement robust data governance policies that define what information AI agents can access. Work with IT security teams to ensure agents meet your organization's security standards, and be sure to perform regular audits of data usage. Even better, look for a vendor that offers secure AI agent solutions right out of the box so that you can begin your journey feeling confident about data security. 

Integration and dependency challenges

Integration with existing systems can be complex and time-consuming to manage. Additionally, over-reliance on agents can introduce operational issues in the event that systems fail or need maintenance. 

How to overcome: Work with your IT team to vet solutions and plan integrations. Ideally, a platform will offer native integrations with AI agents. As you get started, consider running a pilot program or implement a phased rollout. Maintain human expertise by having staff understand what agents do and how to intervene when needed, and document processes to ensure that knowledge isn’t lost. 

Team buy-in

Some teams or leaders may be resistant to using AI agents, fearing job replacement.

How to overcome: It helps to frame AI agents as an opportunity for humans to learn new AI skills and to focus on the benefits of AI, which include finding more time for higher-value or more fulfilling work, and keeping skillsets competitive. Invite your team to help select new platforms, or offer training and incentives to adopt AI agents.

AI agent best practices

Here are a few best practices to lean on as you get started with AI agents:

AI agent use cases 

If you’re wondering what are some real-world applications of AI agents, here are a few AI agent business applications that can make a meaningful difference and create efficiencies across key areas of your organization’s workflows: 

Marketing and content

Operations and project management

Product management

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