What Are AI Agents? Key Things to Know
What are AI agents? Uncover the essential details, from their core features to applications in automation, business, and beyond.
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What are AI agents? That is the question that many are talking about as companies increasingly consider rapidly evolving AI developments in the context of the workplace.
AI companies are highlighting the possibilities of AI agents, and considering the continuous updates to the AI industry, including AI agentic technology, the concept is now becoming much more conceivable in certain industries.
But what are AI agents? Here, we will break down everything you need to know about what AI agents are, why they’re becoming increasingly popular, and review concerns or limitations around the technology.
Key Takeaways (TL;DR)
- AI agents are already within certain industries, and consumers should expect to see them appear more and more in the coming years.
- In early 2025, OpenAI released Operator, a Computer Using AI agent (CUA) in research preview.
- AI agents are generally seen as an excellent cost-saving solution to help businesses reduce costs and increase profits (although initial investment in AI agent tech may be cost-intensive).
- There are some concerns and limitations around AI agents, including security, bias, and implications in the workplace.
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What Are AI Agents? A Brief Overview
First, let’s start by defining what we mean by the term ‘AI Agent.’
Google defines AI agents as, a software system. The system uses AI (artificial intelligence) to conduct tasks for users.
In concept, it’s similar to the LLMs (large language models) that many are now familiar with, however, there are some differences.
As Forbes notes, an AI agent is different from an LLM like ChatGPT because ChatGPT needs a human to input instructions or a prompt to produce a result.
That’s why, as Microsoft notes, what makes AI agents stand out is their reasoning and learning capabilities because these typically enable AI agents (depending on the model or type) to “run complex workflows autonomously” or independently.
In their guide, Microsoft further notes that AI agents are typically capable of:
- Planning tasks or actions: powered by LLM integrations they can plan a series of actions.
- Using tools: implement tools like code or search features to complete tasks.
- Processing information: depending on the AI agent they may be able to process environmental information (like visuals or audio).
- Strong memory capacity: use past interactions (like how it used tools) to inform actions.
IBM notes that this makes AI agents a popular choice for tasks such as virtual assistance or customer service.
Recently OpenAI released Operator, an AI agent that is available through a research preview. In particular, it’s a computer-using agent (CUA). This gives it the ability to click buttons on a web page, type, or scroll.
AI Agents are Becoming Increasingly Popular: Here’s Why
One of the top reasons that AI agents are gaining interest is their cost-saving potential. AI agents are designed to automate mundane tasks, speed up processes, and reduce costs, which could result in improved profit margins for businesses. However, Forbes notes that due to the level of capital they require to deploy, initially, they may be cost-intensive.
Then, their potential application for customer service also makes them an intriguing option to support human teams. With the ability to have an automated assistant, businesses could potentially enhance user experience and offer a more seamless and personalized experience.
Take an example of an eCommerce site. Rather than waiting up to 24 or 48 hours to get a response from busy support staff, a customer could receive answers rapidly, in an instant conversation with an AI agent, streamlining the experience.
Then, if additional support was required, the human customer service representative could step in to answer or clarify any remaining questions.
AI Agents: Concerns and Limitations
However, there are concerns and limitations around AI agents that are important to keep in mind.
One of the first concerns is the potential for AI agents to impact the workforce and jobs.
Forbes notes that some jobs could be impacted, others may incorporate the tech as a support to boost staff productivity, and that it may also lead to new jobs opening up, “leaders can instead choose to position the technology as a tool for accelerating market growth or super augmenting your most valuable asset—your people.”
Going back to our eCommerce example, if a company deploys AI agents for customer support, then, the job of the human who once sent email responses may adapt and change (for instance it may focus on reviewing the AI agent's replies to customers) and incorporate the new tech. However, as AI agents are rapidly developing time will tell the full impact.
There is also a concern regarding potential bias or unfair outcomes. If a model is trained on a specific set of data, then it may produce inaccurate answers or biased outputs.
Amazon Web Services, notes that due to this limitation, it’s important to keep a human in the loop as a safety measure to ensure that the outputs are factual and fair.
This can also help to address concerns in a customer experience context, as a human customer service agent would still be available to review the AI agent’s responses and ensure they were in line with company policies.
After all, AI has been known to hallucinate (or present false information as fact) in a number of cases. Read more in our guide on AI hallucinations.
In addition to the potential for inaccurate outputs, other limitations of AI agents include access to technical resources for training and data privacy concerns. So, it’s essential to ensure that if AI agents are incorporated into workflows that proper data security measures are implemented.
Final Thoughts on AI Agents
However, there is cause to be positive about the future of AI Agents. If handled correctly and transparently, AI Agents could free up time for people to work on the tasks they excel at, while AI agents work hard in the background, streamlining manual tasks.
Although time will tell the complete impact of AI agents, one thing is for sure, transparency and clarity are absolutely crucial in the coming weeks, months, and years.
To maintain transparency, check out the Originality.ai AI Detector. Its industry-leading performance and best-in-class Chrome Extension will enable you to publish with confidence in a rapidly evolving digital world.
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Frequently Asked Questions
Tools for conducting a plagiarism check between two documents online are important as it helps to ensure the originality and authenticity of written work. Plagiarism undermines the value of professional and educational institutions, as well as the integrity of the authors who write articles. By checking for plagiarism, you can ensure the work that you produce is original or properly attributed to the original author. This helps prevent the distribution of copied and misrepresented information.
Text comparison is the process of taking two or more pieces of text and comparing them to see if there are any similarities, differences and/or plagiarism. The objective of a text comparison is to see if one of the texts has been copied or paraphrased from another text. This text compare tool for plagiarism check between two documents has been built to help you streamline that process by finding the discrepancies with ease.
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String comparison is a fundamental operation in text comparison tools that involves comparing two sequences of characters to determine if they are identical or not. This comparison can be done at the character level or at a higher level, such as the word or sentence level.
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String comparison is a crucial component of text comparison tools, as it forms the basis for determining the similarities and differences between texts. The results of the string comparison can then be used to generate a report or visual representation of the similarities and differences between the texts.
Syntax highlighting is a feature of text editors and integrated development environments (IDEs) that helps to visually distinguish different elements of a code or markup language. It does this by coloring different elements of the code, such as keywords, variables, functions, and operators, based on a predefined set of rules.
The purpose of syntax highlighting is to make the code easier to read and understand, by drawing attention to the different elements and their structure. For example, keywords may be colored in a different hue to emphasize their importance, while comments or strings may be colored differently to distinguish them from the code itself. This helps to make the code more readable, reducing the cognitive load of the reader and making it easier to identify potential syntax errors.
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