AI Agents: The Next Frontier in Artificial Intelligence

Agents are the 'third wave' of the AI revolution

Marc Benioff, CEO of Salesforce, calls agentic AI the "third wave" in the rapid evolution of the field. "In just a few years, we've already witnessed three generations of AI," he observed in a recent piece in the New York Times. "First came predictive models that analyze data. Next came generative AI, driven by deep-learning models like ChatGPT. Now, we are experiencing a third wave -- one defined by intelligent agents that can autonomously handle complex tasks." Also: I'm an AI tools expert, and these are the only two I pay for AI agents, or intelligent assistants, are intended to serve as digital co-workers, assistants, or customer service representatives, communicating via natural language processing. They "have the potential to augment human capabilities in ways previously unimaginable," Benioff observed. "Imagine a world where businesses can deploy an AI workforce of agents to manage customer interactions, analyze data, optimize sales strategies and execute operational tasks in real time and with little human supervision." Across the industry, there is agreement that AI agents, with their narrow focus, bring new capabilities and ROI that wider AI cannot deliver effectively. "Agentic AI will be the next wave of unlocked value at scale," Sesh Iyer, managing director and senior partner with BCG X, Boston Consulting Group's tech build and design unit, told ZDNET. Also: We're not ready to support autonomous AI agents, survey suggests He added that this is "an opportunity to redesign processes fundamentally and unlock significant productivity gains." As with both analytical and gen AI, AI agents need to be built with and run along clear ethical and operational guidelines. This includes testing to minimize errors and a governance structure. As is the case with all AI instances, due diligence to ensure compliance and fairness is also a necessity for agents, Iyer said. As is also the case with broader AI, the right skills are needed to design, build and manage AI agents, he continued. Such talent is likely already available within many organizations, with the domain knowledge needed, he added. "Upskill your workforce to manage and use agentic AI effectively. Developing internal expertise will be key to capturing long-term value from these systems." There are notable differences between generative AI and agentic AI as well. "Agentic AI is specifically designed to make decisions autonomously, often without human intervention, which differs from how gen AI is typically used," said David Brault, an expert at Mendix. There are a number of features and functions that separate agentic AI from gen AI, he noted, starting with context and focus. Also: 25% of enterprises using AI will deploy AI agents by 2025 While generative AI applications can be targeted across many capabilities and industries, agentic AI "is focused on specific environments and contextual situations," he added. Accordingly, agentic AI's current best use cases are "predictable and defined tasks with low risk of errors or low severity of impact when errors occur," agreed Michael Connell, chief operating officer at Enthought. In addition, integrating agentic AI with existing systems differs that that of generative AI. "Leveraging the decision-making capabilities of agentic AI often requires modifications to existing systems and integrating with existing APIs to utilize established business logic to improve decision accuracy," Brault said. To prepare for the shift from gen AI to agentic AI, "start small and scale strategically," he advises. "Identify a few high-impact use cases -- such as customer service -- and run pilot programs to test and refine agent capabilities. Alongside these use cases, understand the emerging platforms and software components that offer support for agentic AI." Also: What is Google's Project Mariner? This AI agent can navigate the web for you This includes looking beyond the technology and focusing on the user journey and associated workflows, Iyer urged. "Instead of grounding efforts solely in the technology, think holistically about the workflows agents will transform. Aim to reduce mundane tasks, improve productivity, and create better human-machine collaboration." "The challenge is applying agentic AI in the enterprise setting or in innovation-driven industries, like materials science R&D or pharma, where there is higher uncertainty and risk," said Connell. "These more complex environments require a very nuanced understanding by the agent in order to make trustworthy, reliable decisions." Also: What is Google's Project Mariner? This AI agent can navigate the web for you As with analytical and gen AI, data -- particularly real-time data -- is at the core of agentic AI success. It's important "to have an understanding of how agentic AI will be used and the data that is powering the agent, as well as a system for testing," said Connell. "To build AI agents, you need clean and, for some applications, labeled data that accurately represents the problem domain, along with sufficient volume to train and validate your models." Connell added that a growing reliance on agents "will necessitate new supervisory frameworks, especially in high-stakes fields where traditional oversight models will be inadequate." This means human oversight is always needed -- especially with a risk of unintended consequences if agents are misapplied.

What is an AI agent? A computer scientist explains the next wave of artificial intelligence tools

Interacting with AI chatbots like ChatGPT can be fun and sometimes useful, but the next level of everyday AI goes beyond answering questions: AI agents carry out tasks for you. Major technology companies, including OpenAI, Microsoft, Google and Salesforce, have recently released or announced plans to develop and release AI agents. They claim these innovations will bring newfound efficiency to technical and administrative processes underlying systems used in health care, robotics, gaming and other businesses. Simple AI agents can be taught to reply to standard questions sent over email. More advanced ones can book airline and hotel tickets for transcontinental business trips. Google recently demonstrated Project Mariner to reporters, a browser extension for Chrome that can reason about the text and images on your screen. In the demonstration, the agent helped plan a meal by adding items to a shopping cart on a grocery chain's website, even finding substitutes when certain ingredients were not available. A person still needs to be involved to finalize the purchase, but the agent can be instructed to take all of the necessary steps up to that point. In a sense, you are an agent. You take actions in your world every day in response to things that you see, hear and feel. But what exactly is an AI agent? As a computer scientist, I offer this definition: AI agents are technological tools that can learn a lot about a given environment, and then -- with a few simple prompts from a human -- work to solve problems or perform specific tasks in that environment. Rules and goals A smart thermostat is an example of a very simple agent. Its ability to perceive its environment is limited to a thermometer that tells it the temperature. When the temperature in a room dips below a certain level, the smart thermostat responds by turning up the heat. A familiar predecessor to today's AI agents is the Roomba. The robot vacuum cleaner learns the shape of a carpeted living room, for instance, and how much dirt is on the carpet. Then it takes action based on that information. After a few minutes, the carpet is clean. The smart thermostat is an example of what AI researchers call a simple reflex agent. It makes decisions, but those decisions are simple and based only on what the agent perceives in that moment. The robot vacuum is a goal-based agent with a singular goal: clean all of the floor that it can access. The decisions it makes -- when to turn, when to raise or lower brushes, when to return to its charging base -- are all in service of that goal. A goal-based agent is successful merely by achieving its goal through whatever means are required. Goals can be achieved in a variety of ways, however, some of which could be more or less desirable than others. Many of today's AI agents are utility based, meaning they give more consideration to how to achieve their goals. They weigh the risks and benefits of each possible approach before deciding how to proceed. They are also capable of considering goals that conflict with each other and deciding which one is more important to achieve. They go beyond goal-based agents by selecting actions that consider their users' unique preferences. Making decisions, taking action When technology companies refer to AI agents, they aren't talking about chatbots or large language models like ChatGPT. Though chatbots that provide basic customer service on a website technically are AI agents, their perceptions and actions are limited. Chatbot agents can perceive the words that a user types, but the only action they can take is to reply with text that hopefully offers the user a correct or informative response. The AI agents that AI companies refer to are significant advances over large language models like ChatGPT because they possess the ability to take actions on behalf of the people and companies who use them. OpenAI says agents will soon become tools that people or businesses will leave running independently for days or weeks at a time, with no need to check on their progress or results. Researchers at OpenAI and Google DeepMind say agents are another step on the path to artificial general intelligence or "strong" AI -- that is, AI that exceeds human capabilities in a wide variety of domains and tasks. The AI systems that people use today are considered narrow AI or "weak" AI. A system might be skilled in one domain -- chess, perhaps -- but if thrown into a game of checkers, the same AI would have no idea how to function because its skills wouldn't translate. An artificial general intelligence system would be better able to transfer its skills from one domain to another, even if it had never seen the new domain before. Worth the risks? Are AI agents poised to revolutionize the way humans work? This will depend on whether technology companies can prove that agents are equipped not only to perform the tasks assigned to them, but also to work through new challenges and unexpected obstacles when they arise. Uptake of AI agents will also depend on people's willingness to give them access to potentially sensitive data: Depending on what your agent is meant to do, it might need access to your internet browser, your email, your calendar and other apps or systems that are relevant for a given assignment. As these tools become more common, people will need to consider how much of their data they want to share with them. A breach of an AI agent's system could cause private information about your life and finances to fall into the wrong hands. Are you OK taking these risks if it means that agents can save you some work? What happens when AI agents make a poor choice, or a choice that its user would disagree with? Currently, developers of AI agents are keeping humans in the loop, making sure people have an opportunity to check an agent's work before any final decisions are made. In the Project Mariner example, Google won't let the agent carry out the final purchase or accept the site's terms of service agreement. By keeping you in the loop, the systems give you the opportunity to back out of any choices made by the agent that you don't approve. Like any other AI system, an AI agent is subject to biases. These biases can come from the data that the agent is initially trained on, the algorithm itself, or in how the output of the agent is used. Keeping humans in the loop is one method to reduce bias by ensuring that decisions are reviewed by people before being carried out. The answers to these questions will likely determine how popular AI agents become, and depend on how much AI companies can improve their agents once people begin to use them.

What is an AI agent? A computer scientist explains the next wave of artificial intelligence tools

Quinnipiac University provides funding as a member of The Conversation US. Interacting with AI chatbots like ChatGPT can be fun and sometimes useful, but the next level of everyday AI goes beyond answering questions: AI agents carry out tasks for you. Major technology companies, including OpenAI, Microsoft, Google and Salesforce, have recently released or announced plans to develop and release AI agents. They claim these innovations will bring newfound efficiency to technical and administrative processes underlying systems used in health care, robotics, gaming and other businesses. Simple AI agents can be taught to reply to standard questions sent over email. More advanced ones can book airline and hotel tickets for transcontinental business trips. Google recently demonstrated Project Mariner to reporters, a browser extension for Chrome that can reason about the text and images on your screen. In the demonstration, the agent helped plan a meal by adding items to a shopping cart on a grocery chain's website, even finding substitutes when certain ingredients were not available. A person still needs to be involved to finalize the purchase, but the agent can be instructed to take all of the necessary steps up to that point. In a sense, you are an agent. You take actions in your world every day in response to things that you see, hear and feel. But what exactly is an AI agent? As a computer scientist, I offer this definition: AI agents are technological tools that can learn a lot about a given environment, and then - with a few simple prompts from a human - work to solve problems or perform specific tasks in that environment. Rules and goals A smart thermostat is an example of a very simple agent. Its ability to perceive its environment is limited to a thermometer that tells it the temperature. When the temperature in a room dips below a certain level, the smart thermostat responds by turning up the heat. A familiar predecessor to today's AI agents is the Roomba. The robot vacuum cleaner learns the shape of a carpeted living room, for instance, and how much dirt is on the carpet. Then it takes action based on that information. After a few minutes, the carpet is clean. The smart thermostat is an example of what AI researchers call a simple reflex agent. It makes decisions, but those decisions are simple and based only on what the agent perceives in that moment. The robot vacuum is a goal-based agent with a singular goal: clean all of the floor that it can access. The decisions it makes - when to turn, when to raise or lower brushes, when to return to its charging base - are all in service of that goal. A goal-based agent is successful merely by achieving its goal through whatever means are required. Goals can be achieved in a variety of ways, however, some of which could be more or less desirable than others. Many of today's AI agents are utility based, meaning they give more consideration to how to achieve their goals. They weigh the risks and benefits of each possible approach before deciding how to proceed. They are also capable of considering goals that conflict with each other and deciding which one is more important to achieve. They go beyond goal-based agents by selecting actions that consider their users' unique preferences. Making decisions, taking action When technology companies refer to AI agents, they aren't talking about chatbots or large language models like ChatGPT. Though chatbots that provide basic customer service on a website technically are AI agents, their perceptions and actions are limited. Chatbot agents can perceive the words that a user types, but the only action they can take is to reply with text that hopefully offers the user a correct or informative response. The AI agents that AI companies refer to are significant advances over large language models like ChatGPT because they possess the ability to take actions on behalf of the people and companies who use them. OpenAI says agents will soon become tools that people or businesses will leave running independently for days or weeks at a time, with no need to check on their progress or results. Researchers at OpenAI and Google DeepMind say agents are another step on the path to artificial general intelligence or "strong" AI - that is, AI that exceeds human capabilities in a wide variety of domains and tasks. The AI systems that people use today are considered narrow AI or "weak" AI. A system might be skilled in one domain - chess, perhaps - but if thrown into a game of checkers, the same AI would have no idea how to function because its skills wouldn't translate. An artificial general intelligence system would be better able to transfer its skills from one domain to another, even if it had never seen the new domain before. Worth the risks? Are AI agents poised to revolutionize the way humans work? This will depend on whether technology companies can prove that agents are equipped not only to perform the tasks assigned to them, but also to work through new challenges and unexpected obstacles when they arise. Uptake of AI agents will also depend on people's willingness to give them access to potentially sensitive data: Depending on what your agent is meant to do, it might need access to your internet browser, your email, your calendar and other apps or systems that are relevant for a given assignment. As these tools become more common, people will need to consider how much of their data they want to share with them. A breach of an AI agent's system could cause private information about your life and finances to fall into the wrong hands. Are you OK taking these risks if it means that agents can save you some work? What happens when AI agents make a poor choice, or a choice that its user would disagree with? Currently, developers of AI agents are keeping humans in the loop, making sure people have an opportunity to check an agent's work before any final decisions are made. In the Project Mariner example, Google won't let the agent carry out the final purchase or accept the site's terms of service agreement. By keeping you in the loop, the systems give you the opportunity to back out of any choices made by the agent that you don't approve. Like any other AI system, an AI agent is subject to biases. These biases can come from the data that the agent is initially trained on, the algorithm itself, or in how the output of the agent is used. Keeping humans in the loop is one method to reduce bias by ensuring that decisions are reviewed by people before being carried out. The answers to these questions will likely determine how popular AI agents become, and depend on how much AI companies can improve their agents once people begin to use them.

What exactly is an AI agent? | TechCrunch

AI agents are supposed to be the next big thing in AI, but there isn't an exact definition of what they are. To this point, people can't agree on what exactly constitutes an AI agent. At its simplest, an AI agent is best described as AI-fueled software that does a series of jobs for you that a human customer service agent, HR person or IT help desk employee might have done in the past, although it could ultimately involve any task. You ask it to do things, and it does them for you, sometimes crossing multiple systems and going well beyond simply answering questions. For example, Perplexity last month released an AI agent that helps people do their holiday shopping (and it's not the only one). And Google last week announced its first AI agent, called Project Mariner, which can be used to find flights and hotels, shop for household items, find recipes, and other tasks. Seems simple enough, right? Yet it is complicated by a lack of clarity. Even among the tech giants, there isn't a consensus. Google sees them as task-based assistants depending on the job: coding help for developers; helping marketers create a color scheme; assisting an IT pro in tracking down an issue by querying log data. For Asana, an agent may act like an extra employee, taking care of assigned tasks like any good co-worker. Sierra, a startup founded by former Salesforce co-CEO Bret Taylor and Google vet Clay Bavor, sees agents as customer experience tools, helping people achieve actions that go well beyond the chatbots of yesteryear to help solve more complex sets of problems. This lack of a cohesive definition does leave room for confusion over exactly what these things are going to do, but regardless of how they're defined, the agents are for helping complete tasks in an automated way with as little human interaction as possible. Rudina Seseri, founder and managing partner at Glasswing Ventures, says it's early days and that could account for the lack of agreement. "There is no single definition of what an 'AI agent' is. However, the most frequent view is that an agent is an intelligent software system designed to perceive its environment, reason about it, make decisions, and take actions to achieve specific objectives autonomously," Seseri told TechCrunch. She says they use a number of AI technologies to make that happen. "These systems incorporate various AI/ML techniques such as natural language processing, machine learning, and computer vision to operate in dynamic domains, autonomously or alongside other agents and human users." Aaron Levie, co-founder and CEO at Box, says that over time, as AI becomes more capable, AI agents will be able to do much more on behalf of humans, and there are already dynamics at play that will drive that evolution. "With AI agents, there are multiple components to a self-reinforcing flywheel that will serve to dramatically improve what AI Agents can accomplish in the near and long-term: GPU price/performance, model efficiency, model quality and intelligence, AI frameworks and infrastructure improvements," Levie wrote on LinkedIn recently. That's an optimistic take on the technology that assumes growth will happen in all these areas, when that's not necessarily a given. MIT robotics pioneer Rodney Brooks pointed out in a recent TechCrunch interview that AI has to deal with much tougher problems than most technology, and it won't necessarily grow in the same rapid way as, say, chips under Moore's law have. "When a human sees an AI system perform a task, they immediately generalize it to things that are similar and make an estimate of the competence of the AI system; not just the performance on that, but the competence around that," Brooks said during that interview. "And they're usually very over-optimistic, and that's because they use a model of a person's performance on a task." The problem is that crossing systems is hard, and this is complicated by the fact that some legacy systems lack basic API access. While we are seeing steady improvements that Levie alluded to, getting software to access multiple systems while solving problems it may encounter along the way could prove more challenging than many think. If that's the case, everyone could be overestimating what AI agents should be able to do. David Cushman, a research leader at HFS Research, sees the current crop of bots more like Asana does: assistants that help humans complete certain tasks in the interest of achieving some sort of user-defined strategic goal. The challenge is helping a machine handle contingencies in a truly automated way, and we are clearly not anywhere close to that yet. "I think it's the next step," he said. "It's where AI is operating independently and effectively at scale. So this is where humans set the guidelines, the guardrails, and apply multiple technologies to take the human out of the loop -- when everything has been about keeping the human in the loop with GenAI," he said. So the key here, he said, is to let the AI agent take over and apply true automation. Jon Turow, a partner at Madrona Ventures, says this is going to require the creation of an AI agent infrastructure, a tech stack designed specifically for creating the agents (however you define them). In a recent blog post, Turow outlined examples of AI agents currently working in the wild and how they are being built today. In Turow's view, the growing proliferation of AI agents -- and he admits, too, that the definition is still a bit elusive -- requires a tech stack like any other technology. "All of this means that our industry has work to do to build infrastructure that supports AI agents and the applications that rely upon them," he wrote in the piece. "Over time, reasoning will gradually improve, frontier models will come to steer more of the workflows, and developers will want to focus on product and data -- the things that differentiate them. They want the underlying platform to 'just work' with scale, performance, and reliability." One other thing to keep in mind here is that it's probably going to take multiple models, rather than a single LLM, to make agents work, and this makes sense if you think about these agents as a collection of different tasks. "I don't think right now any single large language model, at least publicly available, monolithic large language model, is able to handle agentic tasks. I don't think that they can yet do the multi-step reasoning that would really make me excited about an agentic future. I think we're getting closer, but it's just not there yet," said Fred Havemeyer, head of U.S. AI and software research at Macquarie US Equity Research. "I do think the most effective agents will likely be multiple collections of multiple different models with a routing layer that sends requests or prompts to the most effective agent and model. And I think it would be kind of like an interesting [automated] supervisor, delegating kind of role." Ultimately for Havemeyer, the industry is working toward this goal of agents operating independently. "As I'm thinking about the future of agents, I want to see and I'm hoping to see agents that are truly autonomous and able to take abstract goals and then reason out all the individual steps in between completely independently," he told TechCrunch. But the fact is that we are still in a period of transition where these agents are concerned, and we don't know when we'll get to this end state that Havemeyer described. While what we've seen so far is clearly a promising step in the right direction, we still need some advances and breakthroughs for AI agents to operate as they are being envisioned today. And it's important to understand that we aren't there yet.

We've come a long way from RPA: How AI agents are revolutionizing automation

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More In the past year, the race to automate has intensified, with AI agents emerging as the ultimate game-changers for enterprise efficiency. While generative AI tools have made significant strides over the past three years -- acting as valuable assistants in enterprise workflows -- the spotlight is now shifting to AI agents capable of thinking, acting and collaborating autonomously. For enterprises preparing to embrace the next wave of intelligent automation, understanding the leap from chatbots to retrieval-augmented generation (RAG) applications to autonomous multi-agent AI is crucial. As Gartner noted in a recent survey, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024. As Google Brain founder Andrew Ng aptly stated: "The set of tasks that AI can do will expand dramatically because of agentic workflows." This marks a paradigm shift in how organizations view the potential of automation, moving beyond predefined processes to dynamic, intelligent workflows. The limitations of traditional automation Despite their promise, traditional automation tools are constrained by rigidity and high implementation costs. Over the past decade, robotic process automation (RPA) platforms like UiPath and Automation Anywhere have struggled with workflows lacking clear processes or relying on unstructured data. These tools mimic human actions but often lead to brittle systems that require costly vendor intervention when processes change. Current gen AI tools, such as ChatGPT and Claude, have advanced reasoning and content generation capabilities but fall short of autonomous execution. Their dependency on human input for complex workflows introduces bottlenecks, limiting efficiency gains and scalability. The emergence of vertical AI agents As the AI ecosystem evolves, a significant shift is occurring toward vertical AI agents -- highly specialized AI systems designed for specific industries or use cases. As Microsoft founder Bill Gates said in a recent blog post: "Agents are smarter. They're proactive -- capable of making suggestions before you ask for them. They accomplish tasks across applications. They improve over time because they remember your activities and recognize intent and patterns in your behavior. " Unlike traditional software-as-a-service (SaaS) models, vertical AI agents do more than optimize existing workflows; they reimagine them entirely, bringing new possibilities to life. Here's what makes vertical AI agents the next big thing in enterprise automation: Evolution from RPA to multi-agent AI The most profound shift in the automation landscape is the transition from RPA to multi-agent AI systems capable of autonomous decision-making and collaboration. According to a recent Gartner survey, this shift will enable 15% of day-to-day work decisions to be made autonomously by 2028. These agents are evolving from simple tools into true collaborators, transforming enterprise workflows and systems. This reimagination is happening at multiple levels: Future outlook: As agents gain better memory, advanced orchestration capabilities and enhanced reasoning, they will seamlessly manage complex workflows with minimal human intervention, redefining enterprise automation. The accuracy imperative and economic considerations As AI agents progress from handling tasks to managing workflows and entire jobs, they face a compounding accuracy challenge. Each additional step introduces potential errors, multiplying and degrading overall performance. Geoffrey Hinton, a leading figure in deep learning, warns: "We should not be afraid of machines thinking; we should be afraid of machines acting without thinking." This highlights the critical need for robust evaluation frameworks to ensure high accuracy in automated processes. Case in point: An AI agent with 85% accuracy in executing a single task achieves only 72% overall accuracy when performing two tasks (0.85 × 0.85). As tasks combine into workflows and jobs, accuracy drops further. This leads to a critical question: Is deploying an AI solution that's only 72% correct in production acceptable? What happens when accuracy declines as more tasks are added? Addressing the accuracy challenge Optimizing AI applications to reach 90 to 100% accuracy is essential. Enterprises cannot afford subpar solutions. To achieve high accuracy, organizations must invest in: Without strong evaluation, observability, and feedback, AI agents risk underperforming and falling behind competitors who prioritize these aspects. Lessons learned so far As organizations update their AI roadmaps, several lessons have emerged: Conclusion AI agents are here as our coworkers. From agentic RAG to fully autonomous systems, these agents are poised to redefine enterprise operations. Organizations that embrace this paradigm shift will unlock unparalleled efficiency and innovation. Now is the time to act. Are you ready to lead the charge into the future?

Unlock the Power of AI Agents to Scale Your Business

Artificial Intelligence (AI) agents are transforming task automation and workflow management by handling complex, multi-step processes with minimal human involvement. These autonomous systems are indispensable for businesses and individuals seeking greater efficiency and scalability. Using advanced technologies such as contextual understanding, vector databases, and modular design, AI agents are becoming increasingly sophisticated. This article provides insights into their components, capabilities, challenges, and future potential, helping you integrate them effectively into your workflows. AI agents go beyond traditional assistants by proactively managing tasks, making precise decisions, and streamlining operations. These systems are designed to work autonomously, enabling business leaders and individuals to simplify their processes and focus on higher-value activities. To navigate the world of AI agents, it's important to understand their core components, key challenges, and practical applications. This guide by Nate Herk breaks down essential concepts, demystifies technical jargon, and equips you with the knowledge needed to build scalable, autonomous systems that enhance productivity and collaboration. Understanding the distinction between AI agents and AI assistants is crucial to appreciating their unique potential. While AI assistants are primarily reactive, requiring explicit user commands to perform tasks, AI agents operate autonomously. They can make decisions, execute tasks, and adapt to new information without constant supervision, allowing them to handle more complex and dynamic workflows. For example: This autonomy allows AI agents to excel in scenarios where adaptability and independent decision-making are essential, making them a powerful tool for managing intricate processes. AI agents are built on a foundation of interconnected components, each playing a vital role in their functionality. These components ensure the agent's ability to process information, make decisions, and execute tasks effectively: These components work in harmony, allowing AI agents to adapt to diverse scenarios and deliver efficient, context-aware solutions. Stay informed about the latest in AI Agents by exploring our other resources and articles. AI agents bring a range of advanced capabilities to the table, allowing them to outperform traditional automation systems in many areas. Their key strengths include: For instance, in project management, an AI agent could coordinate team activities, allocate resources, and monitor progress, all while adapting to shifting priorities and unforeseen challenges. The effectiveness of AI agents hinges on the quality and structure of the data they process. High-quality, well-organized data ensures accurate decision-making and task execution. Contextual understanding further enhances an agent's ability to interpret and respond to complex scenarios. Technologies such as vector databases and retrieval-augmented generation (RAG) play a pivotal role in allowing efficient data storage and retrieval, allowing agents to access relevant information precisely when it is needed. By combining robust data pipelines with advanced contextual analysis, AI agents can deliver more accurate and meaningful results, making them invaluable for data-intensive applications. Developing a scalable and efficient AI agent requires a structured approach. Key steps in the development process include: For example, in customer relationship management (CRM), an AI agent could analyze customer interactions, predict future needs, and suggest personalized engagement strategies, all while learning and improving over time. The architecture of an AI agent's workflow plays a critical role in determining its performance and adaptability. Two common approaches are: Selecting the right workflow architecture depends on the specific requirements of your application, as well as the complexity of the tasks the agent will handle. Crafting effective prompts is essential for guiding AI agents and optimizing their performance. To create impactful prompts, consider the following strategies: For instance, when designing a prompt for a project management tool, you might include details about deadlines, team roles, and resource constraints to ensure the agent prioritizes tasks effectively and delivers actionable insights. Despite their potential, building and deploying AI agents comes with several challenges. Common obstacles include: Addressing these challenges requires robust data pipelines, thoughtful design, and continuous monitoring to ensure the agent operates effectively and adapts to evolving needs. AI agents are poised to become even more powerful and accessible in the coming years. Emerging trends shaping their future include: These advancements promise to transform industries, driving innovation and efficiency to new heights. AI agents are transforming the way tasks are automated and workflows are managed, offering unparalleled efficiency, adaptability, and scalability. By understanding their components, capabilities, and challenges, you can harness their full potential to streamline processes and drive innovation. Early adoption and experimentation with AI agents can position you as a leader in this rapidly evolving field, paving the way for greater success and operational excellence.

What are the risks and benefits of 'AI agents'?

Here's what you need to know about harnessing the benefits of AI agents, while mitigating the risks. In the rapidly evolving landscape of artificial intelligence, a new frontier is emerging that promises to revolutionize the way we work and interact with technology. Imagine you're taking a break from work, driving to your vacation destination. The weather turns, which lowers the ambient temperature, and increases the volume of traffic. Your car's infotainment system has already assessed this new data, so it turns up the heating, reroutes you to quieter roads and suggests a lunch spot where you can sit out the storm. By 2027, Deloitte predicts that half of companies that use generative AI will have launched agentic AI pilots or proofs of concept that will be capable of acting as smart assistants, performing complex tasks with minimal human supervision. On the verge of this technological leap, it's important to understand what AI agents are, their potential impact, and how to navigate the associated risks. In collaboration with Capgemini, the World Economic Forum has published a new white paper, Navigating the AI Frontier: A Primer on the Evolution and Impact of AI Agents, which explores the capabilities and implications of AI agents, to provide stakeholders with a better understanding of how these systems can drive meaningful progress across sectors. AI agents, or 'agentic AI', are autonomous systems that sense and act upon their environment to achieve goals. A slew of tech companies are developing AI agents and they're poised to transform industries and redefine productivity. Microsoft, IBM, and others have made significant strides, while OpenAI is set to launch an AI agent codenamed 'Operator' in January, that will have agency to perform tasks such as coding and booking travel. Cognition Software launched 'Devin' in March 2024, an autonomous software engineer capable of performing complex programming tasks based on natural language prompts. Investors have recognized the potential of AI agents, pouring more than $2 billion into start-ups focusing on enterprise applications in the past two years. This influx of capital and innovation is driving rapid advancements in the technology. An AI agent is effectively comprised of several core components: The control centre of advanced AI agents manages the flow of information between user inputs, decision-making and planning, memory management, access to tools and the effectors of the system, enabling action in digital or physical environments. As the technology evolves, we can expect to see more sophisticated multi-agent systems (MAS) capable of distributing tasks and collaborating on complex problems. For example, in a smart city, a multi-agent system (MAS) would manage traffic flow in real time, using vehicle-to-everything (V2X) communication, enabling vehicles to interact with other vehicles, pedestrians and road infrastructure. We will also see greater integration of multimodal data analysis, allowing agents to interpret and produce various types of data, including text, voice and video, as well as industry-specific applications tailored to unique sector needs. As AI agents become more capable at reasoning, planning and self-checking, they will be able to carry out tasks beyond the skill sets of users, such as specialized coding, or take on more tedious tasks quickly and at scale. In a world of talent scarcity, AI agents can help to close skills gaps in various industries, where human expertise is lacking or in high demand. Greater autonomy allows AI agents to tackle open-ended, real-world challenges from scientific discovery, to improving supply chain efficiency and enabling physical robots that can manipulate objects and navigate physical environments, according to Navigating the AI Frontier. The report includes five real-world applications of AI agents in sectors from software development and education, to finance, customer service and healthcare, such as enhancing fraud detection, personalizing learning and improving diagnostics and patient treatment. Despite their potential, AI agents pose certain risks around technical limitations, ethical concerns and broader societal impacts associated with a system's level of autonomy and the overall potential of its use when humans are removed from the loop. Technical risks include errors and malfunctions and security issues including the potential for automating cyberattacks. The autonomous nature of AI agents raises ethical questions about decision-making and accountability, while there are also socioeconomic risks around potential job displacement and over-reliance and disempowerment. Harnessing the benefits of AI agents while mitigating risks will depend on the context of the specific environment of the agent and its application. These are just a few of the measures that organizations should consider in addressing the risks of AI agents: The rise of AI agents is not just a technological shift; it's a transformation in how we conceptualize work and human-machine collaboration. By understanding the capabilities and limitations of AI agents, and by implementing thoughtful strategies for their deployment, businesses can position themselves to harness the full potential of this groundbreaking technology while mitigating associated risks. As we move forward, it will be crucial to maintain a balance between embracing innovation and ensuring responsible implementation. The future of work is being reshaped by AI agents, and those who adapt wisely will be best positioned to thrive in this new landscape.