The Rise of AI Agents: Transforming Business Automation Beyond RPA

AI Agents are Basically RPA with LLMs

Every company, big tech or startups alike, is betting on agentic AI becoming the biggest trend in the near future. As we move away from small language models, everyone will be likely to increasingly talk about implementing AI agents in their workflow. Recently, NVIDIA CEO Jensen Huang suggested that all IT departments would evolve into HR for AI agents. Microsoft CEO Satya Nadella similarly highlighted that there would be a swarm of AI agents in the workforce, likening the shift to the rise of Robotic Process Automation (RPA) in recent years. However, it didn't turn out exactly as predicted. It was predicted that RPA would automate most of the mundane jobs, which would allow teams to focus on larger tasks. Several industry experts now predict that AI agents are going through the same phase as RPA. Nikhil Malhotra, chief innovation officer at Tech Mahindra, on an episode of What's the Point with AIM, pointed out that while a lot of startups would be talking about agentic AI this year, most of the tech would just be RPA. "But the good thing about this would be that these startups will start thinking about agentic loops." For instance, when Anthropic released its computer use feature with Claude 3.5 Sonnet, it could move the cursor, click buttons, and type text, as well as fill out forms, navigate websites, and interact with software programmes. This agentic approach has left many wondering about the potential implications for RPA companies and the future of agentic AI and whether it will meet the same fate. "Wasn't RPA the exact same thing without an LLM and it failed miserably" Given the hype around AI agents, the question of their capabilities in the workforce needs to be examined deeply. The current frameworks seem very similar to RPA but with an LLM in the loop. Though that makes a huge difference, adapting them to the workflow still looks like adopting a 10-year-old technology. It is predicted that the $250 billion SaaS market will be replaced by the $300 billion AI agents market as companies adopt AI agents in their workflows. However, given the huge price difference, people are still not convinced if moving away from the current systems to AI agentic ones is worth it. Moreover, all RPA companies are also entering the AI agent race. Apart from Salesforce, companies like UiPath and Automation Anywhere have started leveraging AI agents because they believe that both offerings are different. This means that RPA is now actually being upgraded to agentic AI, and not much has changed. While speaking with AIM, Param Kahlon, EVP and GM of automation and integration at Salesforce, earlier said that autonomous agents also do not mean the end of RPA technology. "RPA agents were designed to automate repetitive, tedious tasks, such as transferring data between systems when APIs aren't involved. In contrast, autonomous agents process information more like humans, adapting to situations and making decisions based on changing conditions, enhancing efficiency and effectiveness in workflows." Ramprakash Ramamoorthy, director of AI research at ManageEngine and Zoho, told AIM that the dispersion around agentic AI systems in enterprise IT is becoming increasingly polarised. He said that for enterprises, the shift from RPA to agentic AI opens a new era of self-directed operations, which enables faster scaling and better responses to evolving business needs. "Agentic AI is more than just RPA with LLMs; it's a transformative evolution that combines automation with intelligent decision-making. While traditional RPA executes predefined tasks, agentic AI learns, reasons, and adapts in real-time, elevating process automation with cognitive flexibility," Ramamoorthy said. "Majority of agentic applications are basically workflow automation with some minimal amount of people interactions," tech YouTuber Shailesh, who runs channel SV Techie, wrote on X. He explained that there might be a reduction in headcount amongst companies, but nothing like the autonomous hype that is being sold. Anil Kumar, CTO at Exotel, told AIM that calling agentic AI just RPA with LLMs is as unconvincing as saying C++ classes are just C structures with methods. While RPA deals with structured data, agentic AI aims to achieve automation by using LLMs to interpret decision trees. Taking the example of complex human conversations such as loan negotiations, Kumar said that they cannot be expressed as a decision tree. "Agentic AI like the one used in our bots will work backwards from the goal given to them (which is to negotiate and disburse loans) and navigate the nuances of human conversation," Kumar said. "They achieve this by carrying the context of the current conversation, learnings from previous conversations, information from a knowledge base, contractual constraints from a legal document, etc. and make decisions towards achieving the given objective." He added that if this is implemented as RPA along with LLM, it will be like "giving a script to a child actor who will fluster on stage if others go off script". Between 2018 and 2023, AI integration into RPA solutions has steadily evolved. This has enhanced RPA's functionality with sophisticated AI capabilities. The true breakthrough, however, brought about the emergence of agentic AI in 2024. Andreessen Horowitz, in its thesis posted in November last year, pointed out that AI will automate operations and eat the world of RPA. The end of traditional RPA is widely discussed in the industry. Deepak Dastrala, CTO and partner at IntellectAI, told AIM that RPA focuses on automating repetitive, rule-based tasks, which makes it a tactical solution. AI agents, on the other hand, take a more goal-based approach and act more like digital twins of humans, powered by LLMs, equipped with memory, and able to adapt and learn in real time. "That's why RPA's relevance has faded, while AI agents are poised to reshape our work at a level no other automation technology has achieved," he said. "The era of cargo cult programming to churn out generic, modular software is dead and buried. In 10 years, RPA and agent studios will be relics of the past. Instead, we'll see specialised agents, each uniquely designed for specific industries to solve problems end-to-end," said Arnav Bathla, CEO of Layerup. Agentic AI can be viewed as RPA 2.0. The rebranding of advanced RPA as agentic AI is often a marketing move to capitalise on AI's hype. Vendors position their products as "intelligent agents" to differentiate them from traditional RPA, despite the underlying functionality being a continuation of process automation. The fundamental objective - automating repetitive tasks for efficiency - remains unchanged. However, the fate of agentic AI might end up the same as that of RPA if it fails to evolve and address its limitations.

Is Your AI 'Agentic', Or Merely 'Agent-ish'?

Jensen Huang says AI agents are a 'multi-trillion-dollar opportunity'. Mark Benioff thinks agents represent 'what AI was meant to be'. And Satya Nadella thinks SaaS is dead. It's 2025, and agents are the only game in town (or so it would seem). The tech industry adores its buzzwords, and 'AI agents' might be the buzziest of them all! While a few vendor platforms are genuinely building agentic features into their roadmaps, others are merely 'agent washing'. I see lots of confusion among Forrester clients - buyers of these technologies - who are trying to sift through all this frenzy to make sense of what agents really are, what they mean to the business, and what their choices are. We are still early enough along the technology maturity cycle that definitions and characteristics can be a bit fluid, but it is generally accepted that AI agents are LLM-based constructs that demonstrate specific design patterns: planning, reflection, collaboration with other agents, and tool use. Underlying these patterns are two foundational building blocks of true 'agentic' capability: You can immediately see that agency and autonomy feed off each other. Together, these traits distinguish true AI agents from their lesser counterparts. If you look carefully at many of the 'agentic' offerings that SaaS products offer, they come across as a mixed bag. You will quickly realize that these 'agents' have limited autonomy, or limited agency, or are limited to such a narrow context-space that you might as well have just used a deterministic workflow or a regular LLM prompt to produce the same outcome. Unfortunately, several of the purported agentic demos that I have seen from SaaS vendors are merely LLM prompts embedded into a flowchart-y, deterministic process flow, within which they are deployed to perform narrow tasks. Basically, these are LLM-wrappers around deterministic process workflows. These are not 'agentic'. More often than not, they are merely 'agent-ish'. The autonomy spectrum This is not to say that there is little to no value in these 'agent-ish' workflows. Agent-ish workflows have their place in an autonomous ecosystem, and the capability footprint of these 'agent-ish' workflows will get better and better over the next few months. But it's still a stretch to call them AI agents. In this context, it's helpful to think of autonomy at different levels. At Forrester we tend to map AI systems along a spectrum of varying agency and autonomy, across the distinct dimensions of control, execution and monitoring. This is analogous to the concept of levels of autonomy in self-driving cars, but instead, as applied to enterprise processes. Let's outline the key levels: What it means It is not unrealistic to imagine organizations designed in the form of hierarchies wherein agentic systems manage other forms of autonomy across Level 1, 2 and 3 ' (including agent-ish' systems), either replacing or augmenting human labor in these roles. However, most organizations are at very early stages of this journey. So, it is important that technology buyers and decision makers take a clear-eyed view to the hype and to understand that these 'agent-ish' systems are not the Promised Land of enterprise autonomy, but just an intermediate (but nevertheless important) step along the journey.

Maximizing AI Agents for Seamless DevOps and Cloud Success

The fast growth of artificial intelligence (AI) has created new opportunities for businesses to improve and be more creative. A key development in this area is intelligent agents. These agents are becoming critical in transforming DevOps and cloud delivery processes. They are designed to complete specific tasks and reach specific goals. This changes how systems work in today's dynamic tech environments. By using generative AI agents, organizations can get real-time insights and automate their processes. This helps them depend less on manual work and be more efficient and scalable. These agents are not just simple tools -- they are flexible systems that can make informed decisions by using the data they collect and their knowledge base. As a result, they provide great value, by optimizing how resources are used, lowering the risk of errors, and boosting overall productivity. In traditional DevOps, automation is very important for success, yet it often depends on static rules and predefined scripts. While this method works well, it can have problems when there are unexpected changes in workloads or environments. AI agents can help with this. They bring a layer of adaptability that can deal with these potential issues. AI agents look at current conditions and use lessons learned from past experiences to suggest or make changes. For example, in cloud delivery, they can improve how resources are used. This helps make sure systems have just the right amount of resources, so they are not over-provisioned or under-resourced. This change not only cuts costs but also keeps things running smoothly during critical operations. Moreover, AI agents can access and use information from their knowledge base. This helps them predict challenges and suggest solutions. This way, systems stay resilient even when things are uncertain. One great use of AI agents in DevOps is managing cloud environments. Google Cloud is using AI automation to improve scalability, security, and efficiency. What really makes cloud delivery better are the different types of AI agents made for specific tasks. AI agents are great at adjusting resources based on changing needs. They look at traffic patterns, application performance, and user demand. For example, when a new product is launched, they make sure cloud resources scale to handle the surge in visitors. Once the traffic calms down, the resources can go back to normal levels. This use of AI helps organizations deal with changing workloads easily. It gives a smooth user experience and keeps costs under control. Security is another important area where AI agents have a big effect. They look at activity logs and how systems behave in real-time. This way they can spot unusual activity and flag potential threats before they get worse. This proactive way of identifying threats helps reduce risks and keeps sensitive data safe, even in dynamic cloud environments. The development phase usually includes tasks some repetitive tasks, such as writing test cases, debugging code, and preparing for deployments. These manual processes can make productivity slower, introduce errors, and raise costs. AI agents help make repetitive work easier by automating it and offering valuable insights. For example, testing teams can use generative AI agents to automate the test case creation. This helps in comprehensive coverage of all new features without needing a lot of manual work. These agents can also give product recommendations for changes in configuration or optimizations, by looking at historical data, which helps improve the overall quality of the application. Their ability to give real-time feedback helps developers spot problems quickly. They do not have to wait for scheduled reviews. This quick response speeds up development. It also makes sure that the final product is robust and reliable. One strong point of AI agents is they can make smart decisions autonomously. They use collected data along with what they know in their internal model of the world. This helps them look at different options and make the best decisions. To better understand how AI agents operate, let's break down the iterative process they follow, which enables them to adapt and improve all the time: This process of observing, analyzing, making decisions, and adapting helps AI agents stay useful. They can adjust as tasks change or new problems arise. AI agents are here to help humans, not take their place. For example, a sales team can use AI to understand customer behavior better. This helps them adjust their approach and improve customer engagement. DevOps teams can also use AI to manage simple, but also complex tasks. This gives them more time to innovate and make strategic choices. This partnership goes beyond just giving out tasks. AI agents offer helpful insights. These insights help teams make better and quicker decisions. Whether it is about using resources wisely or identifying inefficiencies in a pipeline, the teamwork between people and AI agents leads to amazing productivity. To get the most out of AI agents, organizations need to have a smart plan for how to include them. Here are some best practices to follow: By using these practices, companies can reach the full benefits of AI. They can lower risks and increase their return on investment (ROI). As more companies use AI in DevOps and cloud delivery, there are many opportunities for new ideas. From reducing the risk of errors to improving customer engagement, AI agents are becoming very important for businesses that want to stay ahead. Organizations can use technologies like generative AI, natural language processing, and real-time decision-making. This will help them build systems that are efficient. These systems will also be adaptable and smart. The future is for those who embrace these new ideas today and transform their workflows to get ready for the challenges of tomorrow. AI agents are a big step forward for how businesses handle DevOps and cloud delivery. They can take care of specific tasks, adjust to new environments, and make informed decisions. This makes them essential in today's work processes. As businesses keep using AI solutions, they should focus on using these technologies in a strategic way. This can help them grow, work better, and be more creative. It's important that their teams feel strong and ready to do well during this process. The question is no longer if AI will change the future of DevOps. It is about how fast companies can harness AI's potential to shape that future.

Google maps the future of AI agents: Five lessons for businesses

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More A new Google white paper, titled "Agents", imagines a future where artificial intelligence takes on a more active and independent role in business. Published without much fanfare in September, the 42-page document is now gaining attention on X.com (formerly Twitter) and LinkedIn. It introduces the concept of AI agents -- software systems designed to go beyond today's AI models by reasoning, planning, and taking actions to achieve specific goals. Unlike traditional AI systems, which generate responses based solely on pre-existing training data, AI agents can interact with external systems, make decisions, and complete complex tasks on their own. "Agents are autonomous and can act independently of human intervention," the white paper explains, describing them as systems that combine reasoning, logic, and real-time data access. The idea behind these agents is ambitious: they could help businesses automate tasks, solve problems, and make decisions that were once handled exclusively by humans. The paper's authors, Julia Wiesinger, Patrick Marlow, and Vladimir Vuskovic, offer a detailed breakdown of how AI agents work and what they require to function. But the broader implications are just as important. AI agents aren't merely an upgrade to existing technology; they represent a shift in how organizations operate, compete, and innovate. Businesses that adopt these systems could see dramatic gains in efficiency and productivity, while those that hesitate may find themselves struggling to keep up. Here are the five most important insights from Google's white paper and what they could mean for the future of AI in business. 1. AI agents are more than just smarter models Google argues that AI agents represent a fundamental departure from traditional language models. While models like GPT-4o or Google's Gemini excel at generating single-turn responses, they are limited to what they've learned from their training data. AI agents, by contrast, are designed to interact with external systems, learn from real-time data, and execute multi-step tasks. "Knowledge [in traditional models] is limited to what is available in their training data," the paper notes. "Agents extend this knowledge through the connection with external systems via tools." This difference is not just theoretical. Imagine a traditional language model tasked with recommending a travel itinerary. It may suggest ideas based on general knowledge but lacks the ability to book flights, check hotel availability, or adapt its recommendations based on user feedback. An AI agent, however, can do all of these things, combining real-time information with autonomous decision-making. This shift positions agents as a new type of digital worker capable of handling complex workflows. For businesses, this could mean automating tasks that previously required multiple human roles. By integrating reasoning and execution, agents could become indispensable for industries ranging from logistics to customer service. 2. A cognitive architecture powers their decision-making At the heart of an AI agent's capabilities is its cognitive architecture, which Google describes as a framework for reasoning, planning, and decision-making. This architecture, called the orchestration layer, allows agents to process information in cycles, incorporating new data to refine their actions and decisions. Google compares this process to a chef preparing a meal in a busy kitchen. The chef gathers ingredients, considers the customer's preferences, and adapts the recipe as needed based on feedback or ingredient availability. Similarly, an AI agent gathers data, reasons about its next steps, and adjusts its actions to achieve a specific goal. The orchestration layer relies on advanced reasoning techniques to guide decision-making. Frameworks such as ReAct (Reasoning and Acting), Chain-of-Thought (CoT), and Tree-of-Thoughts (ToT) provide structured methods for breaking down complex tasks. For instance, ReAct enables an agent to combine reasoning and actions in real time, while ToT allows it to explore multiple possible solutions simultaneously. These techniques give agents the ability to make decisions that are not only reactive but also proactive. According to the paper, this makes them highly adaptable, capable of managing uncertainty and complexity in ways that traditional models cannot. For enterprises, this means agents could take on tasks like troubleshooting a supply chain issue or analyzing financial data with a level of autonomy that reduces the need for constant human oversight. 3. Tools expand agents' reach beyond training data Traditional AI models are often described as "static libraries of knowledge," limited to what they were trained on. AI agents, on the other hand, can access real-time information and interact with external systems through tools. This capability is what makes them practical for real-world applications. "Tools bridge the gap between the agent's internal capabilities and the external world," the paper explains. These tools include APIs, extensions, and data stores, which allow agents to fetch information, execute actions, and retrieve knowledge that evolves over time. For example, an agent tasked with planning a business trip could use an API extension to check flight schedules, a data store to retrieve travel policies, and a mapping tool to find nearby hotels. This ability to interact dynamically with external systems transforms agents from static responders into active participants in business processes. Google also highlights the flexibility of these tools. Functions, for instance, allow developers to offload certain tasks to client-side systems, giving businesses more control over how agents access sensitive data or perform specific operations. This flexibility could be essential for industries like finance and healthcare, where compliance and security are critical. 4. Retrieval-augmented generation makes agents smarter One of the most promising advancements in AI agent design is the integration of Retrieval-Augmented Generation (RAG). This technique allows agents to query external data sources -- such as vector databases or structured documents -- when their training data falls short. "Data Stores address the limitation [of static models] by providing access to more dynamic and up-to-date information," the paper explains, describing how agents can retrieve relevant data in real time to ground their responses in factual information. RAG-based agents are particularly valuable in fields where information changes rapidly. In the financial sector, for instance, an agent could pull real-time market data before making investment recommendations. In healthcare, it could retrieve the latest research to inform diagnostic suggestions. This approach also addresses a persistent problem in AI: hallucination, or the generation of incorrect or fabricated information. By grounding their responses in real-world data, agents can improve accuracy and reliability, making them better suited for high-stakes applications. 5. Google offers tools to accelerate agent deployment While the white paper is rich with technical detail, it also provides practical guidance for businesses looking to implement AI agents. Google highlights two key platforms: LangChain, an open-source framework for agent development, and Vertex AI, a managed platform for deploying agents at scale. LangChain simplifies the process of building agents by allowing developers to chain together reasoning steps and tool calls. Vertex AI, meanwhile, offers features like testing, debugging, and performance evaluation, making it easier to deploy production-grade agents. "Vertex AI allows developers to focus on building and refining their agents while the complexities of infrastructure, deployment, and maintenance are managed by the platform itself," the paper states. These tools lower the barrier to entry for businesses that want to experiment with AI agents but lack extensive technical expertise. However, they also raise questions about the long-term consequences of widespread agent adoption. As these systems become more capable, businesses will need to consider how to balance efficiency gains with potential risks, such as over-reliance on automation or ethical concerns about decision-making transparency. What it all means Google's white paper on AI agents is a detailed and ambitious vision of where artificial intelligence is headed. For enterprises, the message is clear: AI agents are not just a theoretical concept -- they are a practical tool that can reshape how businesses operate. However, this transformation will not happen overnight. Deploying AI agents requires careful planning, experimentation, and a willingness to rethink traditional workflows. As the paper notes, "No two agents are created alike due to the generative nature of the foundational models that underpin their architecture." For now, AI agents represent both an opportunity and a challenge. Businesses that invest in understanding and implementing this technology stand to gain a significant advantage. Those that wait may find themselves playing catch-up in a world where intelligent, autonomous systems are increasingly running the show.

How AI Agents Are Transforming Business Automation

AI agents are transforming business automation by introducing advanced capabilities that extend far beyond traditional systems. These systems, powered by large language models (LLMs), autonomously make decisions, execute tasks, and integrate seamlessly with tools, becoming a critical component of modern workflow optimization. By understanding the unique features of AI agents, their components, and their applications, businesses can unlock unprecedented efficiencies and gain a competitive advantage in their industries. Imagine a world where your business operations run seamlessly, with mundane tasks handled effortlessly and complex decisions made with precision -- all without constant human intervention. Sounds like a dream, right? But with the rise of AI agents, this vision is quickly becoming a reality. Unlike traditional automation systems that rely on rigid, predefined rules, AI agents bring a new level of intelligence and adaptability to the table. They don't just follow instructions -- they think, decide, and act, making them a fantastic option for businesses looking to streamline workflows and stay ahead in a competitive landscape. In this guide learn about what makes AI agents so unique and why they're poised to redefine how businesses approach automation. From their ability to dynamically analyze situations and collaborate with other systems to their potential for transforming industries, AI agents are more than just a buzzword -- they're a practical solution to real-world challenges. Whether you're a business leader exploring ways to optimize operations or simply curious about the future of automation. AI agents are autonomous systems designed to perform tasks and make decisions with minimal human intervention. Unlike basic automation, which operates on predefined rules, AI agents use the power of LLMs to dynamically analyze inputs, decide on actions, and execute tasks. They go beyond AI-enhanced automation by integrating decision-making capabilities with access to tools, databases, and even other agents. This enables them to manage complex workflows, such as scheduling, data retrieval, and strategic decision-making, with remarkable efficiency. Key Features of AI Agents: These features make AI agents a fantastic force in modern business operations, capable of handling tasks that previously required significant manual effort. To fully appreciate the value of AI agents, it is essential to understand how they differ from other types of automation: This progression highlights how AI agents surpass traditional automation by allowing intelligent decision-making and task execution, making them indispensable for businesses aiming to optimize their operations. Discover other guides from our vast content that could be of interest on AI Agents. AI agents rely on several critical components to deliver their advanced functionality. These components work together to enable seamless task execution and decision-making: These components collectively empower AI agents to autonomously handle tasks such as data analysis, reporting, and decision-making, significantly reducing the need for human intervention. AI agents are already driving substantial improvements across various industries by streamlining workflows and reducing manual effort. Their applications are diverse and impactful: By integrating AI agents into their operations, businesses can reduce costs, improve efficiency, and focus on innovation and growth. Despite their potential, AI agents come with challenges that businesses must address to ensure successful implementation: These challenges highlight the importance of careful planning, continuous monitoring, and iterative improvements when deploying AI agents in business environments. To successfully integrate AI agents into your workflows, consider the following practical steps: This phased approach ensures a smooth transition to AI-driven automation while minimizing risks and maximizing benefits. As AI agents continue to evolve, their potential to reshape industries becomes increasingly evident. By automating repetitive tasks and allowing dynamic decision-making, they empower businesses to focus on innovation, strategic growth, and customer satisfaction. However, addressing challenges such as cost, latency, and safety will be critical to fully realizing their benefits. Organizations that embrace AI agents early and strategically will be well-positioned to thrive in an increasingly automated world.

AI Agents Explained: The Next Evolution in Artificial Intelligence

Serving tech enthusiasts for over 25 years. TechSpot means tech analysis and advice you can trust. When you buy through our links, we may earn a commission. 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. Nvidia CEO Jensen Huang was clearly trying to tell us something as he outlined the evolution of AI technologies, from perception AI to generative AI, agentic AI, and the rise of physical AI, during his keynote at CES 2025. 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. Editor's Note: Guest author Dr. Brian O'Neill is an Associate Professor of Computer Science at Quinnipiac University. Dr. O'Neill's primary research area is artificial intelligence, with a particular emphasis on the role that AI can play in supporting creativity. This article is republished from The Conversation under a Creative Commons license. 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. 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. 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. 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.

Google Unveils Whitepaper on Generative AI Agents

According to the paper, the key components of the agent's architecture include a cognitive framework that organizes reasoning, planning, and decision-making processes. Google has published a comprehensive whitepaper that explores the development and functionality of Generative AI agents. This document outlines how these agents operate by utilising external tools to enhance their capabilities beyond traditional language models. The whitepaper defines a Generative AI agent as an application designed to achieve specific goals by observing its environment and acting upon it using available tools. These agents are characterised by their autonomy, allowing them to operate independently of human intervention when provided with clear objectives. "Agents extend the capabilities of language models by leveraging tools to access real-time information, suggest real-world actions, and plan and execute complex tasks autonomously," the authors stated. According to the paper, the key components of the agent's architecture include a cognitive framework that structures reasoning, planning, and decision-making processes. The orchestration layer is crucial as it guides agents through a cyclical process of information intake and action execution. The document also discusses the importance of tools, such as Extensions and Functions, which allow agents to interact with external systems. These tools enable agents to perform tasks like updating databases or fetching real-time data. "Tools bridge the gap between the agent's internal capabilities and the external world," according to the authors. They provide examples of how agents can utilize various APIs to enhance their functionality. Moreover, the whitepaper highlights the role of Data Stores in providing agents with access to dynamic information, ensuring that responses remain relevant and factually accurate. This capability is particularly important as it allows agents to adapt to changing information landscapes. The whitepaper presents various use cases where Generative AI agents can be applied effectively. For instance, an agent could assist users in booking flights by interacting with multiple APIs to gather necessary information dynamically. In addition, Google described how developers can leverage these agents within applications like Vertex AI. The platform provides a managed environment where developers can define goals, task instructions, and examples to efficiently construct desired system behaviors. On the other hand, OpenAI chief Sam Altman recently published a blog titled Reflections, in which he stated that AI agents could enter the workforce by 2025. "We believe that, in 2025, we may see the first AI agents join the workforce and materially change the output of companies," Altman wrote.