The Rise of AI Agents: Transforming Business Operations and Challenges in Implementation

Finding value from AI agents from day one

Although still so early in its development that there lacks consensus on a single, shared definition, agentic AI refers loosely to a suite of AI systems capable of connected and autonomous decision-making with zero or limited human intervention. In scenarios where traditional AI typically requires explicit prompts or instructions for each step, agentic AI will independently execute tasks, learning and adapting to its environment to refine decisions over time. From assuming oversight for complex workflows, such as procurement or recruitment, to carrying out proactive cybersecurity checks or automating support, enterprises are abuzz at the potential use cases for agentic AI. According to one Capgemini survey, 50% of business executives are set to invest in and implement AI agents in their organizations in 2025, up from just 10% currently. Gartner has also forecast that 33% of enterprise software applications will incorporate agentic AI by 2028. For context, in 2024 that proportion was less than 1%. "It's creating such a buzz - software enthusiasts seeing the possibilities unlocked by LLMs, venture capitalists wanting to find the next big thing, companies trying to find the 'killer app," says Matt McLarty, chief technology officer at Boomi. But, he adds, "right now organizations are struggling to get out of the starting blocks." The challenge is that many organizations are so caught up in the excitement that they risk attempting to run before they can walk when it comes to deployment of agentic AI, believes McLarty. And in so doing they risk turning it from potential business breakthrough into a source of cost, complexity, and confusion. The heady capabilities of agentic AI have created understandable temptation for senior business leaders to rush in, acting on impulse rather than insight risks turning the technology into a solution in search of a problem, points out McLarty. It's a scenario that's unfolded with previous technologies. The decoupling of Blockchain from Bitcoin in 2014 paved the way for a Blockchain 2.0 boom in which organizations rushed to explore the applications for a digital, decentralized ledger beyond currency. But a decade on, the technology has fallen far short of forecasts at the time, dogged by technology limitations and obfuscated use cases. "I do see Blockchain as a cautionary tale," says McLarty. "The hype and ultimate lack of adoption is definitely a path the agentic AI movement should avoid." He explains, "The problem with Blockchain is that people struggle to find use cases where it applies as a solution, and even when they find the use cases, there is often a simpler and cheaper solution," he adds. "I think agentic AI can do things no other solution can, in terms of contextual reasoning and dynamic execution. But as technologists, we get so excited about the technology, sometimes we lose sight of the business problem." Instead of diving in headfirst, McLarty advocates for an iterative attitude toward applications of agentic AI, targeting "low-hanging fruit" and incremental use cases. This includes focusing investment on the worker agents that are set to make up the components of more sophisticated, multi-agent agentic systems further down the road.

How AI agents can generate $450 billion by 2028 - and what stands in the way

Agentic AI is one of the fastest-emerging technologies in business, with the potential to generate $450 billion in economic value through revenue uplift and cost savings across surveyed countries by 2028, according to the Rise of agentic AI: How trust is the key to human-AI collaboration. The new report from Capgemini Research Institute reveals insights from a survey of 1,500 senior executives across 14 countries on the emergence of AI agents as a transformative force in business. Also: My 8 ChatGPT Agent tests produced only 1 near-perfect result - and a lot of alternative facts Here is Capgemini's definition: "AI agents are programs/platforms/software that are connected to the business environment with a defined boundary, make decisions autonomously, and act to achieve specific goals with or without human intervention. With the latest advances in reasoning AI models, AI agents are able to break down tasks, 'reason' through potential pathways to find solutions to the given problem, try those solutions, and present successful outcomes." Capgemini notes that the capabilities of AI agents are increasing fast, and the costs to operate and develop AI agents is declining. AI agents are one of the top technology trends for 2025. AI agents could generate around $450 billion in total economic value in the 14 countries surveyed by 2028. Organizations with scaled implementation are projected to generate around $382 million (2.5% of annual revenue) on average over the next three years, while we expect others to generate around $76 million (0.5% of annual revenue). This applies to an average organization with $15 billion in annual revenues. Capgemini expects that surveyed organizations will collectively achieve $19 billion in gains over the next 12 months, with this figure projected to increase to $92 billion by the third year. Other predictions outside of Capgemini include: Also: Is HR ready for AI? Goldman Sachs predicts that Gen AI will drive a 6.1% rise in US GDP over the next decade -- by 2028, this translates to around $540 billion in the US. IDC forecasts that AI technologies overall will influence 3.5% of global GDP by 2030 -- by 2028, this implies an impact of around $1.9 trillion globally. MIT research estimates that a combination of AI capabilities could automate around one-fifth (slightly over 20%) of value-added tasks. A substantial 93% anticipate that organizations that have successfully scaled the implementation of AI agents within the next 12 months will achieve a competitive advantage. Also: Business leaders continue to push workers toward daily use of AI Currently, 23% of organizations have initiated AI agent pilot projects, while 14% have progressed to partial or full-scale implementation. About 30% are exploring AI agents, and another 31% are preparing for experimentation or deployment within the next six to 12 months. In fact, the pace of AI agent adoption mirrors the rapid trajectory seen with generative AI. Many organizations that claim to be implementing AI agents are deploying solutions with limited autonomy. As many as 85% of business processes are expected to be at low levels of autonomy in the next 12 months. Industries noted in the Capgemini report include automotive, financial services, life sciences, telecom, and retail -- with specific company examples and use cases. Also: Your next job? Managing a fleet of AI agents The report noted that 62% of companies prefer to partner with solution providers, such as Salesforce and system integrators, to implement or tailor already available AI agents. The ready availability of in-built agents, pre-existing integrations with legacy systems, and staff fluency in using these tools likely contribute to the preference. Capgemini found that 16% of organizations have developed a strategy and roadmap to implement agentic AI. Research found that 39% of organizations don't have a strategy, but have multiple initiatives across functions to develop innovative solutions that can be scaled up. The need for dedicated leadership to oversee AI agent initiatives is also a priority, with 26% appointing new leaders specifically for AI agents and 59% delegating this responsibility to existing AI or gen AI leadership. Over half of organizations prefer consumption-based pricing for AI models within AI agents. Consumption-based (55%), platform-based (43%), and license-based(37%) models are the preferred pricing models among organizations for agentic AI solutions. Customer services and support, IT, and sales are the functions where most executives predict that AI agents will be actively performing at least one process or sub-process daily within the next 12 months. These functions often involve high volumes of interaction, require responsiveness over precision, and depend on contextual, conversational engagement. Also: The death of spreadsheets: 6 reasons why AI will soon be the dominant business reporting tool Digital labor will handle one process by 2028, as 58% of business functions are likely to have AI agents handling at least one process or sub-process daily within three years. Over the next 12 months, we expect AI agents with Level 3 autonomy or higher to manage 15% of processes and subprocesses in each business function. This will rise to 25% within the next one to three years. Fully autonomous AI agents (Level 5) are expected to handle around 4% of business processes within three years. Capgemini noted that in the next 12 months, it expects AI agents to make 6% of day-to-day decisions, increasing to 8% in one to three years. And 25% of processes within a business function are expected to be handled by AI agents with Level 3 or higher autonomy by 2028. Almost half (47%) of organizations in the implementation phase report above average level of trust in AI agents, compared with 37% that are still in the exploration phase. This strong correlation between trust and implementation confirms that trust plays a crucial role in the adoption of AI agents. Only 22% of executives trust fully autonomous AI agents for enterprise applications, down from 43% in 2024. The gap is widening in the wrong direction. Also: Beyond the AI trust gap: A leader's playbook The decline in trust is not limited to AI agents; it extends to AI and gen AI as well. Capgemini noted that half of organizations have insufficient knowledge of AI agents' capabilities. Employee skills and AI infrastructure maturity add to the trust deficiency. Over four in five (82%) organizations report low-to-medium maturity across dimensions such as computing, integration, orchestration, fine-tuning, and cybersecurity. The Capgemini report concludes with this powerful reminder: "The winners in this next wave of AI will not be those who simply deploy more AI tools. Rather, they will be those who rethink their business, reimagine workflows, reskill their workforces, restructure their organizations, and embed ethical safeguards from the outset." I believe AI agents may represent the biggest technological impact on businesses ever. This is not about new technology. This is about a new world. The most successful and resilient businesses of the future will be autonomous. Autonomous businesses will have a hybrid workforce -- humans and digital labor, leveraging AI agents to augment and upgrade human capabilities. AI agents will be used for both cognitive transfer and cognitive upgrade opportunities. Autonomous businesses will also position themselves to best take advantage of the new stability-performance business operating models.

The Wrong Way To Think About Implementing AI Agents

Recently, analysts at Gartner published a bold prediction regarding the future of agentic AI in the enterprise: more than 40% of in-progress agentic AI projects will be canceled by the end of 2027. This would seem to support other recent findings from related studies on AI agents in the enterprise. Earlier this year, for example, researchers at Carnegie Mellon University conducted an interesting-yet-flawed experiment: They staffed a fake software company, TheAgentCompany, entirely with AI agents. They asked the agents -- each powered by a specific LLM -- to take on the day-to-day work of a modern software company. They assigned the agents work, and that was about it so far as instruction or orchestration. After that, they asked them to get to work. AI agents have been the subject of frenzied excitement in the enterprise, with such prominent CEOs as Mark Benioff, Jensen Huang, Satya Nadella and Mark Zuckerberg all predicting their impending, transformative preeminence. CMU's experiment, therefore, garnered lots of interest. But as outlets like Business Insider have reported, the results were not good. The best-performing agent finished just 24% of the jobs assigned to it. Most completed just 10%. It cost each agent on average $6 to complete an individual task, which added up quickly, since the jobs the agents had been assigned required completing many different tasks. Simple tasks stalled due to agents' inability to overcome unexpected challenges, like dismissing a pop-up ad. Observers were quick to interpret these results -- along with results of still more studies conducted over the past year or so -- as evidence that AI agents are perhaps not quite as capable as tech CEOs have made them out to be. "[AI agents] are clearly not ready for more complex gigs humans excel at," Futurism's Joe Wilkins wrote. Here's how Business Insider's Shubham Agarwal put it: "The findings, along with other emerging research about AI agents, complicate the idea that an AI agent workforce is just around the corner -- there's a lot of work they simply aren't good at." Agarwal concluded the experiment was a "total disaster." This, however, is the incorrect conclusion to draw -- incomplete at best and irrelevant at its core. Augment, don't replace That's because it stems from a flawed premise -- specifically, that AI agents should be expected to replace humans outright. They're not. They're meant to augment them. The agents in CMU's experiment, in other words, were set up to fail. The culprit in the experiment was not the capacity of the agents themselves, but a misapplication of their purpose. This, interestingly, is what underpins Gartner's recent research into AI agents in the enterprise. According to Anushree Verma, a senior director analyst at Gartner, many in-progress AI agent deployments will fail ultimately because, "They are mostly driven by hype and are often misapplied." What CMU's experiment ultimately showcases is precisely this: what happens when agentic AI rollouts stem foremost from such misapplication. It proves not that agents can't complete complex work, but rather that CMU simply attempted to implement AI agents in entirely the wrong way. So what's a better way? To start, we shouldn't treat this technology as magic. AI agents, simply put, are tools. They're not human replacements. They're things for humans to use. And just like any tool, the value humans derive from agents comes down not just to how smart or powerful individual agents are, but how strategically we leverage them to improve our own capacity. Setting a bunch of specialized AI agents loose inside an organization without structures governing how they should work with each other or with human workers -- not to mention without connecting them to the various departments, systems and policy centers, such that they can be orchestrated across them -- simply isn't very strategic. In fact, it's not a strategic way to leverage any tool, resource or intelligent entity, humans included. Try the same experiment, but substitute AI agents with highly intelligent human workers. Let those workers loose inside your organization without roles, responsibilities, organization or protocol, and you'll get the same result: noisy, inefficient, expensive chaos. LLMs can't deliver consistently good work or work effectively together toward a common set of goals without other supporting technology or infrastructure. So what might be more useful instead? If the goal is to determine what, ultimately, AI agents are capable of in an enterprise context, we should experiment with them using conditions consistent with an enterprise context. And we should ensure there's adequate structure in place behind the scenes -- such as end-to-end orchestration infrastructure -- enabling AI agents to deliver genuine enterprise value. Structure and strategy matter People who believe AI agents are exciting because they'll replace humans have it all wrong. AI agents are exciting not because they'll replace humans, but because they'll replace traditional enterprise software. It's in this way that AI agents could transform the enterprise -- by improving not just the capacity of human-led organizations, but the experiences provided human workers inside them. But only if we will it. For organizations of every sort -- from TheAgentCompany to Alphabet to those surveyed by Gartner -- getting transformational value out of agents will come down to one thing: how strategically we integrate them into the infrastructure of our day-to-day operations, and what sort of structures we put in place to govern them. This is as true of AI agents as it is of any other sort of intelligent entity we leverage inside the enterprise, including humans. Intelligent entities need structure to work effectively. You want intelligent entities to be able to work autonomously and creatively in pursuit of the goals you set for them. But to effectively pursue those goals, you also need direction and hierarchy, governance and org charts, processes and rules. It's on what sort of structure we put around AI agents, in order to maximize their impact for humans, that we should be iterating and experimenting. This is a matter, in the end, not only of strategy and performance, but of security; thinking carefully about how we construct and deploy AI agents in the enterprise, for example, is how we will wall off AI from things it shouldn't be touching internally, such as login credentials, sensitive data or certain actions. It's also, however, the only way we'll ever truly find out just what this technology is capable of. Anything else is a waste of time. Sagi Eliyahu is the co-founder and CEO of Tonkean, an AI-powered intake and orchestration platform that helps enterprise-shared service teams such as procurement, legal, IT and HR create processes that people actually follow. Tonkean's agents use AI to anticipate employees' needs and guide them through their requests.

AI Agents: the next big phase of artificial intelligence

The challenges and opportunities of scaling AI agents for large-scale automation Artificial intelligence (AI) has entered a new phase of its evolution - one where models do not just reason but also act. Welcome to the age of AI agents: where systems can independently execute complex tasks, collaborate with other agents, and operate autonomously at scale. This shift is poised to unlock transformative gains in productivity and efficiency across every industry. Traditionally, AI interactions have centered around a single, often large, model designed to perform a variety of tasks. However, with AI agents, this is changing. Instead of relying on one massive model to handle everything from start to finish, AI agents break down tasks into smaller, specialized components, each handled by different agents. Compare this to moving from a single craftsman to an intelligent network of specialist workers, making AI more specialized and efficient. For example, today, if someone asked an AI to design a new computer chip, the task would be processed end-to-end by one model. In the world of AI agents, that same request would be divided among a network of agents - each responsible for specific aspects like layout, simulation, and optimization - working together to deliver the result faster and more intelligently. Beyond responding to specific requests and tasks, the impact of AI agents will be transformative. They are set to drive large-scale automation, bringing greater adaptability, intelligence and autonomy to processes that were previously manual or considered to be inefficient. At the same time, AI agents are set to reshape workplace operations and practices, by enhancing how repetitive tasks like document management, customer support, and workflow orchestration are handled. The pivot towards AI agents is also set to influence AI investment strategies. The Arm AI Readiness Index report reveals that 80 percent of organizations surveyed have an AI budget, with 87 percent expecting it to grow. Businesses are increasingly prioritizing AI tools and platforms that support modular, scalable agent ecosystems. The impact of AI agents will be widespread and cross-industry. Sectors like finance, insurance, healthcare, retail, logistics, and creative services are already exploring a variety of use cases where AI agents can be adopted, ranging from fraud detection to automated underwriting, and even content creation. The potential is staggering. Moreover, AI agents will not be confined to one environment, with workloads covering a wide range of systems. In mobile, imagine saying "book me a flight" or "sort my photos," and having a local network of AI agents coordinate these requests seamlessly. AI-first wearables may soon allow us to blend the physical and virtual worlds by using agentic AI to reason, predict, assist, and adapt. For example, you may glance at a flower, asking "what flower am I looking at?" -- and your smart glasses will instantly identify it, offering care tips or fun facts. Even virtual assistants in the home could use AI agents to control devices and complete everyday household tasks more efficiently. On a larger scale, future autonomous vehicles could deploy multiple AI agents to handle various workloads, like navigation, object detection, real-time decision-making, and passenger interactions. Meanwhile, in cloud or enterprise settings, AI agents will power next-generation customer service and decision-making systems for improved responses. A key enabler of AI agents is the rise of smaller AI models. These are easier to customize for specific tasks, more power-efficient to run, and faster to deploy across distributed systems. By using a collection of smaller models rather than one giant model, businesses can optimize both the performance and power-efficiency that are critical for everything from mobile devices to datacenters. In fact, as explained in the Arm Silicon Reimagined report, many of these smaller models are already providing great results in terms of AI capabilities and performance, while running entirely on the device. AI agents represent more than just the next evolution of AI - they signal a fundamental shift in how work gets done, decisions are made, and value is created. Autonomous, task-driven systems powered by AI agents have the potential to enhance productivity, streamline operations, and enable entirely new customer experiences. By moving beyond standalone AI models to networks of multiple specialized AI agents, organizations in any industry can unlock faster, smarter, and more cost-effective ways of operating across every function. As AI agents become more capable, collaborative, and context-aware, they will redefine our expectations of technology - not simply as tools, but as proactive, intelligent collaborators. The organizations that embrace this shift early will not only boost efficiency, but also uncover new opportunities for innovation, differentiation, and growth in this new AI world. We list the best business cloud storage.

AI agents are here, but companies are still learning how to put them to use

Agentic AI is the hottest new trend in today's tech sector, as both large companies and buzzy startups promise that a legion of autonomous programs will soon be able to manage our personal and professional lives. Sapna Chadha, vice president for Southeast Asia and South Asia frontier at Google Asia Pacific, described agentic AI as the logical next step in this new technology. "AI agents are where you take intelligent language models and give them access to tools," she said Tuesday at the Fortune Brainstorm AI Singapore conference. This access allows the language models to stitch together complex and multi-step actions. Vivek Luthra, Accenture's Asia-Pacific data and AI lead, shared one example from Accenture's own experience: Marketing teams could use an AI agent to manage campaigns, allowing human employees to engage in more value-added functions. (Accenture is a founding partner of Brainstorm AI Singapore) Chadha predicted that almost a third of all enterprise software will have agentic AI built in by 2028, and could automate almost 15% of day-to-day work and workflows. But Luthra suggested that most companies aren't there yet. Accenture clients fall into three stages of agentic AI adoption. The first is AI assistance, where staff members ask an agentic co-pilot for help in much the same way they might ask a fellow team member a question. The second stage is treating it as an advisor, increasing the overall capability of all human employees and empowering them to make the right decisions. The final stage is giving autonomous agents the authority to handle entire processes on their own. As of now, Luthra says, most clients are in the first and second stage, with fewer companies prepared to let AI agents truly handle things on their own. According to Luthra, companies leading the way on AI begin by imagining new ways of structuring workflows, then assess what skills are needed in the workplace to make that happen. Then, they put agentic AI into practice with a cross-platform "workbench" that gives employees opportunities to integrate AI agents into their daily lives.

Agentic AI finding solid traction at enterprises on clear tech use cases - The Economic Times

Agentic AI, which are bots that can handle tasks autonomously on behalf of users, is becoming a defining narrative in enterprises as they see clear use cases and return on investments in AI unlike a few months ago, according to experts. This includes homegrown enterprises as well, which are investing in agentic AI, driving demand for such solutions for large global technology majors, executives told ET. Companies such as IBM and Automation Anywhere are also doubling up on the India market by setting up innovation centres and hiring more people respectively as they see demand increase in the region. Globally this is a $11-18 trillion market opportunity, according to a McKinsey report. A report from Grand View Research estimates the Indian market to be $1.73 billion by 2030, growing at a CAGR of 54%. Companies are tapping into this. Last week, OpenAI launched ChatGPTAgent, which can handle users' tasks autonomously using virtual computers. Over the past few months, companies such as IBM, Salesforce, Snowflake and Google have launched agentic AI suites to help customers automate their workflows. While there is demand, the space is not without challenges. Experts told ET that adoption is low as most companies are still experimenting with GenAI and implementation will take another couple of years. Why is agentic AI taking off now? There are a couple of reasons. Giridhar LV, co-founder of Nuvepro, which works with enterprises for skilling employees in GenAI, and a former Mindtree executive, said unlike 3-4 months back, agentic AI applications show a clear path for return on investment. For instance, agentic workflows can automate repeated tasks freeing up the time for more productive work across departments and industries. "You can now completely automate a workflow with or without humans in the loop. This was not as clear 3-4 months back," he said. "We see organisations experiment with agentic workflow over the next few months to see where agentic workflow makes sense and where it does not," he said. While coding agents are widely adopted to increase productivity, other areas that are seeing increased adoption include customer service and document processing. Dinesh Nirmal, senior vice president at IBM Software, told ET recently that enterprises want to optimise time-consuming processes. "For instance, document processing is taking off because enterprises want to disrupt that line of business. (If) You have a payment process for a vendor, (and) payment takes a month to pay, can the same thing be done in a day," Nirmal said. As a result, many enterprises are now willing to invest in the technology. India taking off Ankur Kothari, co-founder of Automation Anywhere, recently told ET that unlike 5-6 years ago, where the multinational companies formed a large portion of their clientele, they are seeing increasing demand from the Indian enterprises. IBM's Nirmal shared that they work with large financial institutions and are seeing more enterprises adopting their technology. "The Indian market is opening up and we are seeing a lot of momentum," he said. The company is seeing new technologies creating opportunities for its existing customers and new customers, he said. But this is not without challenges. Challenges While there is hype, there are also concerns making the adoption slow. The latest McKinsey report highlighted that over 80% of the companies have not seen any material impact on their top or bottom line from their GenAI initiatives. A Bengaluru-based tech investor shared that the biggest challenge is also with enterprises, who are experimenting with these technologies but there are fewer deployments, as there are potential concerns over hallucination and security, which are inherent to AI systems. Nirmal explained that any new technology will take time for adoption, and in the case of GenAI, there are multiple challenges in terms of reliability, scalability, security and regulatory concerns. According to him, it will take another 18-24 months to see real benefits of agentic AI as enterprises implement these solutions.

Building Context-Aware AI Agents

Join the DZone community and get the full member experience. Join For Free Artificial intelligence is on a rapid evolutionary track, and the once awe-inspiring conversational capabilities of ChatGPT raise very few eyebrows these days. AI developers are shifting into a higher gear, and these days, the focus is all about agents. They're building more advanced AI systems that transform large language models into thinkers, decision-makers, and action-takers, which can automate many kinds of work. To create an AI agent, the developer must assign an LLM to a specific role, assign it a clear goal to accomplish, and provide access to the necessary resources for the agent to fulfill its mission. When AI agents are focused on a clearly defined objective and can utilise APIs, web browsers, search engines, and databases as humans do, they can autonomously determine how to perform the assigned task. Agentic AI represents an entirely new paradigm for developers, enabling multiple agents to collaborate on complex, multistep tasks and redefine the nature of business automation. How Did We Get Here? One of the most important capabilities for AI agents is their ability to understand context. LLMs can be taught to remember what was said earlier during a conversation or in previous sessions, and they can take this into account when it comes to making decisions, without any changes made to their underlying code. This in-context learning is what enables LLMs to adapt and respond more effectively to complex queries. AI agents are further enhanced by retrieval-augmented generation (RAG), which is a popular technique that enables LLMs to augment their knowledge with data from dynamic sources beyond their initial training sets. This is what makes it possible to customize an LLM's responses for a given context, such as providing customer service support for a specific organization. A more recent development is multimodal models, or MLLMs, which enable AI agents to explore and navigate through a graphical user interface. MLLMs combine the capabilities of LLMs, which perform well at tasks involving natural language processing but struggle when it comes to processing visual elements, and large vision models or LVMs, which excel at processing visuals but do not possess the advanced reasoning skills of traditional LLMs. By blending an LVM's visual processing with an LLM's reasoning, MLLMs can analyze and understand both text and images. Navigating the Web A key skill for any AI agent is the ability to explore, understand, and take actions online, which means developers need to teach it how to surf the web using a browser. Browser Use One of the most popular tools for this is Browser Use, an open-source framework that helps to make the internet "readable" to AI agents. Browser Use enables agents to go beyond their visual recognition capabilities by breaking down each website into a structured text. Once this is accomplished, AI agents can process what they're seeing online in a more deterministic way, including dynamic, embedded web elements that computer vision-based agents might miss. This means it can understand all of the options available on a specific web page and identify what it needs to do. Scraping Browser AI agents also need a specialized browser that allows them to navigate the web at scale, avoiding the various pitfalls set up by web publishers to try and prevent automated bots from navigating through them and importing their data. With Bright Data's Scraping Browser, AI agents gain access to a variety of tools that can help them to do this at an unprecedented scale. With unlimited concurrent sessions, thousands of agents can explore the web continuously, thanks to API and script management integrations that provide granular control. It also offers a range of mechanisms for getting around the bot-blocking tools implemented by sites such as Amazon and Facebook that aim to curtail autonomous traffic. These include browser fingerprinting, automated retries, advanced Captcha solvers, and a library of more than 150 million proxy IP addresses. Sequential Task Execution Now that our AI agents are set up to explore the web, the next step for developers is to teach them to execute tasks sequentially, in a logical order, so they can undertake complex work involving multiple steps. When AI agents are tasked with gathering context from multiple sources and reasoning across them, they often struggle. Some examples of this might include adaptive surveys, which require an agent to perform sentiment analysis in real-time and then ask follow-up questions. Similarly, tasks such as supplier risk assessment, customer churn analysis, and forecasting bottlenecks in manufacturing operations involve pulling data from multiple domains. Agentic Teams To address this, developers must devise a method for unifying input data and integrating it so that their AI agents can gain a comprehensive understanding of the information they're drawing on. The easiest way to do this is to employ teams of specialized AI agents that are each trained to understand or work on a specific domain or task. By using Crew AI's open-source agentic AI framework, developers can quickly spin up a team of AI agents that can collaborate to perform multi-step tasks. These agentic teams will split a task between them, with each one focusing on whatever aspect falls within its capabilities, leaving the other tasks to an agent that's better suited for it. Once their work is complete, they'll combine the results. Standardized Interactions These AI agent teams may require access to a range of different software tools to complete their assigned tasks, which is where the Model Context Protocol comes into play. The open-source MCP is rapidly emerging as the de facto way for AI agents to interact with software, APIs, and services because of the way it standardizes context sharing and action execution, allowing those agents to operate in dynamic, multi-tool environments. MCP provides AI agents with structured access to almost any API, data source, or tool, enabling natural and flexible workflows within applications while reducing the custom logic required for integration. Just as APIs transformed the way software communicates, MCP is set to become a universal language for agent-tool interaction, providing support for chaining tools across domains to enable more powerful compound actions. Cross-Domain Context We'll also need a semantic layer to link the information found in structured datasets with the live, unstructured data that's sourced from the internet. Wren AI offers a powerful semantic layer that helps developers to standardize cross-domain data, which is often stored in incompatible formats, so it can be amalgamated and interpreted consistently by AI agents. Crucially, it provides the business context that agents need to work with structured enterprise data, so it can be tagged and aligned with web-based data to create comprehensive knowledge graphs. By mapping different cross-domain entities in this way using a knowledge graph, AI agents can more accurately identify context-based relationships between them. Armed with this ability to execute sequential tasks, developers will be able to create AI agents that can generate more relevant cross-domain insights by contextualizing external, web-based data against internal metrics. For instance, an AI agent might be able to connect an external news story regarding supply shortages to update the risk score in an organization's internal procurement system, taking into account the company's existing stocks, the expected duration of the shortage, and the ability to source alternatives from different suppliers. Automation at Unprecedented Scales AI agents represent a dramatic evolution of LLMs, which have transformed from providing simple, grounded responses based on their pre-trained data into intelligent entities that can actively explore and interact with their environments and complete assigned tasks. When developers combine data and web exploration with logical reasoning and decision-making, AI agents can perform more complicated, multi-step tasks with greater autonomy and accuracy. It will usher in a new era of more robust and flexible task automation by LLMs with almost human-level understanding and problem-solving skills. AI agents are becoming much more "human" in terms of what they can do, and we're only just beginning to realize the possibilities this will unlock for enterprise acceleration.

Custom AI Agents Signal Major Workforce And Development Shifts, Says OutSystems ...

Custom AI agents are poised to reshape how enterprises build software and structure their workforces, according to new research from OutSystems. The global survey of 550 software executives found that 93% of companies are actively developing or planning to develop their own AI agents, highlighting the fast-growing adoption of so-called 'agentic AI'. The study, created with CIO Dive and KPMG, suggests businesses are embracing AI agents as a strategic response to mounting pressure for digital transformation, faster innovation, and cost-efficient operations. Agentic AI offers a solution by automating complex processes, integrating siloed systems, and enabling personalised digital interactions at scale. "AI agents are no longer just a concept -- they're becoming operational building blocks within organisations," said Woodson Martin, CEO of OutSystems. "Soon, they'll function like specialised digital teams, identifying business opportunities and refining solutions without direct human intervention." Beyond streamlining development cycles, AI agents are altering the makeup of software teams. OutSystems' data shows that nearly 70% of executives expect AI to introduce new, specialised roles such as agent orchestration and oversight positions. Additionally, 63% anticipate significant reskilling of existing staff to manage and work alongside AI tools. AI's business value is also becoming clearer: more than half of respondents cited improved customer experiences, accelerated software development timelines, and the automation of repetitive tasks as key benefits. Customer service leads current AI agent deployment, but executives are eyeing broader use across product development, marketing, and supply chains. However, the study also underscores risks tied to AI proliferation. Governance and compliance concerns top the list, alongside fears over the transparency of AI decision-making and the growing technical debt caused by uncoordinated AI deployments. Michael Harper of KPMG LLP noted, "Many organisations that began with cautious AI pilots are now seeing tangible productivity and quality gains. This is giving them the confidence to integrate AI more broadly into their operations." As businesses increasingly lean on AI-powered autonomy to scale and innovate, the emergence of AI agents signals a turning point not just for software development, but for workforce strategy and organisational design.