Summarize this content to 2000 words in 6 paragraphs in Arabic AI is moving from \u201cco-pilot\u201d to \u201cautopilot\u201d. The development of generative artificial intelligence is increasingly focused on \u201cagentic AI\u201d: the use of AI agents that perform tasks autonomously, either within fixed parameters or to achieve goals set by the user.Bring in the agentsAI agents are not new but they are becoming ever more sophisticated. In their basic form they are simply tools built to carry out tasks such as answering queries to a script, as chatbots do, or fetching information from the web. These functions are limited, requiring no follow-up action without further input. Such reactive AI systems operate solely on programmed responses.\u00a0More complex AI agents, with autonomy and adaptability, have also been around for a long time. They control home thermostats and automate factory processes.\u00a0This type of technology is, however, rapidly developing capabilities beyond fetching and delivering information or performing distinct tasks. AI agents powered by large language models (LLMs) can analyse data, learn from it and make decisions based on both programmed rules and information acquired through interaction with their environment.\u00a0Such adaptable AI can perform increasingly complex actions in pursuit of a goal and without taking a prescribed path. Using advanced machine learning and neural networks, it can understand context, analyse and respond to dynamic situations, learn from experience and use problem-solving and reasoning to make strategic decisions.\u00a0Predictive capabilities based on historical statistical analysis add another layer, enabling AI agents to plan, automate and execute tasks as well as to make informed decisions with specific goals in mind. They carry out their tasks after being given natural language prompts and without constant user input. They can also be designed to check each other\u2019s work in an iterative process that improves quality and reliability.Foundations for progressSeveral developments have enabled AI agents to become more complex while at the same time being easier to use. Generative AI has provided a natural language interface, broadening access to AI, especially for users who are less tech-savvy. Generative AI interprets a prompt by the user then other AI fulfils the task. Google says: \u201cGenerative AI is just one piece of the AI puzzle. Other AI technologies, like predictive AI, vision AI, and conversational AI, are crucial for building sophisticated AI agents.\u201dAdvances in computing power and memory have enabled large language models and more sophisticated machine learning. The understanding of context and the ability to plan has improved as AI systems learn more data and improve their capacity to remember interactions.These are the foundations for AI agents, with the ease of interaction accelerating development as more users gain access. At the same time AI itself is speeding up the innovation cycle, refining its outputs and creating iterative processes at ever higher speeds.Hype or reality?AI agents can speed up analysis and decisions as well as taking over certain functions from employees but they still fall short of full autonomy.\u00a0Cassie Kozyrkov, the founder and chief executive of Decision Intelligence and formerly chief decision scientist at Google, says AI agents\u2019 main role in an enterprise still lies in taking over repetitive tasks with \u201cwell understood and well designed processes\u201d that do not require \u201ccreative spin\u201d.While there is huge potential for agentic AI to perform ever more complex tasks, Pascal Bornet, an expert in automation and author of Agentic Artificial Intelligence, points to a \u201csignificant gap\u201d between hype and reality. Even with a clear directive, systems cannot yet perform complex tasks end to end, especially in nuanced or novel situations, without some human oversight.\u00a0That said, the field \u201cis advancing rapidly\u201d. Bornet likens development to the progression from fully manual to fully autonomous cars, which is rated from level zero to level five. Currently, autonomous cars operate at levels two to four, depending on the environment. Automation can handle many tasks but human oversight, and occasional intervention, is needed.\u00a0AI agents are at a similar stage. Most operate at levels two or three, with some \u201cspecialised systems\u201d reaching level four in tightly defined domains. Level five, where agents fully understand, plan and execute complex missions with minimal human input across any domain or corporate boundary, remains theoretical.Given the challenges involved in folding capabilities into a coherent system, fully integrated multimodal agents are some way off but Bornet says the building blocks are in place. He says some applications, such as that developed for veterinarians by Pets at Home, the UK FT250 company, exemplify audio processing but multimodal systems will require a sophisticated orchestration of agents with different types of expertise.Functional applicationsWhile some sectors have adopted agentic AI more than others, as covered below, it can be put to work in functions that are common to most businesses. Bornet says the opportunity is systemic. \u201cAgentic AI isn\u2019t coming for a [single] department, it\u2019s coming for all of them. Every workflow with friction is a use case waiting to be transformed.\u201d\u00a0Currently agents are used mostly in internal roles to gain efficiency and savings rather than top-line growth. A 2025 report from UK Finance co-authored with Accenture said: \u201cMost near-term uses involve single-agent deployments targeting productivity and efficiency gains and improvements to customer and colleague experience\u201d. The trade body found \u201crelatively few\u201d examples within financial services aimed at increasing sales or revenue. It also noted that most deployments were \u201cclosely monitored by an employee acting as a competent supervisor\u201d.Across industry, AI that can reduce the time spent on mundane work to \u201cfree up\u201d employees for more creative or skilled tasks has been adopted faster than elsewhere.\u00a0Bornet and his team have gathered data from 167 companies in various sectors that have deployed what he classifies as level three LLM-based agents in production environments. Customer service, internal operations, and sales and marketing functions have seen the highest adoption, with benefits ranging from time savings of 12 to 30 per cent in customer service, 30 to 90 per cent in internal operations and increased revenue of nine to 21 per cent for sales and marketing teams.\u00a0It should be noted that the use of AI agents alongside humans does not always enhance performance. An analysis of a customer service software company by the US National Bureau of Economic Research found that AI both improved issue resolution and cut the time taken. However it was newer staff who benefited most, with the AI electronically transferring the knowledge of experienced people. The performance of older hands did not improve.\u00a0The reverse can be true in roles that are highly skilled. Attila Kecsmar, the co-founder and chief executive of Antavo, the AI loyalty cloud programme platform, says that in more technical areas, such as programming, those who use AI without an adequate understanding of the output will struggle, while the productivity and speed of competent workers will be supercharged.\u00a0Customer service This has been the most visible deployment of AI from a consumer perspective but feedback has been mixed. Industry proponents say how well chatbots perform but customer surveys suggest the opposite. Preferences could change as customer service agents develop and digital natives make up more of the consumer base. Better responses and 24\/7 support may improve customer perceptions.\u00a0Older agents answered queries based on set scripts that quickly ran out of road, especially with complex queries. Newer agents, given their ability to remember and respond to dynamic inputs, are more responsive. They are able to act based on up to date client data as well as to recall historical interactions with customers.With agentic AI, customer service interfaces have developed beyond dial-up chatbots. Google Gemini is behind Volkswagen\u2019s MyVW app, a virtual assistant that answers a driver\u2019s queries about their car.Coding The application of AI in coding is well documented. In a report by the McKinsey consultancy, Lenovo said that its engineers\u2019 speed and quality of code production improved by 10 per cent.\u00a0Kecsmar agrees that agent-supported engineers can achieve much more but says this in turn will lead to rising expectations for human productivity and performance. Given natural language interfaces, it is increasingly feasible for laypeople to write code.\u00a0This is the real revolution in agentic AI, Kozyrkov says. \u201cBefore, you had to go and get yourself schooled in the arcane arts of some new language and now you don\u2019t \u2014 you speak your mother tongue and it works.\u201d\u00a0While this presents an opportunity, she cautions that it is also one of the greatest risks in deploying AI in an enterprise. \u201cUnfortunately the mother tongue is vague and not everybody knows when they\u2019re being ambiguous. Now you can program a machine without thinking it through, so it\u2019s hardly a surprise that you get unintended consequences.\u201dMarketing and campaign management As covered in our report on personalisation and marketing, AI has hugely expanded the reach of marketing departments, enabling mass communications to be targeted at ever smaller segments.\u00a0AI agents can take this further. Antavo has developed an AI agent for its brand customers which helps them to devise and communicate loyalty programmes and campaigns. It can decide an appropriate approach for a brand in any sector and analyse data and give ideas, illustrated with charts, on how to optimise and develop a programme. It can also look inwards, finding and delivering relevant information to help customer service employees resolve consumers\u2019 queries.\u00a0Human resources AI agents can be used in hiring, scheduling meetings, retention and management, predicting turnover and identifying where training may be required.Virtual assistants These are capable of executing simple tasks with minimal supervision, such as scheduling meetings with clients, sending standard emails and general client communications. Claude, Anthropic\u2019s AI model, can find information from many sources in a computer so that it can complete a form.Finance Applications include AI systems that can make trading decisions based on real-time data analysis or systems that suggest investment strategies based on a client\u2019s profile. AI can also help with identifying fraud, flagging its suspicions in real time.Healthcare Autonomous diagnostic tools can identify problems using patient histories and images, recommend personalised healthcare treatments, monitor patient health and recommend or remind people about follow-up actions. AI agents can be deployed in robotic-assisted surgery to improve control and accuracy. Pattern recognition, deep learning and computer vision all enhance machines\u2019 ability to adjust surgery incisions in real time. Systems such as Philips\u2019 IntelliVue Guardian manage postsurgical complications by providing early warnings for those patients most at risk.Law In addition to simple and repetitive tasks such as contract drafting, agents can advise on cases. Based on analysis of historical data or judges\u2019 rulings they can predict potential outcomes to a suit and suggest arguments.\u00a0Already A&O Shearman, the international law firm, is using an AI tool created in collaboration with Harvey, a start-up. This makes use of a business\u2019s financial information to assess in which jurisdictions a client needs to file in the event of a merger. It then identifies any missing data and drafts the information requests for each party.Manufacturing and logistics While autonomous cars have yet to reach the mainstream, autonomous lorries are about to arrive. Aurora Innovation, which works with Volvo, Uber and FedEx in the US, plans to use 10 driverless lorries between Dallas and Houston. AI agents are also used in manufacturing for monitoring and maintaining equipment and optimising processes. They can perform quality control on both inputs and outputs with greater consistency than humans.\u00a0Retail Beside the chatbots deployed in customer service, AI agents can be used along the supply chain to monitor and manage inventory levels based on historical data and to predict trends and demands.\u00a0DrawbacksThere are various issues that enterprises need to consider when adopting AI.Companies operating with legacy tech or which have inadequate or inconsistent data will find it harder to make progress. Any data quality issues experienced when training agents will be exacerbated by \u201cslop\u201d the colloquial name for the proliferation of LLM-created content.EY says this could be solved in part by agents sourcing information from several inputs rather than relying on static scraped data. For instance iterative AI could gather data from wearables, which would layer current and contextual data on top of historical information.Generative AI is just one piece of the AI puzzleConnection within and between companies is hampered by data incompatibilities as well as the inadequacies of existing application programming interfaces. Bornet says the lack of a standard protocol presents a hurdle to multi-agent systems that might otherwise cross corporate boundaries.\u00a0Kecsmar believes this problem may itself be solved by agents. \u201cIn future the agents developed around data exchange skills will be able to create their own data exchange. They will be uploaded with how their host company communicates data and they will have a tool call to interface data between different sources.\u201d\u00a0Trust is a problem in several areas, for instance in sectors where the options for reversal are limited. \u201c\u2018Fully automate and leave it\u2019 in the financial services industry is a terrible idea,\u201d Kozyrkov says, adding that \u201cthe golden rule of AI is that it makes mistakes\u201d. Consumers might be unwilling to let agents have autonomy over their bank accounts or credit cards. There is also a lack of trust among leaders in terms of AI performance and with workers who face the risk of replacement. Once systems can link up across business boundaries, will companies trust external agents?\u00a0Use of untrammelled AI also adds to cyber security threats by increasing points of access and the risk of unexpected actions. Kozyrkov says: \u201cOne of the top suggestions is: limit its access. Don\u2019t give it any data that you wouldn\u2019t want leaked.\u201d Granting AI the same access as a human employee dramatically increases the attack surface, meaning systems are more vulnerable.Constraint on computing capacity is a further hurdle. Despite the investment in infrastructure the competition for stretched resources is fierce. Still, no user pays what it costs to run an AI query even in energy terms, a point raised at an FT Climate Capital Council round table last year. For companies using commercial services, current pricing is based on the number of employees \u2014 but what will happen if staff levels shrink due to AI adoption?\u00a0Companies also need to consider the ethical implications of AI adoption. Research at Cambridge university notes that \u2014 if they cannot already \u2014 agents may soon be able to predict our habits and spending patterns and influence or manipulate them, although this is likely to be of greater concern to consumers.Accountability is another imponderable. With whom does this lie when agents are carrying out end to end tasks without human intervention, or with connections to other companies?How to adopt AI agentsAs with any new technology, it is important to identify business needs first. Bornet says the most sophisticated option is not necessarily always the best \u2014 successful implementation lies in choosing the right level for each application.\u00a0\u201cConsider a financial services company implementing AI agents,\u201d he says. \u201cThey might choose level one or two agents for transaction processing, where predictability and audit trails are crucial. However they might implement level three agents for customer service, where adaptability and context awareness are more valuable than strict control.\u201d\u00a0The golden rule of AI is that it makes mistakesKeeping an agent\u2019s function as simple as possible means there is less scope for problems. Bornet recommends starting with repetitive tasks such as meeting documentation and follow-ups.\u00a0Transparency is also key. Bornet says his team has encountered the consequences of both a lack of control over AI adoption and an employee\u2019s unchecked enthusiasm. This ranges from \u201cworker anxiety and resignations in a manufacturing company to reputational damage when agents made unauthorised decisions in a financial firm\u201d. They found that inadequate technical knowledge, governance, or change management stymied adoption in several cases.Kozyrkov, while \u201cincredibly excited for all the ways AI can be used to fuel innovation\u201d, cautions that it must be used wisely. It is vital to have safeguards and to clearly define objectives to avoid unanticipated consequences. \u201cThe future is modularisation. You wouldn\u2019t trust the smartest human to do everything, so why would you trust an AI?\u201d\u00a0She sees people having a central role, even in a future with AI. \u201cIf your goal is to remove humans as quickly as possible, you may find yourself removing key human functions without perhaps realising what you\u2019ve removed.\u201d The most fruitful results, she says, will come to those who see AI agents as a way to \u201celevate the worker\u201d rather than viewing the latter as \u201can overseer for the agentic system\u201d.Designing processes with AI in mind will give an advantage, Kecsmar says, advising that companies should think about developing or deploying AI-native rather than AI-enabled tools. The effect of \u201cnative AI\u201d is more meaningful than what he calls \u201cuplift AI\u201d, where agents such as chatbots simply make jobs easier. This means building AI capabilities from the ground up, not just seeing them as a bolt-on. Companies should think of AI as a strategic capability, they should rethink processes to optimise the function of AI agents.Winners and losersIt is clear that AI is already disrupting workforces. Klarna, the Swedish fintech company, said in late 2024 that it would be able to halve its employee count by using AI, while customer services companies have been changing the mix of human and AI agents. The logistics sector has also seen the effect of AI: Amazon has used autonomous robots in its warehouses for years.This potential for AI agents to unseat entire work teams might delay their adoption in existing businesses, which will give an advantage to start-ups that build agents into processes and systems. For such AI-native companies, agents will be integrated into workflows from day one and they will also act as virtual workers with specialisations previously outside the range of most small companies.\u00a0Kecsmar says Antavo adopted this \u201cAI-first\u201d mindset in developing its agent to help customers plan their loyalty programmes. Rather than design a technology that could take step by step inputs to create a loyalty strategy, the agent digests a brand\u2019s goals and devises an execution plan. Kecsmar believes such tools will turn any company strategy into an executable plan.\u00a0Ultimately AI might also help to devise plans to develop products and markets, shifting its contribution from cost and efficiency to top-line gains.Further advances will be possible once agents can talk to each other across data and company boundaries. Kecsmar believes people will then be able to command specialised agents from different providers to work together via an \u201corchestration layer\u201d. For instance, agents from a marketing specialist could talk to those from point of sale and loyalty specialists to assess a customer\u2019s data and devise a campaign.This could threaten horizontal workflow managers whose selling point is interoperability, for instance third-party logistics fulfilment or customer resources management. In a sign of where things might head, Klarna said it would abandon its use of Workday and Salesforce and develop its own software using AI.\u00a0Not everyone agrees. Kozyrkov says many software-as-a-service companies are building their own agents. \u201cIt will likely make a lot more sense for you to use Agentforce over building your own agent unless there\u2019s some very compelling reason why you wouldn\u2019t want a company that you already trust with that data to be helping you save time using its products.\u201d Connecting that company\u2019s agents to the rest of your business is another matter.ConclusionIt is clear that there is potential for the use of AI agents but companies must have a clear, needs-based strategy and be fully aware of the risks and how to mitigate them.For companies that are early adopters of more advanced agents there will be huge benefits. These systems learn as they go along, which means they improve with time, providing even more advantages than previous, more static technologies.\u00a0\u201cAI agents create what we call \u2018compounding intelligence advantages\u2019,\u201d, Bornet says. \u201cEarly adopters will train agents faster, redefine business models and develop AI expertise,\u201d leaving behind any companies that delay.\u201cAI agents are really going to help those who know what they need done, what it looks like when it\u2019s done and have a way to limit surprises,\u201d Kozyrkov says.<\/p>\n","protected":false},"excerpt":{"rendered":"
Summarize this content to 2000 words in 6 paragraphs in Arabic AI is moving from \u201cco-pilot\u201d to \u201cautopilot\u201d. The development of generative artificial intelligence is increasingly focused on \u201cagentic AI\u201d: the use of AI agents that perform tasks autonomously, either within fixed parameters or to achieve goals set by the user.Bring in the agentsAI agents<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[63],"tags":[],"class_list":{"0":"post-304231","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-tech"},"_links":{"self":[{"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/posts\/304231","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/comments?post=304231"}],"version-history":[{"count":0,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/posts\/304231\/revisions"}],"wp:attachment":[{"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/media?parent=304231"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/categories?post=304231"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/globetimeline.com\/ar\/wp-json\/wp\/v2\/tags?post=304231"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}