The partners say the platform, a first of its kind, is a major step in Europe’s industrial digital transformation

أطلقت شركة إنفيديا وديتشه تيليكوم منصة سحابية للذكاء الاصطناعي بقيمة 1.2 مليار دولار، مما يمثل خطوة كبيرة في التحول الرقمي الصناعي في أوروبا. من المتوقع أن تعزز هذه المبادرة القدرات التكنولوجية عبر مختلف القطاعات في المنطقة.

Nvidia y Deutsche Telekom han lanzado una innovadora plataforma de AI Cloud con una inversión de 1.2 mil millones de dólares, marcando un avance significativo en la transformación digital industrial de Europa. Esta iniciativa se espera que mejore las capacidades tecnológicas en varios sectores de la región.

Nvidia et Deutsche Telekom ont lancé une plateforme AI Cloud révolutionnaire avec un investissement de 1,2 milliard de dollars, marquant une avancée significative dans la transformation numérique industrielle de l'Europe. Cette initiative devrait améliorer les capacités technologiques dans divers secteurs de la région.

Nvidia and Deutsche Telekom have launched a groundbreaking AI Cloud platform with an investment of $1.2 billion, marking a significant advancement in Europe's industrial digital transformation. This initiative is expected to enhance technological capabilities across various sectors in the region.

Nvidia and Deutsche Telekom Launch $1.2B AI Cloud

<A HREF="https://fortune.com/2025/11/19/exclusive-doppel-raises-70-million-series-c-at-more-than-600-million-valuation-to-fight-ai-powered-social-engineering-attacks/"><IMG VSPACE="4" HSPACE="4" BORDER="0" ALIGN="RIGHT" SRC="http://www.techmeme.com/251119/i39.jpg"></A>
<A HREF="http://www.techmeme.com/251119/p39#a251119p39" TITLE="Techmeme permalink"><IMG WIDTH=11 HEIGHT=12 SRC="http://www.techmeme.com/img/pml.png" STYLE="border:none;padding:0;margin:0;"></A> Allie Garfinkle / <A HREF="http://www.fortune.com/">Fortune</A>: 
<A HREF="https://fortune.com/2025/11/19/exclusive-doppel-raises-70-million-series-c-at-more-than-600-million-valuation-to-fight-ai-powered-social-engineering-attacks/">Doppel, which makes an AI social engineering detection service, raised a $70M Series C led by Bessemer at a $600M+ valuation, up from $205M in May</A>&nbsp; &mdash;&nbsp; Senior Finance Reporter And Author Of Term Sheet&nbsp; &mdash;&nbsp; I tried to fool my brother, sort of.&nbsp; &mdash;&nbsp; Next to him and his Pekingese on the couch &hellip;

دوبل، وهي شركة متخصصة في خدمات الكشف عن الهندسة الاجتماعية باستخدام الذكاء الاصطناعي، نجحت في جمع 70 مليون دولار في جولة تمويل من السلسلة C بقيادة بيسمر. وقد زادت هذه الجولة من تقييم الشركة إلى أكثر من 600 مليون دولار، ارتفاعًا من 205 مليون دولار في مايو.

Doppel, una empresa que se especializa en servicios de detección de ingeniería social mediante IA, ha recaudado con éxito 70 millones de dólares en una ronda de financiación Serie C liderada por Bessemer. Esta ronda ha elevado la valoración de la empresa a más de 600 millones de dólares, un aumento significativo desde los 205 millones en mayo.

Doppel, une entreprise spécialisée dans les services de détection d'ingénierie sociale par IA, a levé avec succès 70 millions de dollars lors d'un tour de financement de série C dirigé par Bessemer. Ce tour de financement a porté la valorisation de l'entreprise à plus de 600 millions de dollars, contre 205 millions en mai.

Doppel, a company specializing in AI social engineering detection services, has successfully raised $70 million in a Series C funding round led by Bessemer. This funding round has increased the company's valuation to over $600 million, a significant rise from $205 million in May.

Doppel, which makes an AI social engineering detection service, raised a $70M Series C led by Bessemer at a $600M+ valuation, up from $205M in May (Allie Garfinkle/Fortune)

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<a href="https://fetch.ai/">Fetch AI</a>, a startup founded and led by former DeepMind founding investor, Humayun Sheikh, <a href="https://www.businesswire.com/news/home/20251119088395/en/Fetch-Combines-Personalized-AI-with-Multi-Agent-Collaboration-to-Handle-Complex-Consumer-Tasks-Launches-Claim-Your-Agent-to-Fight-Brand-Knock-Offs">today announced the release</a> of three interconnected products designed to provide the trust, coordination, and interoperability needed for large-scale AI agent ecosystems. The launch includes <a href="https://asi1.ai/">ASI:One</a>, a personal-AI orchestration platform; <a href="https://business.fetch.ai/">Fetch Business</a>, a verification and discovery portal for brand agents; and <a href="https://agentverse.ai/?sort=relevancy&amp;page=1&amp;recommended=true">Agentverse</a>, an open directory hosting more than two million agents. Together, the system positions Fetch as an infrastructure provider for what it calls the “Agentic Web”—a layer where consumer AIs and brand AIs collaborate to complete tasks instead of merely suggesting them.The company says the tools address a central limitation in current consumer AI: models can provide recommendations but cannot reliably execute multi-step actions that require coordination across businesses. Fetch’s approach centers on enabling agents from different organizations to interoperate securely, using verified identities and shared context to complete end-to-end workflows.“We’re creating the same foundation for agents that Google created for websites,” said Humayun Sheikh, Founder and CEO of Fetch AI, and an early investor in DeepMind, in a press release provided to VentureBeat. “Instead of just finding information, your personal AI coordinates with verified brand agents to get things done.”<h2>Background: Fetch’s Founding and DeepMind Connection </h2>Fetch AI was founded in 2017 by Humayun Sheikh, an entrepreneur whose early investment in DeepMind helped support the company’s commercial development before its acquisition by Google. “I was one of the first five people at DeepMind and its first investor. My check was the first one in,” Sheikh said, reflecting on the period when advanced machine learning research was still largely inaccessible outside major technology companies.His early experience helped shape Fetch’s direction. “Even in 2013, it was clear to me that agentic systems were going to be the ones that worked. That’s where I focused—on the agentic web,” Sheikh noted. Fetch built on this thesis by developing infrastructure for autonomous software agents, focusing on verifiable identity, secure data exchange, and multi-agent coordination. Over the past several years, the company has expanded to a 70-person team across Cambridge and Menlo Park, raised approximately $60 million, and accumulated more than one million users interacting with its model—data that informed the design of the newly launched products.Sheikh added that his decision to bootstrap the company initially came directly from the proceeds of the DeepMind exit, noting in the interview that while the sale to Google was “a good exit,” he believed the team could have held out for a higher valuation. The early self-funding period allowed Fetch to begin work in 2015—well before transformer architectures went mainstream—on the hypothesis that agentic infrastructure would become foundational to applied AI.<h2>ASI:One — A Platform for Multi-Agent Orchestration</h2>At the core of the launch is ASI:One, a language model interface designed specifically for coordinating multiple agents rather than addressing isolated queries. Fetch describes it as an “intelligence layer” that handles context sharing, task routing, and preference modeling.The system stores user-level signals such as favored airlines, dietary constraints, budget ranges, loyalty program identifiers, and calendar availability. When a user requests a complex task—such as planning a trip with flights, hotels, and restaurant reservations—ASI:One retrieves those preferences and delegates work to the appropriate verified agents. The agents then return actionable outputs, including inventory and booking options, rather than generic recommendations.In practice, ASI:One functions as a workflow generator across organizational boundaries. By contrast with conventional LLM applications, which often rely on APIs or RAG techniques to surface information, ASI:One is built to coordinate autonomous agents that can complete transactions. Fetch notes that personalization improves over time as the model accumulates structured preference data.Sheikh emphasized the distinction between orchestrated execution and traditional AI output. “This isn’t searching for options separately and hoping they work together,” he said. “It’s orchestration.” He added that Fetch’s architecture is intentionally modular: “Our architecture is a mix of agentic and expert models. One large model isn’t enough—you need specialists. That’s why we built ASI1, tuned specifically for agentic systems.”The interview also revealed new details about ASI:One’s personalization systems: the platform uses multiple user-owned knowledge graphs to store preferences, travel history, social connections, and contextual constraints. These knowledge graphs are siloed per user and not co-mingled with any Fetch-operated data. Sheikh described this as a “deterministic backbone” that gives the personal AI a stable memory layer beyond the probabilistic output of a single large model.ASI:One launches in Beta today, with a broader release planned for early 2026. Fetch also offers ASI:One Mobile, released earlier this year, giving users access to the same agent-orchestration capabilities on iOS and Android. The mobile app connects directly to Agentverse and the user’s knowledge graphs, enabling on-the-go task execution and real-time interaction with registered agents.<h2>Fetch Business — Verified Identity and Brand Control</h2>To enable reliable coordination between consumers and companies, Fetch is introducing a verification and discovery portal called Fetch Business. The platform allows organizations to verify their identity and claim an official Brand Agent handle—for example, @Hilton or @Nike—regardless of which tools they use to build the underlying agent.Fetch positions the product as an analogue to ICANN domain registration and SSL certificate systems for websites. Verified status is intended to protect consumers from interacting with counterfeit or untrusted agents, a problem the company describes as a major barrier to widespread agent adoption.The system includes low-code tools for small businesses to create agents in a few steps and connect real-time APIs such as inventory, booking systems, or CRM platforms. “With Fetch, you can create an agent in one minute. It gets a handle, like a Twitter username, and you can personalize it completely—even give it your social media permissions to post on your behalf,” Sheikh said. Once a brand claims its namespace, its agent becomes discoverable to consumer AIs and other agents inside Agentverse.The company has pre-reserved thousands of brand namespaces in anticipation of demand. Verification status persists across any platform that integrates with Agentverse, creating a portable identity layer for business agents.The interview highlighted that Fetch Business inherits web-trust primitives directly: domain owners verify their identity by inserting a short code snippet into their existing website backend, allowing the system to pass a cryptographic challenge and grant the agent an authenticity badge similar to a “blue check” for agent identities. Sheikh framed this as “reusing the trust layer the web already spent decades building.”Companies can begin claiming agents now at <a href="https://business.fetch.ai/">business.fetch.ai</a>.<h2>Agentverse — An Open Directory of More Than Two Million Agents</h2>The final component of the release is <a href="https://agentverse.ai/">Agentverse</a>, an open directory and cloud platform that hosts agents and enables cross-ecosystem discoverability. Fetch states that millions of agents have already registered, spanning travel, retail, entertainment, food service, and enterprise categories.Agentverse provides metadata, capability descriptions, and routing logic that ASI:One uses to identify appropriate agents for specific tasks. It also supports secure communication and data exchange between agents. The company notes that the directory is platform-agnostic: agents built with any framework can join and interoperate.According to Sheikh, the lack of a discovery layer is one reason most AI agents see little or no usage. “Ninety percent of AI agents never get used because there’s no discovery layer,” he said. He framed the role of Agentverse in more technical terms: “Right now, if you build an agent, there’s no universal way for others to discover it. That’s what AgentVerse solves—it’s like DNS for agents.” He also described the system as an essential component of the emerging agent economy: “Fetch is building the Google of agents. Just like websites needed search, agents need discovery, trust, and interaction—Fetch provides all of that.”The interview further underscored that Agentverse is cloud-agnostic by design. Sheikh contrasted this with competing agent ecosystems tied to specific cloud providers, arguing that a universal registry is only viable if independent of proprietary cloud environments. He said the open architecture enables an LLM to query any agent “within one minute of deployment,” turning agent publication into a near-instantaneous process similar to registering a domain.Agentverse also integrates payment pathways, enabling agents to execute purchases using partners such as Visa, Skyfire, and supported stablecoins. Consumers can configure spending limits or require explicit approval for transactions.<h2>Industry Context and Implications</h2>Fetch’s launch comes at a time when consumer AI platforms are exploring the shift from static chat interfaces toward autonomous agents capable of completing actions. However, most agent systems remain limited by siloed architectures, limited interoperability, and weak verification standards.Fetch positions its infrastructure as a response to these limitations by providing a cross-platform coordination layer, identity system, and directory service. The company argues that an agent ecosystem requires consistent verification mechanisms to ensure that consumers interact with authentic brand representatives rather than imitations. By establishing namespace control and portable trust indicators, Fetch Business aims to fill a gap similar to early web domain verification.At the same time, ASI:One attempts to centralize user preference data in a way that enables more efficient personalization and multi-agent coordination. This approach differs from generalist LLM applications, which often lack persistent preference architectures or direct access to brand-controlled agents.The interview also made clear that micropayments and digital transaction infrastructure are central to Fetch’s long-term vision. Sheikh referenced integrations with protocols such as Coinbase’s 402 and AP2, positioning these capabilities as essential for autonomous agents to complete end-to-end tasks that include financial execution.<h2>Takeaway</h2>Fetch’s combined release of ASI:One, Fetch Business, and Agentverse introduces an interconnected stack designed to support large-scale deployment and usage of AI agents. The company frames the system as foundational infrastructure for an agentic ecosystem, where consumer AIs can coordinate with verified brand agents to complete tasks reliably and securely. The additions to its identity, discovery, and orchestration layers reflect Fetch’s long-standing thesis—rooted partly in lessons from DeepMind’s early development—that intelligence becomes meaningful only when paired with the capacity to act.

أطلقت شركة Fetch AI، وهي شركة ناشئة يقودها هميون شيخ، ثلاثة منتجات مترابطة تهدف إلى تعزيز نظام وكلاء الذكاء الاصطناعي. تشمل العروض الجديدة ASI:One، وهي منصة تنسيق ذكاء اصطناعي شخصي، وFetch Business، وهو بوابة للتحقق والاكتشاف لوكلاء العلامات التجارية، وAgentverse، وهو دليل مفتوح يضم أكثر من مليوني وكيل. تسعى هذه المبادرة إلى إنشاء بنية تحتية قوية لما تصفه Fetch بـ 'الويب الوكالي.'

Fetch AI, una startup dirigida por Humayun Sheikh, ha lanzado tres productos interconectados destinados a mejorar el ecosistema de agentes de IA. Las nuevas ofertas incluyen ASI:One, una plataforma de orquestación de IA personal, Fetch Business, un portal de verificación y descubrimiento para agentes de marca, y Agentverse, un directorio abierto con más de dos millones de agentes. Esta iniciativa busca establecer una infraestructura sólida para lo que Fetch describe como la 'Web Agente.'

Fetch AI, une startup dirigée par Humayun Sheikh, a lancé trois produits interconnectés visant à améliorer l'écosystème des agents IA. Les nouvelles offres comprennent ASI:One, une plateforme d'orchestration d'IA personnelle, Fetch Business, un portail de vérification et de découverte pour les agents de marque, et Agentverse, un annuaire ouvert avec plus de deux millions d'agents. Cette initiative vise à établir une infrastructure robuste pour ce que Fetch décrit comme le 'Web Agentique.'

Fetch AI, a startup led by Humayun Sheikh, has launched three interconnected products aimed at enhancing the ecosystem of AI agents. The new offerings include ASI:One, a personal-AI orchestration platform, Fetch Business, a verification and discovery portal for brand agents, and Agentverse, an open directory with over two million agents. This initiative seeks to establish a robust infrastructure for what Fetch describes as the 'Agentic Web.'

The Google Search of AI agents? Fetch launches ASI:One and Business tier for new era of non-human web

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<h1>
 
 
 Sector HQ Weekly Digest - November 17, 2025
</h1>

Who's shipping vs who's just talking? Here's this week's AI industry intelligence.

<h2>
 
 
 🏆 Top 10 Companies
</h2>

<ol>
<li>
OpenAI - Score: 442385.7 | 343 events this week</li>
<li>
Anthropic - Score: 266113.1 | 51 events this week</li>
<li>
Amazon - Score: 135242.8 | 22 events this week</li>
<li>
Nvidia - Score: 128360.7 | 161 events this week</li>
<li>
Google - Score: 111135.8 | 125 events this week</li>
<li>
Microsoft - Score: 95559.7 | 99 events this week</li>
<li>
Meta - Score: 78015.6 | 61 events this week</li>
<li>
Apple - Score: 64936.3 | 94 events this week</li>
<li>
Tesla - Score: 41242.1 | 12 events this week</li>
<li>
AMD - Score: 40235.9 | 43 events this week</li>
</ol>

<h2>
 
 
 📈 Biggest Movers
</h2>

<ul>
<li>↑ Sony jumped 277 positions to #58</li>
<li>↑ Stability AI jumped 183 positions to #78</li>
<li>↑ Bytedance jumped 143 positions to #68</li>
<li>↑ Scale AI jumped 122 positions to #66</li>
<li>↑ Palantir jumped 107 positions to #17</li>
</ul>

<h2>
 
 
 🚨 Hype Gap Alerts
</h2>

No high hype alerts this week

<h2>
 
 
 📊 Key Insights
</h2>

<ul>
<li>Total companies tracked: 100</li>
<li>Total events this week: 1317</li>
<li>Average activity per company: 13.2 events</li>
</ul>

<h2>
 
 
 💡 What This Means
</h2>

The AI industry continues to evolve rapidly. Companies that ship consistently rise in our rankings, while those focused on hype alone get flagged by our Hype Gap detector.

Methodology: Our leaderboard tracks real product releases, funding events, partnerships, and market traction - not just PR and social media buzz.




Want real-time updates? Check out the live leaderboard at <a href="https://sectorhq.co" rel="noopener noreferrer">sectorhq.co</a>

Track specific companies and get instant alerts when they move in the rankings.

<h2>
 
 
 Tags
</h2>

<h1>
 
 
 AI #ArtificialIntelligence #MachineLearning #TechIndustry #Startups #AILeaderboard
</h1>

تسليط الضوء على أحدث التطورات في صناعة الذكاء الاصطناعي في نشرة قطاع HQ الأسبوعية بتاريخ 17 نوفمبر 2025، مع التركيز على أداء الشركات الرائدة. تتصدر OpenAI القائمة برصيد 442385.7 و343 حدثًا، تليها Anthropic وAmazon. كما يشير التقرير إلى تحركات كبيرة، حيث قفزت Sony 277 مركزًا في الترتيب، مما يعكس الطبيعة الديناميكية للقطاع.

El Digest semanal de Sector HQ del 17 de noviembre de 2025 destaca los últimos desarrollos en la industria de la IA, centrándose en el rendimiento de las principales empresas. OpenAI lidera con una puntuación de 442385.7 y 343 eventos, seguido de Anthropic y Amazon. El informe también señala movimientos significativos, con Sony saltando 277 posiciones en el ranking, reflejando la naturaleza dinámica del sector de la IA.

Le Digest hebdomadaire du Secteur HQ du 17 novembre 2025 met en lumière les derniers développements de l'industrie de l'IA, en se concentrant sur la performance des principales entreprises. OpenAI est en tête avec un score de 442385,7 et 343 événements, suivi d'Anthropic et d'Amazon. Le rapport note également des mouvements significatifs, Sony ayant grimpé de 277 positions dans le classement, reflétant la nature dynamique du secteur de l'IA.

The Sector HQ Weekly Digest for November 17, 2025, highlights the latest developments in the AI industry, focusing on the performance of top companies. OpenAI leads with a score of 442385.7 and 343 events, followed by Anthropic and Amazon. The report also notes significant movements, with Sony jumping 277 positions in the rankings, reflecting the dynamic nature of the AI sector.

Sector HQ Weekly Digest - November 17, 2025

arXiv:2511.10909v1 Announce Type: cross 
Abstract: The rapidly growing computation demands of deep neural networks (DNNs) have driven hardware vendors to integrate matrix multiplication accelerators (MMAs), such as NVIDIA Tensor Cores and AMD Matrix Cores, into modern GPUs. However, due to distinct and undocumented arithmetic specifications for floating-point matrix multiplication, some MMAs can lead to numerical imprecision and inconsistency that can compromise the stability and reproducibility of DNN training and inference.
  This paper presents MMA-Sim, the first bit-accurate reference model that reveals the detailed arithmetic behaviors of the MMAs from ten GPU architectures (eight from NVIDIA and two from AMD). By dissecting the MMAs using a combination of targeted and randomized tests, our methodology derives nine arithmetic algorithms to simulate the floating-point matrix multiplication of the MMAs. Large-scale validation confirms bitwise equivalence between MMA-Sim and the real hardware. Using MMA-Sim, we investigate arithmetic behaviors that affect DNN training stability, and identify undocumented behaviors that could lead to significant errors.

تقدم الورقة نموذج MMA-Sim، وهو نموذج مرجعي دقيق بت-bit يقوم بتحليل السلوكيات الحسابية لمسرعات ضرب المصفوفات (MMAs) المستخدمة في وحدات معالجة الرسوميات الحديثة، وخاصة نوى Tensor من NVIDIA ونوى Matrix من AMD. مع تزايد الطلبات الحسابية على الشبكات العصبية العميقة (DNNs)، يمكن أن تؤدي المواصفات الحسابية المتميزة لهذه المسرعات إلى عدم دقة عددية تؤثر على استقرار تدريب DNN واستنتاجه. يكشف MMA-Sim عن خوارزميات حسابية مفصلة ويؤكد على التكافؤ بت-bit مع الأجهزة الحقيقية من خلال التحقق الواسع.

El artículo presenta MMA-Sim, un modelo de referencia bit-preciso que analiza los comportamientos aritméticos de los aceleradores de multiplicación de matrices (MMA) utilizados en las GPU modernas, específicamente los Tensor Cores de NVIDIA y los Matrix Cores de AMD. Con el aumento de las demandas computacionales de las redes neuronales profundas (DNN), las especificaciones aritméticas distintas de estos MMA pueden llevar a imprecisiones numéricas que afectan la estabilidad del entrenamiento y la inferencia de las DNN. MMA-Sim revela algoritmos aritméticos detallados y confirma la equivalencia…

Cet article présente MMA-Sim, un modèle de référence bit-précis qui analyse les comportements arithmétiques des accéléra­teurs de multiplication matricielle (MMA) utilisés dans les GPU modernes, en particulier les Tensor Cores de NVIDIA et les Matrix Cores d'AMD. Avec l'augmentation des demandes de calcul des réseaux de neurones profonds (DNN), les spécifications arithmétiques distinctes de ces MMA peuvent entraîner une imprécision numérique, affectant la stabilité de l'entraînement et de l'inférence des DNN. MMA-Sim révèle des algorithmes arithmétiques détaillés et confirme l'équivalence bit-…

The paper presents MMA-Sim, a bit-accurate reference model that analyzes the arithmetic behaviors of matrix multiplication accelerators (MMAs) used in modern GPUs, specifically NVIDIA Tensor Cores and AMD Matrix Cores. With the increasing computational demands of deep neural networks (DNNs), the distinct arithmetic specifications of these MMAs can lead to numerical imprecision, affecting DNN training and inference stability. MMA-Sim reveals detailed arithmetic algorithms and confirms bitwise equivalence with real hardware through extensive validation.

MMA-Sim: Bit-Accurate Reference Model of Tensor Cores and Matrix Cores

The arrangements put LLM maker Anthropic &quot;on a rocket ship,&quot; Nvidia's Jensen Huang said.

أعلنت مايكروسوفت وإنفيديا وأنتروبيك عن شراكات جديدة تهدف إلى تعزيز قدرات الذكاء الاصطناعي. وصف جينسن هوانغ، الرئيس التنفيذي لإنفيديا، التعاون بأنه يضع أنتروبيك 'على صاروخ'. من المتوقع أن تعزز هذه الشراكة بشكل كبير تطوير ونشر تقنيات الذكاء الاصطناعي المتقدمة.

Microsoft, Nvidia y Anthropic han anunciado nuevas asociaciones destinadas a mejorar las capacidades de inteligencia artificial. El CEO de Nvidia, Jensen Huang, describió la colaboración como poner a Anthropic 'en un cohete'. Se espera que esta asociación impulse significativamente el desarrollo y la implementación de tecnologías avanzadas de IA.

Microsoft, Nvidia et Anthropic ont annoncé de nouveaux partenariats visant à améliorer les capacités en intelligence artificielle. Le PDG de Nvidia, Jensen Huang, a décrit cette collaboration comme propulsant Anthropic 'sur une fusée'. Ce partenariat devrait considérablement renforcer le développement et le déploiement de technologies avancées en IA.

Microsoft, Nvidia, and Anthropic have announced new partnerships aimed at enhancing artificial intelligence capabilities. Nvidia's CEO, Jensen Huang, described the collaboration as putting Anthropic 'on a rocket ship.' This partnership is expected to significantly boost the development and deployment of advanced AI technologies.

Microsoft, Nvidia and Anthropic Reveal New Partnerships

A recent IBM panel discussion examined cases in which U.K. enterprises are succeeding with AI agents and what challenges still lie ahead.

ناقشت حلقة نقاش حديثة من IBM حالات نجاح الشركات البريطانية في استخدام وكلاء الذكاء الاصطناعي، مع التركيز على القيمة التي تحققها هذه الشركات من هذه التقنيات والتحديات التي لا تزال قائمة في دمجها.

Una reciente discusión en un panel de IBM destacó las implementaciones exitosas de agentes de IA por parte de empresas del Reino Unido, centrándose en el valor que están obteniendo de estas tecnologías y los desafíos que aún persisten en su integración.

Une récente discussion de panel d'IBM a mis en lumière les mises en œuvre réussies d'agents IA par des entreprises britanniques, en se concentrant sur la valeur qu'elles tirent de ces technologies et sur les défis qui subsistent dans leur intégration.

A recent IBM panel discussion highlighted successful implementations of AI agents by U.K. enterprises, focusing on the value they are deriving from these technologies and the challenges that remain in their integration.

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