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Oživeno 22:45 | Co se vlastně stalo? O moc chytřejší bohužel nejsme ani po společné tiskové konferenci NASA a Intutive Machines. Její výstup by se dal jednoduše shrnout v několika bodech: Přesná poloha sondy je zatím velkou neznámou – čeká se na přelet družice LRO Sonda komunikuje, byť spojení je ...

The U.S. Secret Service has seized the domain of the sanctioned Russian cryptocurrency exchange Garantex in collaboration with the Department of Justice's Criminal Division, the FBI, and Europol. [...]

Amazon Web Services customers can expect to hear a lot more about agentic AI in future with the news that the company is setting up a dedicated unit to promote the technology on its platform.

The division will be headed by Swami Sivasubramanian, until now Amazon’s VP of AI and data services.

“I’m excited about leading a new organization focused on advancing the power of agents, and look forward to bringing the latest innovation into the hands of our customers,” Sivasubramanian wrote on Wednesday in a LinkedIn post that set out the company’s agentic AI ambitions.

“Agentic systems offer possibilities that extend far beyond today’s chatbots and will drive efficiency like we haven’t seen before. They will orchestrate complex workflows and solve problems with human-like reasoning, while maximizing performance and cost effectiveness at scale,” he wrote. “Agents help developers write and debug code more effectively, allow businesses to automate complex decision-making processes, and create systems that can learn and adapt to new challenges.”

AWS CEO Matt Garman announced the appointment internally on Wednesday, in an email leaked to Reuters.

He promoted the formation of the new division as critical for Amazon’s push into cloud AI services, which he said had the potential to “be the next multi-billion business for AWS.”

“We have the opportunity to help our customers innovate even faster and unlock more possibilities, and I firmly believe that AI agents are core to this next wave of innovation,” Garman wrote.

Autonomous agents

The term ‘agentic AI‘ (not to be confused with ‘AI agents’, which refers to more basic helpmates) is open to some interpretation. Broadly, it embraces the principle that AI systems should be autonomous and able to make decisions in real time without the need for human direction or intervention.

This is the dream: to create systems that can solve complex, multi-step problems on their own. For today’s organizations that find themselves weighed down by ever more complex processes, the ability to hand workload to self-learning, problem-solving systems would be an important evolution.

AWS is already a significant player in this nascent space with the Amazon Bedrock platform (which enables agentic AI creation), and tools such as the Multi-Agent Orchestrator and the Amazon Q developer service. If it were to put them inside a single division it might anchor these elements into a more coherent sales story.

“We’re at an inflection point where enterprises are moving beyond experimentation with AI to seeking tangible business value,” said Chris Ashley, vice president of strategy, GTM & partnerships at UK AI platform company Peak.

“AWS have already made key moves over the past couple of years with their development of AWS Bedrock. A dedicated agentic team and capability is the logical next step for AWS to build on the generative capabilities they’ve been building out.”

For AWS customers, Ashley believed that agentic AI was good news, allowing organizations to automate complex workflows and speed up decision making in ways that would be impossible without the technology.

“We’ve seen clients achieve 20-30% efficiency gains when AI agents are properly deployed within existing cloud architectures,” he said.

Portability in question

However, he said that agentic systems could also lead to platform lock-in: “Technology leaders will ultimately want to ensure their agents can operate fluidly and securely across all clouds, all systems and all processes across the business. This will be a key consideration for AWS to strike the balance between agentic portability and having customers leverage their AWS native services.”

His recommendation for anyone evaluating agentic AI is to prioritize features such as the ability to control, direct, and constrain agentic AI, the degree to which agentic AI will work with existing technology, and how easy it is to track its actions and decision making. 

“AWS’s approach appears strong on infrastructure integration, but enterprises should carefully assess how these aspects are addressed,” said Ashley.

The jury is still out. Agentic AI run from inside a cloud platform could accelerate the ease with which organizations can take on what is a promising but unproven technology. This is the lure of the integrated cloud platform – handing some of the hard work to someone else.

Equally, customers will still need to choose their cloud partners carefully in a rapidly evolving field where certainties about the future are still in short supply.

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New York prosecutors say that two people working at a third-party contractor for the StubHub online ticket marketplace made $635,000 after almost 1,000 concert tickets and reselling them online. [...]

If you overlook the introduction of the iPhone 16e with its own Apple-designed 5G modem along with the release of new iPads, Macs and processors, you might argue that Apple didn’t make an impression at this year’s Mobile World Congress. However, it clearly did, as — thanks to rumors from two of the world’s most well-connected Apple bloggers — its mobile plans are casting a large shadow across the industry.

These plans seem to involve the rapid development of faster and more performant mobile processors, intense effort to bring highly competitive and very fast 5G modems to market, platform-wide implementation of artificial intelligence, and whispers of new mobile devices. The latter include the first ever Apple foldable iPhone and a super-thin model expected this year.

All of these solutions will be supported by the company’s other products and services, giving Apple the kind of integration between mobile and other devices most other hardware vendors can’t even dream of. Even if the 5G modem doesn’t quite match up to everything Qualcomm’s modems currently promise, the additional benefits of platform integration could yet make up the gap. 

But with incumbents in the mobile space working feverishly to bring networked services, including 5G-based services, to market, the importance of the Apple modem cannot be underestimated. Will carriers be able to use these chips to deliver private 5G services effectively? Will Apple look to deploy its software development talents to create systems enterprise developers can use to craft bespoke network services for clients? Will Apple seek to make its unique 5G modem a technology to enable further digital transformation?

Those are the kinds of questions people attending MWC this week may have been talking about. It matters that the C1 modem appeared in the week before the event. The speculation driving the conversation at this year’s show included:

Out of the shadows

We know, because Apple told us, that the company has a road map for C1 development.

Apple analyst Ming-Chi Kuo has added a little more flesh to help us see what those plans may involve. In a post, he says Apple is working to upgrade the C1 next year, likely with the introduction of support for mmWave 5G (which is important in the US and a few other places). The current chip only supports the slightly slower (but far more widely used) sub-6GHz band. The analyst says the main challenge Apple is trying to solve is to build reliable and power efficient tech to support the band.

The idea here is that reception will be consistent and battery life not impacted. Kuo also says the C1 will be used in some additional products, but that Apple won’t put its own modem inside all the iPhones it sells until 2026. We don’t yet know what Apple plans for the C2, but what Kuo has been able to tell us suggests that reliability and energy consumption will remain critical pillars in what it does do.

Under the fold

As tariffs, frightening surveillance plans from an increasingly conflict-obsessed UK at war with its own poorest citizens, and regulation of its business, weigh down Apple’s stock performance, it’s not a big surprise that the pace of Apple rumors is accelerating. When it comes to a folding iPhone (in development for years), the latest claims are that Apple’s foldable device will be slim, fast, and equipped with a fold you can barely see. That hinge will combine stainless steel and titanium alloy.

It will also be equipped with Apple Intelligence, including the situation-aware Apple AI the company seems to be having challenges getting to the mass market. That piece in this jigsaw is coming, however, which means the iPhone fold (and all iPhones) will soon be equipped with cross-app AI integration likely to amplify what you can do with these devices. Kuo also says the device will have a 7.8-in. display when unfolded, making it just slightly smaller than an iPad mini; MWC attendees can breathe a sigh of relief in that the Apple foldable isn’t likely to appear before the end of next year. 

In with the thin

We’ll see an iPhone 17 Air appear later on this year, Kuo claims. This will hold a super- high density battery for excellence in battery life and should be 5.44mm thin — thinner than any other smartphone. Speculation suggests it will use a C modem like the iPhone 16e, sport a 48MP camera, and use that super-fast A-series A17 chip, which is part of what enables Apple to make such thin devices.

Explode into space

Meanwhile, Apple’s work with GlobalStar continues, a space race that opens a new frontier in the mobile industry, support of which may really benefit from Apple’s control of modem production. In other words, once again, Apple doesn’t need to attend MWC to become talk of the town. The world’s leading handset vendor already is that.

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** I za částku do třinácti tisíc korun lze pořídit použitelný notebook ** Výrobci i sem začali osazovat šestijádrové procesory ** Kompromisům se nevyhnete, nejsou ale tak výrazné jako dřív

Generative artificial intelligence (genAI) has evolved quickly during the past two years from prompt engineering and instruction fine-tuning to the integration of external knowledge sources aimed at improving the accuracy of chatbot answers.

GenAI’s latest big step forward has been the arrival of autonomous agents. Agentic AI is based on AI-enabled applications capable of perceiving their environment, making decisions, and taking actions to achieve specific goals. The key word here is “agency,” which allows the software to take action on its own. Unlike genAI tools — which usually focus on creating content such as text, images, and music — agentic AI is designed to emphasize proactive problem-solving and complex task execution.

The simplest definition of an AI agent is the combination of a large language model (LLM) and a traditional software application that can act independently to complete a task.

In 2025, 25% of companies that use genAI will launch agentic AI pilots or proofs of concept, according to report by professional services firm Deloitte. In 2027, that number will grow to half of all companies. “Some agentic AI applications…could see actual adoption into existing workflows in 2025, especially by the back half of the year,” Deloitte said. “Agentic AI could increase the productivity of knowledge workers and make workflows of all kinds more efficient. But the ‘autonomous’ part may take time for wide adoption.”

Agentic AI: 5 things IT leaders need to know Agentic AI – which uses AI agents to perform tasks autonomously – is a rapidly evolving field, and enterprise IT leaders need to be aware of its implications. Here are five things they should know:

Agentic AI goes far beyond traditional automation: Unlike traditional automation, which follows predefined rules, agentic AI systems can autonomously plan, execute, and adapt their actions to achieve complex goals. This means they can handle dynamic and unpredictable situations, making them suitable for tasks that require decision-making and problem-solving.

Security and governance are critical: Agentic AI systems’ autonomy raises concerns about security and governance. Security leaders must implement robust security measures to prevent threats. Enterprises need clear governance frameworks to ensure that agentic AI systems operate ethically and in compliance with regulations. Understanding how to audit and control these systems is vital.

Integration and interoperability is key: Agentic AI systems need to integrate seamlessly with existing IT infrastructure and applications, which require careful planning and consideration of interoperability standards. IT leaders should prioritize solutions that offer open APIs and support for common data formats. The capability for these agents to work with legacy systems will be a key deciding factor for adoption.

Skills gap and talent acquisition: Implementing and managing agentic AI systems requires specialized skills in machine learning, natural language processing and robotics. This requires investing in training and development programs. IT leaders need to attract and retain talent with expertise in agentic AI technologies.

Potential for transformative impact and business disruption: On the one hand, agentic AI has the potential to transform enterprise operations, ranging from customer service and supply chain management to research and development. On the other hand, it poses a risk of business disruption, as it can automate tasks that were previously performed by humans. IT leaders must develop strategies to adapt to the changing landscape.

Tech companies large and small are rushing out genAI-based agents, including Microsoft, which last fall announced it’s adding automated agents to M365 Copilot. Cisco unveiled agents for customer service in October; that same month, Atlassian unveiled its Rovo genAI assistant and Asana announced AI Studio, a tool that can be used to build agents.

In other words, AI agents could soon be as pervasive as other genAI tools in the workplace.

Agentic AI operates in two key ways. First, it offers specialized agents capable of autonomously completing tasks across the open web, in mobile apps, or as an operating system. A specific type of agentic AI, called conversational web agents, functions much like chatbots. In this case, the agentic AI engages users through multimodal conversations, extending beyond simple text chats to accompany them as they navigate the open web or use apps, according to Larry Heck, a professor at Georgia Institute of Technology’s schools of Electrical and Computer Engineering and Interactive Computing.

“Unlike traditional virtual assistants like Siri, Alexa, or Google Assistant, which operate within restricted ecosystems, conversational web agents empower users to complete tasks freely across the open web and apps,” Heck said. “I suspect that AI agents will be prevalent in many arenas, but perhaps the most common uses will be through extensions to web search engines and traditional AI Virtual Assistants like Siri, Alexa, and Google Assistant.”

Other uses for agentic AI

A variety of tech companies, cloud providers, and others are developing their own agentic AI offerings, making strategic acquisitions, and increasingly licensing agentic AI technology from startups and hiring their employees rather than buying the companies outright for the tech. Investors have poured more than $2 billion into agentic AI startups in the past two years, focusing on companies that target the enterprise market, according to Deloitte.

AI agents are already showing up in places you might not expect. For example, most self-driving vehicles today use sensors to collect data about their surroundings, which is then processed by AI agentic software to create a map and navigate the vehicle. AI agents play several other critical roles in autonomous vehicle route optimization, traffic management, and real-time decision-making — they can even predict when a vehicle needs maintenance.

Going forward, AI agents are poised to transform the overall automated driving experience, according to Ritu Jyoti, a group vice president for IDC Research. For example, last year, Nvidia released Agent Driver, an LLM-powered agent for autonomous vehicles that offers more “human-like autonomous driving.”

IDC

These AI agents are also finding their way into a myriad number of industries and uses, from financial services (where they can collect information as part of know-your-client (KYC) applications) to healthcare (where an agentic AI can survey members conversationally and refill prescriptions). The variety of tasks they can tackle can include:

• Autonomous diagnostic systems (such as Google’s DeepMind for retinal scans), which analyze medical images or patient data to suggest diagnoses and treatments.
• Algorithmic trading bots in financial services that autonomously analyze market data, predict trends, and execute trades with minimal human intervention.
• AI agents in the insurance industry that collect key details across channels and analyze the data to give status updates; they can also ask pre-enrollment questions and provide electronic authorizations.
• Supplier communications agents that help customers optimize supply chains and minimize costly disruptions by autonomously tracking supplier performance, and detecting and responding to delays; that frees up procurement teams from time-consuming manual monitoring and firefighting tasks. 
• Sales qualification agents that allow sellers to focus their time on high-priority sales opportunities while the agent researches leads, helps prioritize opportunities, and guides customer outreach with personalized emails and responses, according to IDC’s Ryoti.
• Customer intent and customer knowledge management agents that can make a first impression for customer care teams facing high call volumes, talent shortages and high customer expectations, according to Ryoti.

“These agents work hand in hand with a customer service representative by learning how to resolve customer issues and autonomously adding knowledge-based articles to scale best practices across the care team,” she explained.

And for developers, Cognition Labs in March launched Devin AI, a DIY agentic AI tool that autonomously works through tasks that would typically require a small team of software engineers to tackle. The agent can build and deploy apps end-to-end, independently find and fix bugs in codebases, and it can train and fine tune its own AI models.

Devin can even learn how to use unfamiliar technologies by performing its own research on them.

Notably, AI agents also have the ability to remember past interactions and behaviors. They can store those experiences and even perform “self-reflection” or evaluation to inform future actions, according to IDC. “This memory component allows for continuity and improvement in agent performance over time,” the research firm said in a report.

Other agentic AI systems (such as AlphaGo, AlphaZero, OpenAI’s Dota 2 bot) can be trained using reinforcement learning to autonomously strategize and make decisions in games or simulations to maximize rewards.

Agentic AI software development

Evans Data Corp., a market research firm that specializes in software development, conducted a multinational survey of 434 AI and machine learning developers. When asked what they most likely would create using genAI tools, the top answer was software code, followed by algorithms and LLMs. They also expect genAI to shorten the development lifecycle and make it easier to add machine-learning features.

GenAI-assisted coding allows developers to write code faster — and often, more accurately — using digital tools to create code based on natural language prompts or partial code inputs. (Like some email platforms, the tools can also suggest code for auto-completion as it’s written in real time.)

By 2027, 70% of professional developers are expected to be using AI-powered coding tools, up from less than 10% in September 2023, according to Gartner Research. And within three years, 80% of enterprises will have integrated AI-augmented testing tools into their software engineering toolchain — a significant increase from approximately 15% early last year, Gartner said.

One of the top tools used for genAI-automated software development is GitHub Copilot. It’s powered by genAI models developed by GitHub, OpenAI (the creator of ChatGPT), and Microsoft, and is trained on all natural languages that appear in public repositories.

GitHut combined multiple AI agents to enable them to work hand-in-hand to solve coding tasks; multi-agent AI systems allow multiple applications to work together on a common purpose. For example, GitHub earlier this year launched Copilot Workspace, a technical preview of its Copilot-native developer. The multi-agent system allows specialized agents to collaborate and communicate, solving complex problems more efficiently than a single agent.

With agentic AI coding tools like Copilot Workspace and code-scanning autofix, developers will be able to more efficiently build software that’s more secure, according to a GitHub blog.

The technology could also give rise to less positive results. AI agents might, for example, be better at figuring out online customer intent — a potential red flag for users who have long been concerned about security and privacy when searching and browsing online; detecting their intent could reveal sensitive information. According to Heck, AI agents could help companies understand a user’s intent more precisely, making it easier to “monetize this data at higher rates.

“But this increased granularity of knowledge of the user’s intent can also be more likely to cause security and privacy issues if safeguards are not put in place,” he said.

And while most agentic AI tools claim to be safe and secure, a lot depends on the information sources they use. That’s because the source of data used by the agents could vary — from more limited corporate data to the wide open internet. (The latter has a tendency to affect genAI outputs and can introduce errors and hallucinations.)

Setting guardrails around information access, can act like a boss and set limits on agentic AI actions. That’s why user education and training are critical in the secure implementation and use of AI agents and copilots, according to Anderw Silberman, director of marketing at Zenity, a banking software provider.

“Users need to understand not just how to operate these tools, but also their limitations, potential biases, and security implications,” Silberman wrote in a blog post. Training programs should cover topics such as recognizing and reporting suspicious AI behavior, understanding the appropriate use cases for AI tools, and maintaining data privacy when interacting with AI systems.”

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Over 37,000 internet-exposed VMware ESXi instances are vulnerable to CVE-2025-22224, a critical out-of-bounds write flaw that is actively exploited in the wild. [...]

Akira really wasn't horsing around with this one

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Attacks strike, facilities go bust, patients die. But it's preventable

It will cost upward of $75 million to address the cybersecurity needs of rural US hospitals, Microsoft reckons, as mounting closures threaten the lives of Americans.…