The success of agentic AI in financial services depends not just on smarter models, but on an authoritative context data store--one that is accessible, reliable, and governed at scale. Financial services companies have unique needs when it comes to business AI. They operate in one of the most highly regulated sectors while responding to external events that are updated by the second. As a result, the success of agentic AI in financial services depends less on the sophistication of the system and more on the quality, security, and accessibility of the data it relies on. "It all starts with the data," says Steve Mayzak, global managing director of Search AI at Elastic. Agentic AI--systems that can independently plan and take actions to complete tasks, rather than simply generate responses--holds enormous potential for financial services due to its ability to incorporate real-time data and optimize complex workflows.

Artificial intelligence has posed a multi-layered problem for Apple in recent years. We're expecting to hear some big news at WWDC this year about how AI will be integrated into the company's gadgets, but there are still other wrinkles still to be ironed out in its broader approach to the use of this influential technology. According to, one of those challenges is the recent interest and development of agentic AI. To date, Apple has not permitted vibe coding tools on the App Store because they would violate its policies. They could also potentially be used to create original apps for people who would have otherwise gotten software from the App Store, which could pose a threat to Apple's revenue as well as creating a loophole for spreading malware or taking other malicious actions. But applying that same block more broadly to any agentic AI services, which can take active control over a device and its programs, could keep Apple out of the loop as those tools are generating a lot of interest among both developers and casual users.

Maybe think twice before letting an AI take over all your tech? According to the publication, an employee used an in-house agentic AI to analyze a query from a second employee on an internal forum. The AI agent posted a response to the second employee with advice even though the first person did not direct it to do so. The second employee took the agent's recommended action, sparking a domino effect that led to some engineers having access to Meta systems that they shouldn't have permission to see. A representative from the company confirmed the incident to and said that no user data was mishandled.

The promise of autonomous agentic AI requires significant changes in the governance landscape. Parents of young children face a lot of fears about developmental milestones, from infancy through adulthood. The number of months it takes a baby to learn to talk or walk is often used as a benchmark for wellness, or an indicator of additional tests needed to properly diagnose a potential health condition. A parent rejoices over the child's first steps and then realizes how much has changed when the child can quickly walk outside, instead of slowly crawling in a safe area inside. Suddenly safety, including childproofing, takes a completely different lens and approach. Generative AI hit toddlerhood between December 2025 and January 2026 with the introduction of no code tools from multiple vendors and the debut of OpenClaw, an open source personal agent posted on GitHub.

As companies race to adopt agentic AI to spur innovation and gain efficiency, building the right enterprise data infrastructure has become a critical component of success. In the race to adopt and show value from AI, enterprises are moving faster than ever to deploy agentic AI as copilots, assistants, and autonomous task-runners. In late 2025, nearly two-thirds of companies were experimenting with AI agents, while 88% were using AI in at least one business function, up from 78% in 2024, according to McKinsey's annual AI report . Yet, while early pilots often succeed, only one in 10 companies actually scaled their AI agents. One major issue: AI agents are only as effective as the data foundation supporting them. Experts argue that most companies are seeing delays in implementing AI, not because of shortcomings in the models, but because they lack data architectures that deliver business context to be reliably used by humans and agents.

Some people are sceptical about whether the socialising of bots is a sign of what is coming with the rise of agentic AI. Some people are sceptical about whether the socialising of bots is a sign of what is coming with the rise of agentic AI. A bit like Reddit for artificial intelligence, Moltbook allows AI agents - bots built by humans - to post and interact with each other. On social media, people often accuse each other of being bots, but what happens when an entire social network is designed for AI agents to use? Moltbook is a site where the AI agents - bots built by humans - can post and interact with each other. It is designed to look like Reddit, with subreddits on different topics and upvoting.

We (AIhub) will be running a short course on science communication on Wednesday 21 January, from 13:00 - 14:30. In this brief tutorial, science communication experts will teach you how to clearly and concisely explain your research to non-specialists.

The head of Amazon Web Services has big plans to offer AI tools to businesses, but says that replacing coders with AI is "a non-starter for anyone who's trying to build a long-term company." Amid the breathless coverage and relentless AI hype of recent years, one of the world's biggest tech companies--Amazon--has been notably absent. Matt Garman, the CEO of Amazon Web Services, is looking to change that. At the recent AWS re:Invent conference, Garman announced a bunch of frontier AI models, as well as a tool designed to let AWS customers build models of their own. That tool, Nova Forge, allows companies to engage in what's known as custom pretraining--adding their data in the process of building a base model--which should allow for vastly more customized models that suit a given company's needs. Sure, it doesn't quite have the sexiness of a Sora 2 announcement, but that's not Garman's goal: He's less interested in mass consumer use of AI and more interested in enterprise solutions that'll integrate AI into all of AWS's offerings--and have a material impact on a corporate P&L. For this week's episode of, I caught up with Garman after AWS re:Invent to talk about what the company announced, whether he feels behind in the AI race, how he thinks about managing huge teams (and managing internal dissent), and why he's not convinced that AI is (or should be) the great job thief of our era. We always start these conversations with some very quick questions, like a warmup. If AWS had a mascot, what would it be? We have a big S3 bucket sometimes that goes around, so we'll call it that. Sorry, what is an S3 bucket? An S3 bucket is like a thing that you store your S3 objects in, but we actually have a large foam big bucket that walks around and actually looks like a paint bucket. So you do have a mascot. Well, S3 has a bucket, it has a mascot. It's probably the closest we have, and I like it. What's the most expensive mistake you've ever made? Personally, the most expensive mistake I ever made was playing basketball too long and I tore my Achilles. So that cost me about nine months of being able to walk. I probably should have known that into my thirties I was well past basketball-playing age.

Large Language Model(LLM) inference demands massive compute and energy, making domain-specific tasks expensive and unsustainable. As foundation models keep scaling, we ask: Is bigger always better for hardware design? Our work tests this by evaluating Small Language Models coupled with a curated agentic AI framework on NVIDIA's Comprehensive Verilog Design Problems(CVDP) benchmark. Results show that agentic workflows: through task decomposition, iterative feedback, and correction - not only unlock near-LLM performance at a fraction of the cost but also create learning opportunities for agents, paving the way for efficient, adaptive solutions in complex design tasks.

Prediction markets allow users to trade on outcomes of real-world events, but are prone to fragmentation with overlapping questions, implicit equivalences, and hidden contradictions across markets. We present an agentic AI pipeline that autonomously (i) clusters markets into coherent topical groups using natural-language understanding over contract text and metadata, and (ii) identifies within-cluster market pairs whose resolved outcomes exhibit strong dependence, including "same-outcome" (correlated) and "different-outcome" (anti-correlated) relationships. Using a historical dataset of resolved markets on Poly-market, we evaluate the accuracy of the agent's relational predictions. We then synthesize discovered relationships into a simple trading strategy to quantify how discovered relationships translate into actionable strategies. Results show that agent-identified relationships have around 60-70% accuracy, and their induced trading strategies have an average return of 20% over week-long horizons, highlighting the ability of agen-tic AI and large language models to uncover latent semantic structure within prediction markets.