New AI analytics platform is designed for enterprise frameworks
As data demands across organizations intensify they need to scale productivity and enable business users to explore data independently.
Cube is launching an agentic AI analytics platform built on a universal semantic layer which allows it to operate autonomously within enterprise frameworks, automating work while preserving trust, governance, and transparency.
Autonomous AI agents aim to streamline enterprise development
The use of AI in software development can save valuable time completing routine tasks. But what if it could autonomously respond to events, implement changes, and submit code through standard pull requests?
This is what Zencoder is doing with the launch today of Autonomous Zen Agents for CI/CD, bringing groundbreaking AI automation directly into the software development infrastructure.
Governance is top priority for agentic AI users
Nearly 80 percent of IT professionals responding to a new survey rank governance as 'extremely important,' underscoring the fact that while organizations are eager to innovate, they still want to do so responsibly
The study by API management firm Gravitee looks at the use of Agentic AI systems and Large Language Models (LLMs) by large and midsize companies and finds 72 percent of respondents report that their organizations are actively using agentic AI systems today.
Half of security issues in Agentic AI code are API-related
A new report from API and AI security solutions company Wallarm finds that of around 4,700 security issues analyzed in Agentic AI projects, 49 percent were API-related, underscoring the inseparable nature of agent and API security.
The report also finds that over 1,000 issues in Agentic AI repositories remain unaddressed. 22 percent of reported security issues remain open too, with some lingering for 1,200-plus days, highlighting a critical gap between vulnerability discovery and remediation.
Making AI agents 'smarter' with connected data
The latest buzzy concept in the world of AI is now transcending its initial hype. Advances in Agentic AI are accelerating the development of autonomous business systems, building upon progress in machine learning. Acting as an independent ‘agent’, the technology can make informed decisions based on the multimodal data and algorithms they’re built on and then ‘learn’ from its experiences.
But even more excitingly, Agentic AI aims to execute tasks independently. It’s this ability to adapt, plan, and execute complex tasks without direct human intervention is what differentiates Agentic AI from its predecessors.
1Password adds protection for agentic AI in the enterprise
Current AI models can perform many tasks such as generating text, but these are 'prompted' -- that is the AI isn't acting by itself. But this is about to change with the arrival of agentic AI.
Gartner estimates that by 2028, 33 percent of enterprise software applications will include agentic AI, up from less than one percent in 2024, enabling 15 percent of day-to-day work decisions to be made autonomously.
How agentic AI takes GenAI to the next level [Q&A]
Agentic AI has been in the news quite a bit of late, but how should enterprises expect it to impact their organizations?
We spoke to Mike Finley, CTO of AnswerRocket, to discuss Agentic AI's benefits, use cases and more.
Why 2025 is the year AI will revolutionize construction
Generative AI is the tech buzzword of the decade -- and the money has followed obediently. In 2025, American Big Tech firms alone announced an investment of $300bn in AI infrastructure. A year earlier, global venture capital investment into AI startups reached approximately $97 billion.
One sector that runs the risk of falling behind is construction. ONS Business Insights report only 12 percent of UK construction businesses use AI, which likely reflects (and contributes to) a more skeptical view of AI in the industry. Compared with UK employees across other industries, 11 percent fewer construction employees are excited by the prospect of AI in the workplace and 34 percent of construction workers are worried about the technology.
The shift from on-call engineers to agentic incident management
Every engineering team I’ve been a part of has had a 10x engineer. They were active contributors to design reviews -- developing a deep, intuitive understanding of the product and every new feature. Beyond writing code, they reviewed every pull request, tracked every change across the product, and kept a mental map of how all the pieces fit together. They were in the right Slack channels, constantly evaluating process or infrastructure changes to understand how their team might be impacted.
They built operational dashboards, and spent the first 15 minutes of their day scanning key metrics to learn what “normal” looked like so they could spot anomalies instantly. They knew their upstream and downstream dependencies, tracked bugs and releases, and stayed up to date on the tools and platforms their team was built on. All of this context led to one inevitable, risky outcome: whenever something broke, they were the only one who knew where to look and how to fix it.
Agentic AI might take years to transform security, but cyber defenders must prepare now
For the past two years, the world has been swept up in a rising tide of GenAI hype. The technology has evolved from a data science curiosity to a pervasive part of our everyday lives. ChatGPT alone has over 300 million weekly users worldwide -- and people use Large Language Models (LLMs) every day to generate text, images, music and more.
Despite GenAI’s widespread success, difficulty in developing robust applications that make use of trustworthy AI systems has proven difficult. This is most clear when noting the delta between consumer-facing GenAI applications relative to B2B integration of GenAI. But, with agentic AI this is about to change.
Would AI super agents mean goodbye to apps as we know them?
In the Western world, we now have an app for everything. Shopping, banking, gaming, and even controlling the temperature in your home - you name it, there’s an app for it. The iOS app store began in 2008 with 500 apps, yet, now there are over four million apps available across iOS and Android platforms. Each of these apps serve individual needs and consumers have learnt to ignore the digital clutter in favor of app loyalty.
Asia went the opposite way. Instead of narrow-purpose-built apps, they built the 'everything app' long before Elon started dreaming about it with platforms like Paytm, Grab and WeChat. But what would it take for the West to catch up? AI super agents might be the answer to that one.
70 percent of organizations are developing AI apps
Over 70 percent of developers and quality assurance professionals responding to a new survey say their organization is currently developing AI applications and features, with 55 percent stating that chatbots and customer support tools are the main AI-powered solutions being built.
The research from Applause surveyed over 4,400 independent software developers, QA professionals and consumers explored common AI use cases, tools and challenges, as well as user experiences and preferences.
New Connectivity Guru uses AI to streamline Wi-Fi troubleshooting
The average home now has multiple devices reliant on Wi-Fi connections and businesses have many times more. Users expect fast, consistent speeds, but troubleshooting problems can be complicated, leading to frustration, while service teams lack the visibility to diagnose issues beyond the router.
With the launch of its Connectivity Guru, TechSee is harnessing Agentic AI to transform the way Wi-Fi issues are diagnosed and resolved.
Celebrating Data Privacy Day: Ensuring ethical agentic AI in our daily interactions
Both AI agents and agentic AI are becoming increasingly powerful and prevalent. With AI agents, we can automate simple tasks and save time in our everyday lives. With agentic AI, businesses can automate complex enterprise processes. Widespread AI use is an inevitability, and the question going forward is not if we’ll use the technology but how well.
In a world where AI takes on more responsibility, we need to know how to measure its effectiveness. Metrics like the number of human hours saved or the costs reduced are, of course, important. But we also need to consider things like how ethically and securely our AI solutions operate. This is true when adopting third-party solutions and when training AI in house.
Why agentic AI could make API threats a $100 billion-problem
APIs are the glue that holds together the modern enterprise. As digital transformation projects get the boardroom green light in ever greater numbers, so the infrastructure connecting software, data and experiences has expanded. Yet a potential storm is coming in 2025, as a new wave of agentic AI innovation takes hold in the enterprise. In fact, Gartner predicts over 30 percent of the increase in demand for APIs will come from AI and tools that use Large Language Models (LLMs)by 2026.
Unless organizations can mature their API security posture, next year could be the first time we see an LLM app security breach linked to APIs. And without improved API observability, it won’t be the last.
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