Articles about agentic AI

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.

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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. 

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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.

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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.

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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.

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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.

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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.

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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. 

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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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Large-language models (LLMs) and other forms of generative AI are revolutionizing the way we do business. The impact could be huge: McKinsey estimates that current gen AI technologies could eventually automate about 60-70 percent of employees’ time, facilitating productivity and revenue gains of up to $4.4 trillion. These figures are astonishing given how young gen AI is. (ChatGPT debuted just under two years ago -- and just look at how ubiquitous it is already.)

Nonetheless, we are already approaching the next evolution in intelligent AI: agentic AI. This advanced version of AI builds upon the progress of LLMs and gen AI and will soon enable AI agents to solve even more complex, multi-step problems.

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