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A new era of intelligent automation is underway. We are seeing digital agents take on responsibilities that once required constant human supervision. Today, these systems, can make independent decisions, implement them, and continuously learn from their experiences.

Simply put, agentic AI listens, learns, and develops strategies capable of revolutionizing how we work, especially in network operations where it shifts from reactive to proactive, improving resilience and security. It can automate network management, real-time threat detection, and traffic optimization, enhancing efficiency, strengthening security, and boosting network performance for seamless and secure operations.

But how can it be implemented, where can it have the biggest benefit, what is the role of human oversight and what lessons can we learn from the introduction of agentic AI? In this article, I’ll cover these key points and give advice to businesses looking to harness its potential.

The successful implementation of agent-based AI systems requires careful planning.

Firstly, it is important to clearly define goals and key performance indicators for their use. Then, a major challenge is the seamless integration of the solutions into the existing IT network infrastructure. Training and operation of the systems also require the availability of sufficient and high-quality data. Finally, there are ethical considerations of implementing agentic AI that companies need to address from the outset, such as data privacy, protection, governance, human oversight and transparency, to ensure trust is built.

Agentic AI requires guidelines over which data it can access, from where, and whether it is able to share certain data externally. This is imperative to consider within an AI strategy to ensure that your customers and your organization are protected from data and regulatory breaches, such as the EU AI Act.

If your implementation plan takes these considerations into account, nothing stands in the way of the effective use of agentic AI. With digital agents, businesses can streamline their operations, meeting rising customer service expectations. A report by Gartner predicts that by 2029, AI will resolve 80% of common customer service issues without human intervention. These agents analyze customer sentiment in real time and provide tailored responses enhancing customer engagement.

Agentic AI is now playing a pivotal role in network infrastructure and cybersecurity, helping organizations move beyond traditional, rule-based systems. Unlike conventional tools that passively monitor and alert, digital agents can actively observe network behavior, identify anomalies in real time, and take autonomous action to resolve emerging threats. This enables a faster response to incidents, reducing downtime, and therefore helps avoid costly disruptions.

Agentic AI is already being embedded across networking and security infrastructure to deliver real-time, measurable value. The NSaaS model (Networking and Security as a Service) is evolving into something more dynamic, where agentic capabilities enable smart routing, adaptive policy enforcement, and predictive resource allocation. These enhancements ensure better performance, greater visibility, and stronger protection for global customers operating in complex conditions.

There is growing demand for integrated cybersecurity and networking solutions from cloud providers, with many organizations viewing this convergence as essential to enterprise resilience. In this setting, agentic AI offers a unique advantage; it blends machine learning with autonomous decision-making, allowing digital agents to adapt in real time while maintaining stable and efficient network operations. This shift from static systems to intelligent, self-improving agents is reshaping how businesses think about their digital foundations. With this strategic mindset, early adoption of agentic AI gives network providers a chance to get ahead of the curve with smarter services, improved reliability, and a more personalized customer experience.

While we are still at the beginning of the AI journey and its potential is yet to be fully realized, McKinsey found that 77% of companies are either using AI or exploring its potential. It has already changed workflows, it still requires a level of human management, but agentic AI enables new possibilities. It can become more than a support tool. It can become an active participant in business operations, freeing up resources and creating greater efficiency. In networking specifically, the benefits are becoming clear. While machine learning has been used for tasks like digital twins and anomaly detection, agentic AI can manage these processes autonomously. This reduces the need for human intervention at every step and enables networks to become more resilient, secure, and adaptive to real-time demands.

Nevertheless, learning and development around AI in the workforce remains a business imperative. Counterintuitively, while flawed data is often tolerated in human decision-making, we don’t have the same leniency with AI. According to Gartner, 30% of generative AI projects are abandoned after the proof-of-concept stage, primarily due to issues related to data quality, risk management or high costs, highlighting the difficulty organizations face in AI initiatives and importance of getting implementation right from the off.

For some companies, agentic AI could mark a shift from promise to performance - where AI becomes not just an experiment, but a business-critical capability aligned to strategic goals.

As businesses advance their AI capabilities, agentic systems offer a strategic step forward. They enable organizations to align automation with core objectives, turning networks into adaptive ecosystems. For organizations moving from passive AI and aiming to build intelligent, responsive systems, agentic AI is the key enabler. This is not just a technological shift, but a reimagining of what network infrastructure can achieve when paired with AI designed to think, act, and adapt.

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Meta’s move to take a significant stake in Scale AI isn’t just another strategic investment. It’s an admission: human data is the critical infrastructure needed to build better AI, faster.

For years, model architecture and compute have dominated the conversation. But we’re entering a new era, one where the differentiator isn’t how novel your transformer is, but how well your model reflects and responds to real human experience. That demands high-quality, diverse, and continuous human input throughout the development lifecycle.

Scale’s primary service—labelling data outputs using human annotators—has long been essential to AI. But it hasn’t always been glamorous. Data preparation was often seen as a backroom task, while shiny model architectures stole the limelight.

Meta’s investment sends a clear message. The training and evaluation of AI models depend on data that is not just abundant, but accurate, representative, and human-validated. It’s a strategic move that gives Meta both privileged access to Scale’s data infrastructure and a highly influential stake in a key player in the data annotation space.

But therein lies a broader concern: when a major tech company takes a significant stake in a service provider, potential conflicts of interest arise. For organizations in the same competitive landscape, this can raise doubts about alignment, priorities, and incentives, making continued reliance on that provider increasingly difficult to justify.

One thing’s for certain: your data partner has never mattered more. We’re entering a period of market shake-up, where diversification of suppliers and specialization in services will become increasingly valuable to AI builders.

Beyond the boardroom maneuvers, something much more fundamental is happening in AI development. We’ve entered the era of experience. It’s not enough for models to be technically sophisticated or capable of passing abstract benchmark tests. What matters now is how models perform in the real world, across diverse user groups and tasks. Are they trustworthy? Are they usable? Do they meet people’s expectations?

This shift is being driven by an awakening among model developers: in a competitive landscape, it’s not just about who can build the most advanced model, but whose model people choose to use. The new frontier isn’t measured solely in benchmark scores or inference speed—it’s measured in experience quality.

That means the success of an AI model is increasingly dependent on human input throughout its lifecycle. We’re seeing a surge in demand for real-time, continuous human evaluations across multiple demographics and use cases.

Evaluating models in the lab is no longer enough. The real world, with all its complexity and nuance, is now the benchmark.

Some may argue that synthetic data will eventually replace the need for human annotators. While synthetic data has a role to play, particularly in cost-efficient scalability or simulating rare edge cases, it falls short in one critical area: representing human experience. Human values, cultural nuances, and unpredictable behavior patterns cannot be easily simulated.

As we grapple with AI safety, bias, and alignment, we need human perspectives to guide us. Human intelligence, in all its diversity, is the only way to meaningfully test whether AI systems behave appropriately in real-world contexts.

That’s why the demand for real-world, high-fidelity human data is accelerating. It’s not a nice-to-have. It’s essential infrastructure for the next wave of AI.

If human feedback is the engine powering better AI, then the workforce behind that feedback is its beating heart. The industry must recognize the people providing this essential input as co-creators of AI.

This begins with diversity. If AI is going to serve the world, it must be evaluated by people who reflect the world—the best and the breadth of humanity. That means including people from different cultures, socioeconomic backgrounds, and educational levels. It also means ensuring geographic diversity so models don’t just perform well in Silicon Valley but also in Nairobi, Jakarta, or Birmingham.

Equally important is expertise. As AI becomes more specialized, so too must its human evaluators. Educational AI systems should be evaluated by experienced teachers. Financial tools require scrutiny by economists or accountants. Subject matter experts bring context and domain-specific insight that generic crowd work can’t replicate.

But building this kind of human intelligence layer doesn’t just happen. It requires thoughtful infrastructure, ethical foundations, and a commitment to the people behind the data.

That means fair pay, transparency, and a smooth user experience that gives people easy access to interesting and engaging tasks. When contributors feel respected and empowered, the quality of insight they provide is deeper, richer, and ultimately more valuable. Treating evaluators well leads to better data—and better AI.

Meta’s investment in Scale may appear like another play in a long series of tech consolidations, but it’s something more: a signal that the era of human data as critical infrastructure for AI has truly begun.

For model developers, this is a call to action. Relying on one provider—or one type of data—no longer cuts it. Specialization and trust in your human data partners will define the winners in this next phase of AI development.

For the broader industry, this moment is an invitation to rethink how we build and evaluate AI. The technical challenges are no longer the only obstacle. Now we must consider the social contract: How do people experience AI? Do they feel heard, understood, and respected by the systems we build?

And for many, this moment validates the belief that human intelligence is not a constraint on AI progress, but one of its greatest enablers.

The Meta/Scale deal will likely catalyze further consolidation in the human data space. But it also opens the door for more specialized and transparent providers to shine. We anticipate a surge in demand for high-integrity, experience-focused data partners—those who can provide rich, real-world feedback loops without compromising trust.

Ultimately, this isn’t just about who builds the most powerful model. It’s about who builds the most useful, trusted, and human-centric model. The future of AI is intuitive, inclusive, and deeply human. And that future is already taking shape.

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This article was produced as part of TechRadarPro's Expert Insights channel where we feature the best and brightest minds in the technology industry today. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro

• Microsoft is prepping Windows XP Crocs, dubbed the '50th Anniversary Exclusive'
• It'll be the first version of Windows to take this step, if true
• The Crocs come with six Jibbitz and a carrying tote

Yes, Microsoft is still celebrating its 50th anniversary, and while the company has done a lot of looking back, it’s also looking forward. Err, at least taking a step forward.

Sure, we’ve seen some iconic Windows ugly sweaters, including one with Minesweeper and one with Clippy, but Windows XP is going where no other version of Windows has ever gone before – to Crocs.

TechRadar's confirmed with the tech giant that the Microsoft 50th Exclusive Crocs – aka the Windows XP Crocs – are official, and got five images of the shoes.

According to a report from The Verge, Windows XP Crocs are currently available for internal order by Microsoft employees – priced at $80 – with the story noting that the employees “get first dibs” ahead of a “worldwide launch.”

We’ve seen other collaborations from the Croc brand, with plenty of Disney properties included – I mean, kachow, Lightning McQueen Crocs that light up – along with fashion houses, and even McDonald's. The Windows XP Crocs, though, take the iconic green hills and blue skies wallpaper to the shoe form.

And I know what you’re thinking, but the images of the Windows XP Crocs do indeed confirm the existence of a Clippy Jibbitz (aka what Crocs calls their shoe charms). The Windows XP Crocs will come with an iconic helper as well as a pointer, the MSN butterfly, a classic Internet Explorer logo, the recycling bin, and a folder. That comes to a whopping six Jibbitz in total.

You also get a drawstring tote that's inspired by the classic, now iconic, Windows XP wallpaper. Microsoft did confirm the existence of the Crocs to us and shared these images, but didn't share anything more on pricing or availability.

At a reported price tag of $80, the Windows XP Crocs aren’t cheap, but if you’re a Microsoft collector or someone who’s also opted to get the previous ugly holiday sweaters, they might be the perfect shoe to add to your collection. Of course, I think many would be happy if Microsoft goes the route of other retro, nostalgia-fueled drops – it could be a fresh skin for Windows or even another wallpaper drop, and that would still be a great way to honor the 50th.

You might recall that Microsoft dropped a limited 50th anniversary edition of the Surface Laptop, which looked pretty snazzy. It’s also a more subtle way to celebrate 50 years of Microsoft than, say, blue and green Crocs.

Stick with TechRadar as once we learn more about pricing and how to get a pair of the Microsoft 50th Exclusive Crocs, we'll be sure to update this post.

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