Leading the way with Agentic AI

Kevin Cochrane at Vultr explains how the UK can lead in the Agentic AI revolution

 

The UK stands at the brink of a transformative era in Artificial Intelligence, with Agentic AI set to redefine business operations. Gartner predicts that by 2028, Agentic AI will be embedded in one-third of enterprise software applications, automating 15% of daily work-related decisions. For UK businesses, this presents an opportunity to drive efficiency, enhance decision-making and secure a leadership role in the global AI economy.

 

However, to fully realise the benefits of this new AI paradigm, companies must rethink their approach to cloud computing and data infrastructure.

 

A key enabler of this transformation will be the increased adoption of independent cloud computing providers. With data centre infrastructure demands surging and government initiatives shaping AI development, UK business leaders must make strategic decisions to ensure they can harness Agentic AI while remaining competitive, compliant and cost-efficient.

 

Low-latency, real-time Edge processing

Agentic AI systems autonomously plan and execute tasks to achieve predefined business objectives, requiring real-time data processing with minimal latency. Traditional centralised cloud models introduce network lag, which can hinder performance, particularly in applications that demand instantaneous responses.

 

By using edge computing, which processes data closer to its source, businesses can enable real-time responsiveness, a critical requirement for Agentic AI. This approach also reduces bandwidth costs and enhances security by limiting the transmission of sensitive data to central servers. The shift towards edge computing will be crucial in enabling AI-driven automation across industries such as finance, healthcare and manufacturing.

 

Scaling AI with silicon diversity

As AI workloads grow in complexity, reliance on a single type of processor can lead to performance bottlenecks and increased costs. A more efficient approach is embracing silicon diversity, utilising a mix of CPUs, GPUs and AI-specific accelerators to optimise performance for different AI tasks.

 

As AI workloads become more varied, businesses must ensure they have access to the right mix of computing resources without overcommitting to expensive, monolithic infrastructure solutions. Only a select few enterprises with substantial resources can afford to procure the extensive GPU and CPU capabilities required to scale GenAI and Agentic AI.

 

The only approach that makes sense is serverless inference, using cloud-provider-managed resources that offer optimal compute as a serverless function. Enterprises don’t need to concern themselves with understanding or maintaining ideal compute configurations, incurring the capital expense involved in procuring infrastructure or managing the rapidly evolving innovation that quickly renders even the newest infrastructure components obsolete. Independent cloud providers often support a broader range of hardware architectures than larger hyperscalers, giving businesses greater flexibility to optimise their AI infrastructure.

 

Data sovereignty in AI

Agentic AI relies on vast amounts of proprietary and sensitive data, making data sovereignty a crucial concern. With strict regulations like the UK’s Data Protection Act and emerging AI governance frameworks, businesses must ensure their AI systems comply with local data laws.

 

For many UK businesses, hyperscale cloud providers have been the default choice. However, independent cloud providers offer UK-based data residency solutions, ensuring that sensitive business information remains within national jurisdiction. This mitigates the legal and security risks of cross-border data transfers while giving businesses greater control over their AI operations.

 

Independent providers also help businesses avoid vendor lock-in, giving them the flexibility to adapt as AI requirements evolve. By fostering a more competitive cloud landscape, independent providers are crucial in supporting the UK’s ambitions to become a global AI leader.

 

What’s next in the infrastructure boom?

The UK Government has recognised the strategic importance of AI and the infrastructure needed to support it. Initiatives such as the AI Opportunities Action Plan aim to accelerate AI adoption by streamlining planning processes and enhancing access to power grids for data centres.

 

As demand for AI-ready infrastructure grows, UK businesses must align their technology strategies with these developments. This includes choosing cloud partners that prioritise scalability, performance and compliance while ensuring data remains under UK jurisdiction.

 

The rise of Agentic AI presents UK businesses with an unprecedented opportunity to drive innovation and efficiency. However, to fully capitalise on this AI revolution, companies must invest in infrastructure that supports real-time edge processing, embraces silicon diversity and harnesses serverless computing to scale efficiently.

 

Partnering with independent cloud providers will be a strategic move, offering UK businesses the flexibility, compliance and cost-efficiency needed to compete in an AI-driven world. By making strategic decisions today, UK businesses can establish themselves as leaders in the global AI economy.

 

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Kevin Cochrane is Chief Marketing Officer at Vultr

 

Main image courtesy of iStockPhoto.com and mathisworks