AI agents are rapidly entering the UK workforce. New research shows that globally, that 9 out of 10 software leaders are developing their own agentic AI solutions or have plans to do so. In Europe, the UK leads, with 47 percent of companies actively integrating the technology, ahead of the regional average of 40 percent.

Unlike many past innovations that stalled after initial hype, agentic AI is transitioning quickly from proof-of-concept to large-scale deployment. For organisations still evaluating or experimenting, the time is to act now. But rushed, ad hoc initiatives won’t deliver results. Unlocking agentic AI’s full potential requires a systematic approach built on modular data and AI platform.

Ensure data readiness and orchestration

The foundation begins with data readiness. High-quality, multimodal data that is consistent, accurate, fair, complete and governed is at the heart of agentic AI performance. Organisations must ensure flexible and secure orchestration frameworks that enable real-time data flow and transformation, along with strong governance for ownership, data privacy, data classification, standardised definitions, and enhanced lineage. Real-time data girds and Model Context Protocol (MCP)-enabled services are critical to keep agents continuously updated.

Build cross-disciplinary knowledge to prioritise use cases and define KPIs to measure value delivery. A cross-functional team, comprising business executives, AI and data specialists, and finance and compliance experts should align AI initiatives with organisational goals, identify high-value use cases, and define KPIs to measure impact.

Operations, IT and finance contribute their unique experience and perspective to assess the technical and commercial feasibility of shortlisted ideas and establish standardised metrics to compare and track performance across projects; this even helps to showcase agentic AI outcomes to interested stakeholders. This team also sets governance protocols to ensure agents operate within regulatory and ethical boundaries.

AI agents to go from automation to autonomy

Composable architecture is key to scaling agentic AI. Breaking business capabilities into micro functions, API-driven components or microservices that allow seamless integration with existing workflows and quick adaption to change. Standard interfaces and APIs ease agentic AI integration with current workflows to allow real-time data exchange.

Organisations can separately scale each micro functions as and when required to quickly adapt to changes in the business and technology landscape. This approach supports low-risk innovation by allowing developers to test new agents within isolated components. Above all, composability unlocks the full value of agentic AI by enabling multiple, specialised AI agents to collaboratively resolve complex, cross-functional problems.

When it comes to building and operationalising AI agents, organisations in the UK. would do well to choose a robust open-source platform that offers modular templates, tool integration, and scalable deployment. Other capabilities to look for include pro-code agent creation, management of agent and tool lifecycles, reusable components, advanced agent templates, and responsible AI features such as human-in-the-loop mechanisms and enterprise-grade observability.

Trust and transparency through the agentic lifecycle

Concerns about AI’s accuracy and fairness deter adoption at scale: in a recent survey, 72 percent of AI practitioners said that the lack of rigorous evaluation mechanisms hindered trustworthy deployment.

It is therefore, imperative to assuage these concerns right from the start by embedding transparency and trust at every step, from data ingestion to action execution. Responsible AI principles must be embedded from the start, ensuring transparency, accountability, and explainability and compliance with regulations such as the EU AI Act and GDPR. Safety and reliability should be proactively managed throughout the agentic lifecycle.

Agentic AI moves mainstream

Agentic AI is no longer experimental. It is moving from pilot projects to full- scale integration, capable of making autonomous decisions and learning from experience. The time for testing is over. U.K. organisations must take a structured, step-by-step approach to harness the transformative potential of agentic AI.