As the hype and number of use cases grow, businesses across the UK are looking to how AI agents can help them gain competitive advantage. They are no longer experiments — agents have the capacity to act as virtual employees, managing confidential data, operating independently, and autonomously connecting with customers. From increased productivity and quicker insights to new digital services, their potential is huge.
However businesses without the right governance in place before putting agents into production are taking a huge risk. With the EU AI Act, upcoming UK legislation, and sector-specific rules tightening the net, governance is no longer optional — it’s the foundation of trust.
At the same time, the opportunity is massive. Since July 2024, the UK AI industry has been attracting an average of £200 million in investment every day. Organisations that can balance innovation with governance will be the ones that turn AI agents from hype into competitive advantage.
Navigating risks without clear guardrails
Regulatory scrutiny is rapidly increasing. AI agent deployments must adhere to stricter safety, transparency, and accountability standards from the offset due to the EU AI Act, upcoming UK legislation, and sector-specific regulations.
Yet too many organisations are still operating without a clear roadmap. Measuring the quality of agent behaviour is often ad hoc, based on gut feel rather than consistent benchmarks, which undermines trust and makes it hard to prove value.
Data is another stumbling block. AI agents depend on proprietary, well-governed datasets, yet many organisations lack the volume, accessibility or quality to train them effectively. Add to this the relentless pace of change of AI models and tools themselves, and it’s no wonder that some projects are stalling before they can deliver meaningful results.
Governance as the backbone of trust
With the right governance, all actions and outputs of agents can be traced back via the data lineage, from the raw data used for training to the logic performed in real time. Strong access restrictions and security measures are applied via a unified governance model, which handles agents with the same discipline as human employees.
It also creates a single, consistent view across data and AI assets, removing siloes and enabling safe discovery and re-use. Governing the business semantics that underpin decisions is equally critical, so both people and agents work from the same definitions of metrics and KPIs. Finally, monitoring agents after deployment is essential to detect drift, bias or harmful behaviour before they cause any real damage.
In the era of AI agents, fragmented governance models simply won’t scale. These systems act autonomously to complete tasks, taking actions that can affect customers, finances and brand reputation. They must be governed with the same principles that apply to people: security, transparency, accountability, quality and compliance. And as the technology stack evolves, governance needs to be both unified across all data and AI assets and open to any tool or platform. Otherwise, innovation will be slowed by integration barriers.
Scaling safely from pilots to production
When properly implemented, lineage and governance allow for rapid development of agents, without causing problems — transforming promising experiments into systems fit for production. This process is helping the most cutting-edge companies reduce the time between idea and implementation. They are able to adjust performance to strike the ideal balance between cost and quality by automating the assessment and optimisation of their agents, creating synthetic data that fills gaps in proprietary sources, and developing domain-specific benchmarks.
Automated evaluation is especially important. Businesses that lack it are often forced to rely on “gut checks” to determine whether an agent is performing well, which leads to inconsistent quality and costly trial-and-error. By contrast, those that generate task-specific evaluation, use synthetic data to enhance training and optimise across the latest models and techniques, can scale agents with confidence, knowing they meet quality thresholds while controlling costs.
Flo Health, the world’s leading health app for women, offers a clear example. With an AI agent system, it doubled medical accuracy over standard commercial large language models, while meeting stringent internal standards for safety, privacy and clinical validity. This turned an experimental tool into a trusted production system in a highly regulated sector.
Building a competitive edge with agents
Businesses in the UK have a limited time frame to take the lead in AI agents before competitors from across the world overtake them. This leadership will come from deploying the correct agents, those that are safe, transparent, and based on controlled, high-quality data, as opposed to deploying the most agents, the quickest.
To achieve this, businesses must make sure that every system is based on a consistent business context, integrate assessment and optimisation into the agent lifecycle, and most importantly, view governance as a fundamental component of their data and AI strategy.
Unregulated innovation is a risk that no company should undertake. Building AI agents that start with governance and lineage, to inspire trust and market confidence, may help UK organisations go beyond hype and achieve quantifiable results.
Dael Williamson
Dael Williamson is EMEA CTO at Databricks. He advises UK and EU start-ups and brings decades of experience in technology strategy, enterprise architecture, and AI-driven business transformation.
