From experiment to impact: why AI agents need governance from day one (www.techradar.com)

Databricks’ EMEA CTO argues that AI agents—autonomous systems acting like virtual employees—need formal governance from day one as UK businesses rush to commercialize them. With the UK AI sector drawing heavy investment and new rules such as the EU AI Act and pending UK legislation increasing regulatory scrutiny, rushing unvetted agents into production risks reputational, financial and compliance harm. Many organizations still evaluate agent behavior ad hoc, lack governed proprietary data, and struggle with rapid model/tool churn, stalling projects before they deliver value. The piece stresses governance and data lineage as accelerators, not hurdles: every agent action and output should be traceable from raw training data through real-time logic, backed by robust access controls, security, and unified business semantics (shared metrics/KPIs). Continuous monitoring for drift, bias and harmful behavior, plus interoperability so governance spans all platforms and tools, are essential. Practically, teams should adopt automated evaluation, task-specific benchmarks, synthetic data to fill training gaps, and optimization across models to balance cost and quality. Done right, these measures turn experiments into production-grade, explainable, and scalable agents—giving UK organizations a narrow window to lead by deploying the right, governed agents rather than the most of them.