It’s Monday morning. The compliance team arrives at the office, bleary-eyed, only to discover their AI “colleague” has already spent the weekend scanning new EU and UK regulations, flagged several suspicious transactions, drafted a fresh policy update, and even booked a meeting to walk the team through its findings. That may sound like science fiction,  but thanks to agentic AI, it’s fast becoming business reality.

Unlike traditional AI tools that simply analyse data or answer prompts, agentic AI refers to systems that take the initiative. They can plan, execute and sequence tasks toward a goal, for instance, monitoring transactions, launching investigations, generating reports or coordinating across multiple systems. In other words, these are autonomous, goal-directed agents that go beyond passive insight to active compliance orchestration.

The moment is now. With the EU Artificial Intelligence Act in force since August 2024, enterprise-grade AI systems are being drawn into regulatory and governance frameworks, while business adoption of autonomous agents is accelerating rapidly. In a world of mounting regulatory scrutiny across the UK and EU, agentic AI promises not merely efficiency gains, but a fundamental rewrite of how organisations manage risk and compliance.

The Shift from Reactive to Proactive

For decades, risk and compliance teams have worked in “rear-view mirror mode”, piecing together incidents only after they had unfolded. Today, agentic AI is shifting this approach towards a genuinely proactive model, acting as the new nerve centre of enterprise risk management.

Agentic systems now scan regulatory change in real time, from Financial Conduct Authority (FCA) notices to European Banking Authority (EBA) guidelines and European Commission updates. This ensures that emerging obligations are captured before they disrupt operations. Examples include platforms that track and summarise regulatory updates continuously, such as RegRadar.

Crucially, these AI agents autonomously triage risks such as cyber anomalies, fraud patterns or supplier ESG breaches before human analysts even log in. They pull signals from customer relationship management (CRM) records, transaction-monitoring engines, HR systems and whistleblowing channels, orchestrating multi-step workflows that once required several teams. A practical example is AI-driven suspicious-activity screening, increasingly used in financial services, such as the workflow automation described by Thomson Reuters.

Agentic AI also enables dynamic risk heatmaps and rapid micro-simulations of potential compliance exposures under proposed regulatory changes. As these capabilities mature, AI “co-pilots” are evolving into true “AI teammates”, executing complex, cross-system tasks and giving human specialists space to focus on governance, judgement and strategic risk mitigation.

The Audit Trail Fights Back

For years, concerns about “black box” models, opaque reasoning and accountability gaps have fuelled scepticism about AI in compliance. Yet a counter-trend is emerging: agentic AI that documents its own decision paths, data sources and evidence in real time, creating an audit trail more reliable than human note-taking. In the EU and UK, where regulators increasingly demand demonstrable governance, this machine-generated transparency is becoming a genuine competitive advantage. The UK Corporate Governance Code’s renewed emphasis on internal controls and the EU AI Act’s focus on traceability have only intensified the shift.

In practice, organisations are already using AI agents to justify sanctions-screening outcomes or to log every step of a model-driven risk rating. Some firms are even experimenting with “AI as the auditor’s auditor”, cross-checking inconsistencies in risk assessments, policies and reporting. Entertainingly, and slightly alarmingly, for the first time the compliance function may be supported by a system that leaves fewer gaps in documentation than the humans who rely on it.

The Human–AI Partnership

A persistent misconception in boardrooms is that agentic AI will soon replace entire compliance teams. In reality, EU and UK regulatory frameworks explicitly require human oversight, ownership of risks and clear accountability lines, as reinforced by the UK Corporate Governance Code and the EU AI Act’s human-in-the-loop provisions. Far from eliminating people, agentic AI elevates their role.

Humans remain indispensable for sense-checking outputs in ethically ambiguous situations, applying proportionality, and taking decisions that carry legal liability or stakeholder impact. Consider a practical example: an AI agent detects a supplier ESG breach, drafts a revised risk rating, and automatically schedules a remediation call. The system has done the legwork, yet only a human can judge whether to escalate the issue politically, terminate the relationship entirely, or adjust the response based on contextual intelligence the AI does not possess.

A fresh trend is now emerging: “AI orchestration roles”, where compliance professionals manage fleets of autonomous agents rather than performing every task themselves. In this model, AI handles the autopilot functions, but humans still hold the controls where it matters most.

Invisible Guardians – AI for Early Detection & Behavioural Risk

A new frontier in compliance is emerging, where agentic AI acts as an “invisible guardian”, detecting, predicting and preventing organisational misconduct long before it escalates. Behavioural-analytics systems are already capable of spotting micro-patterns such as abrupt shifts in communication tone, unusual access behaviour or subtle policy deviations. Platforms using behavioural indicators to detect insider risk, such as those described by Proofpoint, show how early signals can be surfaced with remarkable precision.

Agentic AI is also powering predictive culture monitoring, analysing sentiment, whistleblowing metadata and process anomalies to identify emerging cultural hotspots. Real-time monitoring is increasingly expected by EU/UK regulators, particularly in areas such as sanctions, bribery and environmental, social and governance (ESG) exposure. The European Commission’s guidance on responsible business conduct underscores this widening expectation for proactive detection.

Looking ahead, organisations may deploy “digital integrity officers”, AI agents roaming corporate systems much like cybersecurity tools patrol networks, continuously scanning for behavioural and operational integrity risks. These systems do not replace governance frameworks, but rather they illuminate blind spots that humans rarely see. As a result, compliance functions gain something they have never had before, continuous, predictive oversight that feels more science fiction than spreadsheet.

The New Operating Model

By 2030, agentic AI will not have eliminated risk, but it will have fundamentally changed the rhythm of how risk and compliance teams operate. The shift is already visible, as manual tasks are becoming automated, reactive workflows are turning anticipatory, traditional documentation is giving way to self-documenting systems, and human-centred processes are evolving into human-guided AI ecosystems. Regulators are signalling the direction clearly. The EU AI Act’s governance requirements, the FCA’s expanding expectations for operational resilience and the EBA’s guidelines on internal governance all reward firms that build mature, transparent AI-enabled controls early.

Practical examples are emerging across the sector. Financial institutions using AI agents to pre-assemble regulatory submissions, insurers running continuous model-risk checks, and corporates leveraging autonomous systems to map their entire supply-chain exposure in hours rather than months. These are no longer experiments, but early drafts of the 2026–2030 operating model.

The organisations that will thrive in this new era are those that do not ask how to control agentic AI, but how to collaborate with it.

And what about you…?   

  • Which tasks in your team could realistically be orchestrated or partially automated by AI agents—and which absolutely require human judgement or accountability?
  • What worries you most about deploying agentic AI, loss of control, regulatory scrutiny, transparency, data quality, and what safeguards or operating models would help address those concerns?