Autonomous agents are no longer confined to research labs. From contract analysis in law firms to predictive maintenance in factories, they are starting to influence high-stakes enterprise workflows. But autonomy brings risk: what happens when an agent makes an unauthorised decision, or when its reasoning cannot be explained?
This is why governance is emerging as the true differentiator in enterprise adoption. The EU AI Act, GDPR, UK FCA rules, and NHS data standards all signal a future where enterprises must demonstrate accountability, auditability, and explainability. For agentic AI to succeed, enterprises must move beyond abstract “AI ethics” discussions and embed concrete compliance frameworks into system design.
In this article, we’ll examine the risk landscape of autonomous agents, explore compliance challenges across finance, legal, healthcare, and construction, and outline the practical frameworks and guardrails enterprises can use to balance autonomy with oversight.
The Risk Landscape of Autonomous Agents
Self-executing AI introduces fundamentally variant risk categories that traditional automation frameworks were never designed to handle, which includes goal misalignment, strategic deception, and autonomous decision-making without human oversight:
- Unauthorised Operational actions: an agent updating financial records in an Enterprise Resource Planning (ERP) system without proper validation using only prompt injection attacks, potentially resulting in fraudulent financial transfers of hundreds of thousands of dollars or scheduling safety-critical tasks without proper authorization chains or Human-In-Loop verification.
- Opaque decision-making processes: black-box neural network outputs that lack explainability mechanisms required by regulations like GDPR ‘right to explanation’ provisions, making it difficult for regulators or auditors to trace trace decision logic, validate reasoning, or ensure algorithmic fairness.
- Systematic Bias amplification: when underlying data problems includiing historical biases, sampling biases, or data quality issues are magnified by autonomous systems, leading to discriminatory outcomes that can affect employment decisions, financial services, and criminal justice applications. Unlike static systems, autonomous agents can develop biases through their adaptive learning processes, making bias detection and mitigation significantly more challenging.
The compliance gap is clear: most enterprises have governance models built for static automation (RPA, scripts), not for adaptive, decision-making agents. Closing this gap requires rethinking governance from first principles. Most enterprises have governance frameworks which are designed for Level 1-2 automation (rule-based RPA and scripted workflows) and usually operate on static logic and predefined decision trees. These frameworks fundamentally cannot address Level 3-4 autonomous agents which exhibit goal-driven behavior, iterative planning, adaptive learning, and emergent decision-making capabilities.
Industry Lens – Finance & Legal
Financial services are tightly regulated by the UK’s Financial Conduct Authority (FCA),which requires explainability for algorithmic decisions. Under the Consumer Duty framework effective since July 2023, firms must evidence the fairness of their algorithms and demonstrate good customer outcomes. While not mandating specific explainability requirements, the FCA expects firms to meet existing transparency obligations under MiFID II RTS 6 for algorithmic trading .For autonomous agents handling trading workflows, risk assessments, Anti-Money Laundering (AML) transaction monitoring, or automated credit decisions or compliance reporting, the stakes are high: an unauthorised trade or miscalculated credit score could trigger regulatory penalties. Regulatory fines for AML/KYC breaches exceeded $5 billion globally in 2024 alone. An unauthorized trade or algorithmic malfunction could trigger MiFID II RTS 6violations, resulting in enforcement action under MAR 7A market abuse provisions. The FCA's recent multi-firm review of algorithmic trading controls(August 2025) specifically highlighted governance weaknesses that expose firms to significant regulatory penalties.
- Guardrails needed: real-time monitoring, audit logs of all transactions, and human-in-the-loop oversight for high-value or critical actions. Mandatory guardrails under MiFID II RTS 6include: real-time pre-trade and post-trade risk controls with defined ownership structures, comprehensive audit trails meeting Article 9 annual self-assessment requirements, effective kill switches for immediate order withdrawal under Article 12, and human-in-the-loop oversight with Senior Manager accountability under SM&CR frameworks. Additional requirements include conformance testing with trading venues (Article 6), stress testing under extreme market conditions (Article 10), and robust governance arrangements with board-level oversight.
Law firms must preserve client confidentiality and legal privilege. If an agent processing contracts inadvertently discloses sensitive terms, the result could be catastrophic, which include not only disclosure of sensitive contract terms but potential GDPR violations with fines up to £17.5 million or 4% of global annual turnover.
- Guardrails needed: role-based access control, comprehensive logging of document handling, protection to prevent unauthorized disclosure, comprehensive audit logs tracking all document interactions with immutable timestamps and user attribution, explainability modules using techniques like attention mechanisms to clarify clause flagging rationale while maintaining privilege, and mandatory client informed consent protocols under Model Rule 1.6 for any AI tool deployment.
Industry Lens – Healthcare & Public Services
Healthcare: The NHS Data Security and Protection Toolkit(DSPT)2025-26 requires annual mandatory self-assessment against the National Data Guardian's 10 data security standards for all organizations processing NHS patient data. Under GDPR Article 9 and UK Data Protection Act 2018 Schedule 1, health data constitutes 'special category' personal data requiring explicit consent or legitimate healthcare exemptions. Autonomous agents handling clinical scheduling, AI-powered triage systems, or automated record extraction must comply with both DSPT mandatory requirements and GDPR's heightened protection standards, where violations can result in ICO fines up to £17.5 million or 4% of global annual turnover. Agents handling scheduling, triage, or record extraction must ensure that PII is never mishandled.
- Guardrails needed: PII detection, consent validation, geographic residency controls, and end-to-end encryption within workflows. Automated PII detection using named entity recognition (NER) for health records, with de-identification meeting NHS Digital standards, dynamic consent validation systems compliant with GDPR Article 7 withdrawal requirements, geographic residency controls preventing cross-border data transfers without adequate safeguards under UK GDPR Chapter V, end-to-end encryption meeting NHS Technical Security Policy requirements, and human-in-the-loop mechanisms for high-risk clinical decisions to prevent algorithmic bias in triage outcomes. Additional controls include real-time monitoring for false positives in AI triage systems (documented cases show unnecessary resource allocation due to overestimation of mortality risks),automated audit trails for all patient interactions, and explainable AI modules to support clinical decision-making transparency.
Public Services & Construction: Construction and public infrastructure projects involve safety-critical processes governed by the Health and Safety at Work Act 1974. Regulators such as the UK’s Health and Safety Executive (HSE) demand verifiable records for incidents and inspections. If autonomous site-monitoring agents fail to escalate hazards, liability risks multiply. HSE's 2025 regulatory approach specifically addresses four key AI application areas: maintenance systems with predictive analytics, health and safety management including automated risk assessments, autonomous control of equipment and processes, and occupational monitoring through computer vision. HSE expects comprehensive risk assessments for AI deployments impacting workplace safety, with appropriate controls to reduce risks to as low as reasonably practicable (ALARP).
- Guardrails needed: escalation protocols, immutable audit logs, and lineage traceability of safety decisions. Mandatory construction AI guardrails under HSE majorly include: automated escalation protocols with defined thresholds for immediate human intervention in safety-critical scenarios, immutable audit logs meeting HSE incident reporting requirements under RIDDOR (Reporting of Injuries, Diseases and Dangerous Occurrences Regulations), complete lineage traceability of safety decisions including data provenance, algorithmic decision paths, and human override capabilities, and explainable AI systems that can provide clear rationale for safety recommendations to satisfy HSE's transparency requirements. In addition to this it includes continuous monitoring of AI systems against safety parameters , regular third-party audits of autonomous systems, integration with existing HSE Management of Health and Safety at Work Regulations (MHSWR), and fail-safe mechanisms
Building Practical Compliance Frameworks
Embedding governance into agentic systems requires structured frameworks. The below framework suggests best practices and actionable steps to ensure safe, ethical, and compliant deployments:
- Policy-First Design: map agent workflows to relevant regulations before deployment. For example, encoding GDPR “right to explanation” obligations directly into contract-review agents.
- Workflow-to-Regulation Traceability
- Document every decision point, data input, and output in your agent’s process flow.
- For each step, link to specific legal or regulatory obligations (e.g., GDPR Article 22 “right to explanation, ”MiFID II RTS 6 pre-trade controls, NHS DSPT data security standards).
- Regulatory Contract Encoding
- Encode obligations directly into your agent’s logic: contract–review agents should automatically flag any clause triggering GDPR "automated decision" provisions and generate human-review tasks with explainability metadata.
- Validate such rulesets against a centralized policy repository to ensure consistency and up-to-date coverage
- Embedded Controls: enforce compliance through built-in safeguards such as data minimisation, consent verification, audit logs, and access restrictions.
- Data Minimisation
- Implement pre-processing filters so that it can automatically remove or anonymize unnecessary personal data fields before any downstream processing.
- Enforce schema-based constraints To restrict agents from requesting or storing prohibited data.
- Consent Verification
- Build real-time checks against consent records: The operations for categories like healthcare , finance must query a consent management service and any actions lacking valid authorization should be aborted or queued
- Every consent check must be associated with proper user ID, timestamp, and decision outcome for audit future purposes.
- Immutable Audit Trails
- Record every agent action - including data inputs, policy checks, decision rationales, and outputs - in append-only, tamper-evident logs.
- Use blockchain or write-once storage when regulatory standards demand non-repudiation (MiFID II, RIDDOR).
- Fine-Grained Access Restrictions
- Employ role-based and attribute-based access control (RBAC/ABAC) to enforce the principle of least privilege.
Agents should obtain time-limited tokens scoped strictly to the actions and data necessary for each workflow step Cross-Functional Governance Teams: involve compliance, legal, IT, and operations from the start. Governance cannot be owned by IT alone - it requires joint accountability. This shifts governance from a reactive afterthought to a proactive enabler of safe adoption.
Balancing Autonomy and Oversight
Enterprises must strike a balance between agent autonomy and human oversight:
- Controlled Autonomy: agents may act independently for routine, low-risk tasks (like data entry, basic scheduling) but must defer to humans for exceptions and high-risk decisions (like policy exceptions, edge cases and task surpassing).
- Escalation Models: build workflows where agents automatically route ambiguous or policy-sensitive cases e.g., large financial transfers, potential privilege conflicts, clinical red flags and automatically escalate these to respective to supervisors .
- Future-Proofing: sector-specific frameworks under the EU AI Act and emerging UK regulations will demand higher accountability standards. Building in escalation and explainability today will make compliance tomorrow far easier.
This balanced governance model ensures agents accelerate workflows without eroding trust and maintain regulatory compliance , and clear human accountability.
Compliance-First Adoption
Autonomous agents can deliver major efficiencies in finance, legal, healthcare, and construction - but only if they operate within compliance-first architectures. Auditability, explainability, and human oversight must be designed in, not retrofitted later.
Key takeaway: Governance is not a blocker to adoption - it is the enabler of trust and scale. Enterprises that embed compliance into their agentic AI frameworks today will be the ones best positioned to leverage autonomy tomorrow.
Want to explore how governance frameworks can make your AI adoption safe and scalable? Merit Data and Technology helps enterprises design AI systems with compliance built into the architecture.
