AI Agents and the Three Lines of Defence: A Banking Inspired Approach
It recently struck me, whilst reviewing a multi-agent AI system design, that we're unconsciously recreating the same control structures that have served the banking industry so well. The parallels between AI agent architectures and the Three Lines of Defence (3LoD) model aren't just coincidental – they might be showing us the path to responsible AI deployment in regulated industries.
For those unfamiliar with the 3LoD model, it's a fundamental risk management framework that has become the gold standard in banking and financial services. Imagine it as a series of checkpoints, each providing a different layer of protection. The first line consists of the operational teams who own and manage risks directly in their day-to-day work. The second line provides oversight through risk management and compliance functions, setting the rules and monitoring adherence. The third line, typically internal audit, offers independent assurance that everything is working as intended. This model has proven remarkably effective at managing risk while enabling innovation – exactly what we need for AI agent systems.
The Banking Blueprint
The traditional 3LoD model in banking consists of:
First Line: Business operations and risk ownership
Second Line: Risk control and compliance functions
Third Line: Internal audit and independent assurance
Modern AI agent architectures, particularly multi-agent systems, naturally align with this proven framework, offering similar benefits in terms of control, oversight, and risk management.
The Multi-Agent Model: A Flexible Framework
In advanced multi-agent systems, we typically see six core personas that mirror and enhance the 3LoD approach:
First Line (Operational Execution)
Orchestrator: Acts as the front-line coordinator, managing workflow and directing tasks
Planner: Develops strategic approaches and execution plans
Creator: Generates initial content or solutions
Second Line (Control and Review)
Author: Refines and standardises output
Reviewer: Performs quality control and compliance checks
Third Line (Independent Oversight)
Approver: Provides final independent verification and validation
Flexible Implementation Based on Risk and Context
While these six core personas provide a useful framework, it's crucial to understand that this isn't a rigid, one-size-fits-all model. The actual implementation should be carefully tailored based on several key factors:
Risk and Impact Assessment The number and type of agents needed should directly correlate with the potential risk and impact of the system's decisions. For instance, an AI system making movie recommendations might need fewer control layers than one making lending decisions or medical diagnoses.
Data Classification The sensitivity and classification of data being processed should inform the agent architecture. Systems handling public data might operate with streamlined oversight, while those processing personal, financial, or classified information would warrant more comprehensive agent controls.
Use Case Complexity The complexity of the use case should drive the granularity of agent separation. Simple, straightforward processes might function effectively with fewer agents, while complex workflows involving multiple stakeholders or regulatory requirements might need additional specialist agents.
Specialised Control Agents for High-Risk Systems
In scenarios where the potential for harm is significant, organisations might implement additional specialised control agents:
Multiple Independent Reviewers
These agents operate in parallel rather than sequence, each applying different validation criteria to the same output. For example:
One reviewer might check for technical accuracy
Another might validate regulatory compliance
A third might assess for bias or fairness
The system requires consensus from all reviewers before proceeding, similar to how critical financial transactions require multiple approvers with different areas of expertise.
Specialist Ethics Agents
Ethics agents serve as dedicated moral compasses within the system, specifically programmed to:
Evaluate outputs against predefined ethical frameworks
Check for potential discriminatory impacts
Assess fairness across different demographic groups
Flag potential unintended consequences
Ensure alignment with organisational values and principles
Real-time Monitoring Agents
These agents act as continuous system observers, providing active surveillance rather than point-in-time checks. They:
Monitor for anomalies in agent behaviour or outputs
Track performance metrics and system health
Identify unusual patterns or deviations from expected behaviour
Calculate and monitor risk scores in real-time
Alert appropriate stakeholders when thresholds are breached
Emergency Shutdown Agents
These agents serve as the system's circuit breakers, with the authority to halt operations when critical issues are detected. They:
Monitor for specific trigger conditions
Execute graceful system shutdowns when necessary
Preserve system state for investigation
Initiate failover to backup systems
Log all shutdown events and their causes
Human-in-the-Loop Validation Agents
These agents manage the interface between automated systems and human experts. They:
Identify decisions requiring human review
Route cases to appropriate human experts
Present relevant information in an accessible format
Capture and incorporate human feedback
Learn from human decisions to improve future routing
Invariably, these specialised agents represent a starting framework rather than a definitive list. They serve as architectural building blocks that organisations can adapt, combine, or extend based on their specific needs and risk profile. Much like how banking institutions customise their control frameworks while maintaining the core principles of the 3LoD, organisations implementing AI systems should view these agent patterns as a foundation for developing their own protective measures and guardrails. The key is to understand the underlying principles – separation of duties, layered validation, real-time monitoring, and human oversight – and then implement them in ways that make sense for your specific use case, regulatory environment, and risk appetite. As AI systems evolve and new risks emerge, this framework can be extended with additional specialized agents or modified to address new challenges while maintaining the core principle of robust, multi-layered control.
Separation of Concerns: Beyond Software Engineering
Furthermore, the principle of separation of concerns in AI agents draws direct parallels to regulatory requirements like Sarbanes-Oxley (SOX) in corporate governance. Just as SOX mandates that no single developer should have complete access to production environments, we should apply the same rigour to AI systems.
This separation manifests in several crucial ways. Access control ensures individual AI agents have limited scope and authority, much like developers are restricted from having complete access to production systems. Dual control mechanisms require multiple specialised agents to validate critical AI decisions, mirroring the requirement for multiple approvers in financial transactions.
Future Implications
The architectural approach of using multiple specialised agents with clear boundaries and responsibilities sets the foundation for several key advantages:
Scalable AI Systems in Regulated Environments
The modularity of multi-agent systems creates an inherently scalable and regulation-ready architecture. When regulatory requirements change, organisations can update individual agents rather than restructuring entire systems, significantly reducing compliance overhead.
Enhanced Trust Through Built-in Oversight
Multi-agent systems establish a natural framework of checks and balances that builds trust through transparency and verification. Each decision made by an agent can be independently verified by others in the system, creating a robust audit trail.
Easier Integration with Existing Systems
Drawing parallels with microservices architecture, multi-agent AI systems offer a superior approach to integration with existing infrastructure. Individual agents can be changed, updated, and validated with minimal disruption to the overall system. Workflow controls can be implemented between agents, making it easier to modify business processes.
Flexible Deployment Models
The componentised nature of multi-agent systems revolutionises deployment flexibility. Organisations can strategically deploy agents across different environments based on security requirements and data sensitivity, with critical agents remaining on-premises whilst moving less sensitive operations to the cloud.
Conclusion
The parallel between banking's 3LoD model and multi-agent AI systems isn't just theoretical—it provides a practical framework for building responsible AI systems. As AI continues to evolve, these principles of segregation, oversight, and control will become increasingly important, particularly in regulated industries where trust and accountability are paramount.
In my view, the future of AI governance lies not in reinventing the wheel, but in adapting and enhancing the tried-and-tested control frameworks that have served other regulated industries well. The question isn't whether we need these controls – it's how we can implement them most effectively in the age of AI.
