When AI agents act on your behalf, who manages the risk?

<p><b>How to build in governance, transparency and trust before agentic AI goes live.</b></p>

<p>Consider this scenario: A network of AI agents, operating within your enterprise, processes a customer transaction autonomously. The agents interpret the request, apply business rules, make a judgment call and execute, all without human involvement.</p> <p>Now, imagine something about that decision going wrong, enough to trigger a regulatory inquiry. Where is the audit trail? Which agent took which action? What evidence can you use to explain why the decision was made?</p> <p>These are the types of governance questions businesses need to ask before their agentic systems scale. However, most governance frameworks were not built for the new realities introduced by agentic AI.</p> <h4>Why a new oversight architecture is needed</h4> <p>Traditional compliance and risk management operates on the assumption that decisions, made by humans, can be audited, explained and attributed to specific individuals. Policies, controls and escalation paths are all designed around this assumption.</p> <p>However, when an autonomous system makes thousands of decisions per hour, each one informed by a complex web of data, learned patterns and probabilistic reasoning, the old governance toolkit is inadequate. A model cannot be held accountable the same way an employee is, and you cannot apply static rules to a system that, by design, adapts and evolves.</p> <p>With agentic AI, governance itself needs to be reimagined not as a layer sitting outside the system but as an architectural decision that shapes how the entire system is designed. This requires a vocabulary and a framework that most organizations have not yet developed.</p> <h4>What governance looks like with agentic AI</h4> <p>There are two key principles of governance with agentic AI: It’s built in from the beginning, and it plays out as the system is operating.&nbsp;</p> <p>It is also entirely dependent on a constellation of agents working together: vertical agents, horizontal agents and orchestrator agents. While a vertical agent might, for instance, underwrite a loan or process an insurance claim, a horizontal agent is a task-oriented generalist that provides a common, reusable service that can be called upon by multiple vertical agents.&nbsp;</p> <p>That’s where governance comes in. A compliance checking agent, for example, would ensure adherence to regulatory requirements across multiple business processes and domains. A risk analysis agent would evaluate various types of risks, providing a specialized assessment to any vertical agent that requires it.&nbsp;</p> <p>Meanwhile, a master orchestrator agent coordinates the activities of the vertical and horizontal agents. Rather than operating in isolation, the agents work as a coordinated network, with the orchestrator ensuring that the right agent handles the right task at the right time.&nbsp;</p> <p>Here’s how this would strengthen governance and minimize risk in a wealth management operation. A portfolio management agent—a vertical agent—receives market data, client objectives and portfolio guidelines and develops a rebalancing strategy based on current allocations, market conditions and client goals.</p> <p>It then calls on a series of horizontal agents to proceed:</p> <ul> <li><b>A document processing agent extracts data from account statements, trade confirmations and other financial documents.</b> This agent uses computer vision and natural language processing to handle documents in various formats, returning structured data.<br> <br> </li> <li><b>A risk analysis agent provides current portfolio composition and proposed changes.</b> This agent performs sophisticated risk calculations such as value at risk, stress testing and correlation analysis, and returns risk metrics and recommendations.<br> <br> </li> <li><b>A compliance checking agent ensures that proposed portfolio changes comply with regulatory requirements,</b> such as concentration limits, as well as client-specific constraints, such as ESG preferences or restricted securities.<br> <br> </li> <li><b>A market data agent accesses real-time and historical market data</b>, pricing information and research from multiple data providers.</li> </ul> <p>Based on these inputs, the portfolio management agent makes portfolio adjustment decisions. It then calls the trade execution agent to execute trades through appropriate channels, and the content generation agent to draft client communications, explaining the changes and rationale.</p> <p>If the portfolio management agent encounters a situation beyond its capabilities, it escalates to a human wealth advisor, providing comprehensive context to facilitate that person’s decision-making.</p> <p>Crucially, the dynamic flow is based on AI confidence thresholds. This ensures the system is always operating at its optimal balance of speed and safety, and that this balance evolves as the system matures.</p> <p>Further, every decision is traceable with reasoning and confidence scores. This is not just a nice-to-have feature; it is a requirement for SOX compliance, regulatory audits and building trust that is essential for progressive autonomy. Risk and compliance are supported by a complete audit trail that is regulatory-ready.</p> <h4>Key agentic AI governance design principles&nbsp;</h4> <p>Businesses should follow four key principles of effective agent network design to ensure governance, explainability and transparency while minimizing risk:</p> <ul> <li><b>Clear interfaces and contracts:</b> Each agent should have a well-defined interface, specifying what inputs it requires, what outputs it provides, what assumptions it makes and what guarantees it offers.<br> <br> </li> <li><b>Error handling and recovery:</b> Agent networks need robust error handling, retrying using alternative agents, escalating to humans or proceeding with degraded capabilities.<br> <br> </li> <li><b>Monitoring and observability:</b> Visibility into which agents are being called, how long operations take, what errors occur and what decisions are made is crucial for troubleshooting, optimization and governance.<br> <br> </li> <li><b>Security and access control:</b> Not all agents should have access to all data or capabilities. Appropriate security and access controls ensure agents only access what they need and are authorized to use.</li> </ul> <h4>The future of risk management with agentic AI</h4> <p>As agentic AI moves from experiment to enterprise infrastructure, governance must be woven into the architecture from day one. Organizations that treat transparency, auditability and risk as core design requirements will be better positioned to scale these systems with confidence.&nbsp;</p> <p>Getting this right now, before these systems reach full scale, is what separates organizations that earn trust from those that lose it.</p> <p><i>For more on this topic, see our&nbsp;</i><a href="https://www.cognizant.com/us/en/industries/banking-technology-solutions/bts-business-process-services#spy-ai-velocity-gap" target="_blank"><b><i>three-part series</i></b></a><i>&nbsp;on &quot;Confronting the AI velocity gap: A new architecture for AI operations.&quot;</i></p>