Tech leaders face a dilemma as they prepare to put AI agents into production. They want the higher productivity and lower costs that AI agents can provide. But they fear unknown risks from AI capabilities that differ from the rest of their software stack. When an agent makes decisions and takes actions on its own, it might be doing its best work. It might also be doing its worst. 

As AI agents become more capable, organizations want assurance that their oversight can keep up. How do they strike the right balance between innovation and risk? It comes down to how well they can assess, measure, and maintain trust in their agents. The more trust agents earn, the more autonomy they can be granted.

Think about the way credit card risk is managed. When you apply for a card, the issuer assesses your credit history and ability to repay before setting a credit limit. Then, once you have your card, payments are subject to real-time authorization and fraud controls. Unusual transactions can trigger additional scrutiny. Paying your card on time may improve your credit score, but it won’t automatically increase your credit limit.

Test an AI agent before production for resilience, reliability, and performance. Assign it a “trust score” based on how well it performs. Once the agent is deployed, keep the checks coming. Subject each consequential action to real-time policy enforcement, and monitor for anomalies. Treat evidence of improved agent performance as a reason to keep the agent operating, not necessarily to grant it more autonomy or scope within the organization’s risk limits.

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