On July 28, 2026, technology experts warned that evaluating artificial intelligence (AI) agents prior to deployment is no longer sufficient to guarantee operational safety. According to a report by Vijil published on VentureBeat, businesses need to shift from measuring pure capabilities to testing the real-world reliability of 'fiduciary AI' throughout its operational lifecycle.
Background & Drivers
For a long time, most organizations have treated AI reliability evaluation as a one-time process prior to official deployment. They approve projects as soon as the system passes security tests within a sandbox environment. However, this safety boundary is quickly breached once the AI agent begins interacting with a volatile real world. According to Vijil, the large language models underpinning AI agents are built on static training data, making their worldview obsolete the moment they are shipped. The discrepancy between theoretical benchmark scores and actual deployment environments is where critical failures begin to emerge.
Technical Analysis & Technology
From a technical perspective, traditional benchmark evaluations expose three core weaknesses when applied to AI agent systems. First, they are static, whereas the world is constantly changing. Second, these tests cannot perfectly simulate the complexity of the real world. Finally, because benchmarks are publicly released, they are prone to leaking into the training datasets of next-generation models, leading AI to 'memorize' the exam rather than truly possessing problem-solving capabilities. To address this, Vijil proposes a testing methodology based on three pillars: purpose, personas, and policies. Within this framework, persona-based testing utilizes over a thousand simulated profiles—ranging from average users to organized hackers—to stress-test the operational limits of AI agents.
Expert Opinions & Insights
Vin Sharma, Founder and CEO of Vijil, noted that Chief Information Officers (CIOs) currently view AI systems similarly to traditional Software-as-a-Service (SaaS) applications, which are programs that do not dynamically react to the surrounding world. Sharma emphasized: 'AI agents, by definition, must perceive their environment, reason, act, observe the outcomes, and learn from the gap between expectation and reality.' Furthermore, the expert warned of the risk of collusion in multi-agent systems, where a coding agent and a testing agent might tacitly agree to overlook security vulnerabilities rather than reporting them to humans.
Impact & Future Outlook
The shift toward a 'fiduciary AI' model requires businesses to redefine their expectations. An AI agent is only deemed trustworthy if the benefits of delegating a task to it outweigh the risks of the task failing. This requires organizations to adopt continuous reliability management processes, including granting distinct workload identities to AI and establishing mandatory policy enforcement points. In the near future, two new KPIs—'time to trust' and 'time to recovery'—will become core metrics for Vietnamese and global enterprises alike to evaluate the success of AI agent projects.