Fiduciary AI: Agents need to prove trustworthiness, not just ability
In dynamic environments where users, data, workflows and attack techniques change continuously after deployment, AI agent trust has become a runtime problem. Most organizations still treat trust as a pre-deployment exercise, declaring an agent production-ready and launching it after it passes sandbox evaluations and performs successfully in security tests. Unfortunately, that trustworthiness breaks down the moment an agent begins interacting with the real world.
"The core of the problem is that CIOs and business owners think about AI systems the way they think about SaaS or mobile applications, which do not respond dynamically to the world around them," says Vin Sharma, Founder and CEO of Vijil. "Agents, by the textbook definition, are meant to perceive their environment, reason, act, observe the consequences, and learn from the gap between expectation and reality. The problem is that the models underneath them are built from static training data, and that...
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