Trustworthy AI starts with surviving production failures
Evaluating AI agents in production tends to focus only on positive results. Did the agent complete the task? Was the output accurate? Did the demo go well?
The answers to those questions matter, but they miss the case that determines whether an enterprise can actually trust agents with real work: what happens in the 30% of instances where something goes wrong?
Co-founder and CTO of Diagrid.
In financial services, for example, an autonomous agent that mishandles a money-movement workflow doesn't turn into an innocuous support ticket. It creates legal liability that can extend across an entire business.
In healthcare, unchecked data access calls can compromise patient safety and lead to HIPAA violations. Highly regulated industries can’t treat failure as a minor inconvenience. And when the stakes are categorically higher than in most other sectors, it changes what "production-ready" means.
The problem? Most popular agent frameworks were built by teams focused...
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