"We were building autonomous AI agents without the basic trust infrastructure that the internet established 40 years ago with DNS. As a PhD researcher and IEEE Senior Member, and I've spent the past year building what I call "DNS for AI agents" - a trust layer that finally gives autonomous AI the security foundation it desperately needs. What started as a research project to solve authentication problems in multi-tenant ML environments"
"The transformation from traditional machine learning to agentic AI represents one of the most significant shifts in enterprise technology. While traditional ML pipelines require human oversight at every step - data validation, model training, deployment and monitoring - modern agentic AI systems enable autonomous orchestration of complex workflows involving multiple specialized agents. But with this autonomy comes a critical question: How do we trust these agents?"
A compromised agent in a 50-agent multi-tenant ML operations system exploited lack of identity verification, impersonated a deployment service, and caused corrupted models and systemic collapse within minutes. Agentic AI enables autonomous orchestration of complex workflows across specialized agents, removing routine human oversight but introducing authentication and trust challenges. A DNS-like trust layer for AI agents can provide identity, authentication, and secure endpoint discovery to prevent impersonation and cascading failures. A production trust system evolved from research to solve multi-tenant authentication, enabling safer large-scale deployment of autonomous agents by establishing secure credentials and verified endpoints.
Read at InfoWorld
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