Alok Shrivastwa

Agentic AI is Easy. Running It in Production is Not.

Everyone is building agents. Almost no one is running them in production. Eighteen months ago, one of our agents changed a VLAN configuration on a network device during a controlled proof of concept. It put an access point on the wrong VLAN, isolating every client connected to it. The agent completed the task successfully. No error was thrown. No alert fired. From the system's perspective—everything was fine. That incident taught us more about production-grade agentic AI than any benchmark or architecture paper. It revealed four failure zones that most enterprise AI teams have not yet engineered for: Silent Failure, Black Box Decisions, Permission Explosion, and Runaway Execution. This session moves beyond agent architecture theory to the operational discipline required to run autonomous systems inside mission-critical enterprise environments—the kind where SLAs are real, permissions matter, and a wrong decision has consequences. We will walk through the 4-Layer Enterprise Agent Stack, a framework built on the principle that "The user is all"—ensuring agent permissions never exceed what the human behind the request is authorized to do. The future of enterprise AI will not be decided by model intelligence. It will be decided by operational discipline.