To unlock agentic AI’s promise for government, America must build reliability

Imagine an AI logistics agent for the Department of Defense that could, in seconds, reroute a fuel convoy around a contested chokepoint, file the necessary clearances, and notify the affected personnel. Imagine a Veterans Affairs AI assistant that walks a Gold Star family through benefits applications without leaving them on hold for 90 minutes. Imagine a Treasury AI system that catches a billion-dollar daycare scheme before the money leaves the account. 

The promise of agentic AI is massive productivity: faster services for citizens, smarter logistics for warfighters, lower costs for taxpayers, and decision advantage over adversaries who are racing to deploy these same capabilities. 

The problem is we cannot currently deploy agentic AI at scale, because today’s systems are not yet reliable enough. 

I spent years at the Pentagon’s Joint Artificial Intelligence Center, helping to build the foundations of the Defense Department’s AI enterprise. I watched promising prototypes collide with operational reality. Models can perform brilliantly in demonstrations and then fail in unexpected ways in the real world. In operational contexts, model hallucinations or an adversary’s prompt injection attack could be catastrophic. Warfighters need to be able to trust that an AI is not optimizing for the wrong objective. We need to ensure that the AI system behaves the same way in testing and production. 

Unlike normal software, AI systems are not programmed by a human. They write their own code and decide how they interpret operator instructions. When an AI system goes........

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