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

Opinion>Opinions - Technology The views expressed by contributors are their own and not the view of The Hill To unlock agentic AI’s promise for government, America must build reliability Comments: by Mark Beall, opinion contributor - 07/05/26 11:00 AM ET Comments: Link copied by Mark Beall, opinion contributor - 07/05/26 11:00 AM ET Comments: Link copied

Adobe Images

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 wrong, there’s no stack trace you can examine. Guiding these AI systems reliably is the central engineering reality of frontier AI in 2026. It is unlike other software challenges the government has faced. And the higher the stakes of the deployment, and the more capable the AI system, the less acceptable these mistakes are.

A chatbot that gets a fact wrong is an inconvenience. A superintelligent agentic system with access to classified military data, financial controls or critical infrastructure is a national security incident waiting to happen.

I have spent the last two years working with lawmakers on what I believe is the central AI policy challenge of this Congress: building the science, the testing frameworks and standards, and the institutions that will unlock trustworthy agentic artificial intelligence that our nation can count on when the stakes are highest.

A bipartisan coalition of national security and AI experts has urged Congress to fund the National AI Reliability and Control Initiative (NAIRCI) at $2 billion in the fiscal 2027 National Defense Authorization Act. NAIRCI provides targeted research funding for the hardest unsolved problems in AI: how to make systems behave predictably, how to verify they are doing what we asked, how to keep them aligned with human intent as their capabilities expand, and how to keep meaningful human stewardship over these systems. This is the science that will accelerate American AI companies, defense primes and businesses in their missions to deliver products that our government can actually field.

Some will argue that any guardrails will slow America down in our competition with China. The opposite is true. Technical AI safety is loadbearing for American AI dominance.

The People’s Republic is racing to deploy agentic AI inside its own state apparatus, and Chinese researchers are encountering the same reliability problems we are. The country that solves them first will not merely have safer AI; it will have AI that actually works at scale in the systems that matter most. Reliability is not a tax on innovation. It is the precondition for the kind of innovation that ends in real deployment rather than fancy marketing, failed pilots and stalled procurements.

Finally, I would note that we are in two races at once. The first is the commercial and military AI competition with the People’s Republic of China, which we must win. Market share is the goal. The second is the longer race, against time itself, to ensure that increasingly powerful systems remain under human stewardship as their capabilities grow. Both races are won the same way: by investing in the science of reliability, by building the evaluation infrastructure, and by creating the policy frameworks that let American companies and American agencies accelerate the deployment of trustworthy AI capabilities.

Agentic AI will transform the government. The question is whether that transformation will be one we shape deliberately, with the safeguards a free society requires, or one that happens to us in the wake of a cascading failure no one anticipated.

Congress has a clear mandate. The American public, in poll after poll, has made clear that they want both the benefits of AI and assurance that the technology is being deployed responsibly. The U.S. technical community is the best in the world and stands by to assist. What remains is the will to act.

Mark Beall is president of the AI Policy Network. He served as the inaugural director of AI Strategy and Policy at the Pentagon’s Joint Artificial Intelligence Center and has testified before Congress on AI and national security.

Add as preferred source on Google Tags agentic AI AI Artificial intelligence China reliable

Comments: Link copied