Agentic AI Governance: A Strategic Imperative for U.S. Competitiveness

The rise of agentic artificial intelligence (AI)—systems that can operate autonomously with minimal human oversight—presents both opportunities and challenges for national security and economic competitiveness, according to experts at the Special Competitive Studies Project (SCSP), a nonprofit and nonpartisan initiative focused on strengthening America's long-term AI competitiveness.

Unlike current AI systems that generate responses based on prompts, agentic AI can independently set goals, create plans, and execute multi-step tasks. As SCSP president Ylli Bajraktari noted in a recent newsletter, “AI is beginning to help build better AI,” potentially creating a self-accelerating loop where AI capability improvement compounds rapidly, far outpacing current projections.

This acceleration has significant implications for global security. Bajraktari warned that “an agent that can navigate complex bureaucratic systems, identify exploitable vulnerabilities, and act without leaving a clear attribution trail represents a qualitative expansion of adversarial capability.” The United States must recognize that adversaries will deploy agentic AI systems in areas where governance is weakest, using them for coercion, espionage, and influence.

Effective governance of agentic AI, however, does not focus solely on the AI model itself. SCSP experts explain that the key lies in the “scaffolding” built around the model. This scaffolding includes connectors to bridge the model to real-world infrastructure such as email, booking systems, and financial platforms; memory that allows the system to learn and adapt over time; planning capabilities that break large objectives into smaller tasks and navigate obstacles; permission structures that define system access; and guardrails that determine what the system will refuse to do, such as spending limits or human sign-offs.

Accountability remains a major challenge. Current governance frameworks fall short in three ways: responsibility is untraceable when AI acts autonomously; existing frameworks only assess whether a task was completed, not whether it was performed safely or caused harm; and agentic AI builds personal profiles that may include sensitive data by accumulating patterns of behavior, preferences, and inferences.

Despite these challenges, SCSP emphasizes that agentic AI is not a technology to be feared or deferred. Institutions that prioritize understanding, shaping, and governing agentic AI will not only determine their own competitive position but also shape the global environment in which it operates. For more insights, visit scsp.ai.